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How Do I Prioritize Features When Building a Web3 MVP?

Building a Web3 minimum viable product presents unique challenges that traditional software development doesn't face. You're not just creating an application—you're architecting an ecosystem where technology, economics, and community coordination intersect.
Talha Ahmad
5 min
MIN

Building a Web3 minimum viable product presents unique challenges that traditional software development doesn't face. You're not just creating an application—you're architecting an ecosystem where technology, economics, and community coordination intersect. With blockchain's immutable nature and the complexity of decentralized systems, knowing which features to prioritize can mean the difference between launching a successful project and wasting months building the wrong things.

In 2025, Web3 MVP development requires more than technical skills. It demands speed, clarity, and the ability to learn from real users fast. This comprehensive guide will walk you through proven prioritization strategies specifically designed for Web3 projects, helping you make informed decisions that maximize your chances of achieving product-market fit.

Understanding the Web3 MVP Challenge

A minimum viable product is a simplified version of your product that includes only the core features necessary to meet the needs of early adopters. In Web3, an MVP allows startups to explore decentralized applications and blockchain functionalities without committing extensive resources upfront.

The stakes are uniquely high in Web3. Smart contract immutability means you can't easily patch bugs after deployment. Scope creep poses existential dangers since adding features often requires complete system overhauls rather than simple updates. Security vulnerabilities aren't just reputational problems—they're existential threats that can drain millions in seconds.

Before diving into feature prioritization, understand what makes Web3 MVP development different from traditional software. You're simultaneously solving technical problems, fostering communities, creating economic systems through tokenomics, and building foundational infrastructure for a decentralized internet.

The Foundation: Identifying Core Value

Before applying any prioritization framework, you must answer one fundamental question: What specific problem does your Web3 project solve, and for whom?

Conduct thorough market research to identify your target audience, assess viability, gauge problem-solving potential, and devise strategies to align your product with customer requirements. In crypto, understanding the optimal approach for delivering value and effectively communicating benefits is paramount.

Define precise, unambiguous metrics that will quantitatively evaluate the efficacy of your launch. For Web3 applications, this might include the number of wallet connections, transaction volume, total value locked, or active community members.

Successful projects like Uniswap demonstrate this principle perfectly. Starting in 2018 with under $50,000, no CEO, and no marketing blitz—just a smart contract on Ethereum and a vision for trustless token swaps—Uniswap focused exclusively on solving one problem: enabling decentralized token exchanges. By 2025, it has surpassed $3 trillion in total trading volume by maintaining laser focus on core functionality before expanding.

The MoSCoW Prioritization Framework for Web3

The MoSCoW method creates a hierarchy for your feature requests based on their importance. Developed by Dai Clegg while working at Oracle in 1994, this framework divides features into four unambiguous categories, particularly useful in conjunction with fixed timeframes.

Must Have (M): These are non-negotiable requirements to launch your product. An easy way to identify Must Have features is asking: "What happens if this requirement isn't met?" If the answer is "cancel the project," it's a Must Have.

For a Web3 DEX, Must Haves might include wallet connection, token swapping functionality, basic liquidity pool creation, and essential smart contract security audits. These are the features that define your product's core value proposition.

Should Have (S): These features are important but not immediately critical. They significantly enhance user experience and can be included in the first release if resources permit, but the product can launch without them.

For our DEX example, Should Haves might include advanced trading features like limit orders, portfolio tracking, or multi-chain support for additional networks beyond your primary blockchain.

Could Have (C): These are desirable enhancements that would improve user satisfaction but aren't necessary for launch. Often called "nice-to-haves," they're the first to be removed if timelines or resources become constrained.

Could Have features might include advanced analytics dashboards, social features, gamification elements, or integrations with other DeFi protocols.

Won't Have (W): These features are explicitly excluded from the current iteration. They may be reconsidered for future releases, but are intentionally deferred to maintain focus and prevent scope creep.

The MoSCoW framework ensures you build a genuine minimum viable product by prioritizing Must Have features while creating contingency within requirements. This approach is particularly effective in Web3 where resource constraints and technical complexity demand ruthless prioritization.

Value vs. Complexity Analysis

Another powerful prioritization technique for Web3 MVPs involves plotting features on a two-dimensional matrix: Value against Complexity (or Effort).

Value represents the benefit your customers and business receive from a feature. Does it alleviate customer pain points? Will it drive user adoption? Does it strengthen your competitive position? In Web3, value might also include community building potential, network effects, or tokenomic alignment.

Complexity encompasses what it takes for your organization to deliver the feature: development time, required expertise, infrastructure costs, security audit requirements, gas optimization needs, and ongoing maintenance burden.

Plot each potential feature on a 2x2 grid:

High Value, Low Complexity: These are your quick wins. Prioritize these features first—they deliver maximum impact with minimal investment. Examples might include integrating a widely-used wallet provider or implementing standard ERC-20 token support.

High Value, High Complexity: These are strategic initiatives that define your competitive advantage. Plan these carefully, break them into smaller deliverables, and build them after quick wins demonstrate traction. Examples might include novel AMM algorithms or cross-chain bridging infrastructure.

Low Value, Low Complexity: These are fill-in tasks suitable when waiting for dependencies or during low-activity periods. Don't let these distract from higher priorities.

Low Value, High Complexity: Avoid these entirely—they're resource drains that won't move the needle on user adoption or business success.

Community-Driven Prioritization in Web3

Unlike traditional software, Web3 projects succeed by building active communities from day one. Your community becomes both your testing ground and marketing engine, making community-driven prioritization essential.

Create dedicated Discord channels for testing feedback. Run community polls to validate feature prioritization decisions. Use governance forums to gather input on economic parameter changes and roadmap decisions.

This community involvement serves multiple purposes beyond feature validation. It provides extensive testing coverage no internal team could match, builds community investment in your project's success, and creates feedback loops that traditional QA processes miss.

However, maintain roadmap discipline. Active communities generate constant feature requests and suggestions. While input is valuable, allowing every suggestion to influence your roadmap leads to scope creep and delayed launches. Communicate priorities and reasoning regularly, and use governance forums to discuss potential changes transparently.

Security and Audit Prioritization

In Web3, security isn't just another feature—it's the foundation everything else builds upon. Hacks are existential threats, not merely reputational problems.

Design smart contracts as modular systems from the beginning. Use proxy patterns that allow upgrades while maintaining security. Plan clear upgrade paths for adding functionality without compromising existing security guarantees.

Prioritize comprehensive security audits for all smart contracts before mainnet deployment. Budget 15-25% of development resources for security reviews, formal verification where appropriate, and bug bounty programs. This isn't optional—it's the price of admission in Web3.

Progressive Decentralization Strategy

Don't try to build fully autonomous organizations from the start. Launch with appropriate centralized control and create clear roadmaps for progressive decentralization.

Your initial MVP should prioritize functionality and security over complete decentralization. Many successful Web3 projects launched with admin keys and centralized control, then gradually transferred governance to the community as the system matured and edge cases were addressed.

Premature decentralization often leads to governance paralysis, inability to respond to emergencies, and security vulnerabilities. Plan your decentralization roadmap as carefully as your feature roadmap.

Real-World Data: Learning from Token Metrics

Token Metrics exemplifies smart feature prioritization in Web3 analytics platforms. Rather than trying to build every possible crypto analysis tool simultaneously, Token Metrics focused on core value propositions first: AI-powered token ratings, smart contract audits, and comprehensive market analytics.

As the premier crypto trading and analytics platform, Token Metrics demonstrates how strategic feature prioritization creates competitive advantages. The platform started with essential Must Have features—reliable data feeds, AI rating algorithms, and intuitive interfaces—before expanding to Should Have capabilities like automated trading indices and cross-chain analytics.

In March 2025, Token Metrics launched integrated on-chain trading, transforming from an analytics platform into an end-to-end solution. This strategic expansion came only after establishing market leadership in analytics, demonstrating smart sequencing of high-value features.

Token Metrics' approach illustrates several key prioritization principles for Web3 builders:

Start with data quality and reliability as non-negotiable Must Haves. Without accurate, real-time blockchain data, no analytics features matter.

Build AI-powered insights as differentiators once core data infrastructure is solid. Token Metrics' Trader Grades (0-100) and Investor Grades provide unique value that competing platforms lack.

Layer on convenience features like integrated trading only after achieving product-market fit with core analytics. This sequencing prevents premature complexity while building toward a comprehensive platform.

Continuously gather user feedback to validate feature priorities. Token Metrics evolved from pure analytics to trading integration based on customer requests for a direct path from research to execution.

For Web3 builders, Token Metrics' journey offers valuable lessons. Prioritize features that deliver immediate, measurable value to early adopters. Build trust through reliability and security before expanding functionality. Listen to your community but maintain roadmap discipline. Sequence features strategically, ensuring each addition builds on proven foundations.

AI-Assisted Prioritization in 2025

Web3 development in 2025 increasingly leverages AI tools for smarter feature prioritization. AI-driven user analytics can predict which features will drive adoption, while AI-generated feature scoring helps teams make data-driven decisions faster.

Use AI to analyze competitor offerings, identify gaps in the market, and predict user behavior patterns. These tools shave weeks off development timelines and reduce human bias in prioritization decisions.

However, AI should inform human judgment, not replace it. Your understanding of community needs, technical constraints, and strategic vision remains irreplaceable.

Practical Implementation Timeline

A realistic Web3 MVP development timeline might look like:

Week 1-2: Problem interviews and hypothesis development. Validate that your identified problem is real and your proposed solution resonates with potential users.

Week 3-4: No-code or low-code prototype to validate user flow. Collect 20-50 user responses before writing a single line of smart contract code.

Week 5-8: Build lean MVP prototype focusing exclusively on Must Have features. Instrument analytics and set clear KPIs for measuring success.

Week 9-12: Iterate based on user data. Deploy to testnet and expand testing to broader community. Begin security audit processes for smart contracts.

Week 13-16: Security hardening, audit remediation, and mainnet deployment preparation.

This aggressive timeline requires ruthless feature prioritization. Every feature not classified as Must Have gets deferred—no exceptions.

Common Prioritization Mistakes to Avoid

Overloading Must Haves: Teams often classify too many features as Must Haves, leading to extended timelines and resource depletion. Be ruthless—if launching without it won't invalidate your core value proposition, it's not truly a Must Have.

Ignoring technical debt: Choosing quick implementations that create maintenance nightmares undermines long-term success. Balance speed with code quality, especially for immutable smart contracts.

Feature parity battles: Trying to match every competitor feature leads to unfocused products that don't excel at anything. Identify your unique value proposition and double down.

Neglecting tokenomics: In Web3, your economic model isn't just a fundraising mechanism—it's a core product feature requiring the same design rigor as smart contracts. Poor tokenomics kills promising projects.

Building in isolation: Launching without community building leads to products nobody uses. Start community engagement on day one, before you have a product to show.

Conclusion

Prioritizing features for a Web3 MVP requires balancing technical constraints, community needs, security requirements, and business objectives. The most successful projects use structured frameworks like MoSCoW alongside value-complexity analysis, while maintaining flexibility to adapt as they learn from real users.

Platforms like Token Metrics demonstrate how strategic feature prioritization creates sustainable competitive advantages. By focusing relentlessly on core value propositions—accurate data, AI-powered insights, and user-friendly interfaces—before expanding to integrated trading, Token Metrics built the premier crypto analytics platform through disciplined execution.

Your Web3 MVP journey begins with identifying the single most important problem you're solving and the minimum feature set required to solve it. Apply proven prioritization frameworks, engage your community early, prioritize security above all else, and plan for progressive decentralization. Most importantly, launch quickly to start the learning cycle—market feedback is the only true validation of your priorities.

The future belongs to Web3 builders who recognize they're not just creating products, but architecting ecosystems. Master feature prioritization, and you'll dramatically increase your odds of building something users actually want in the decentralized internet of tomorrow.

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Token Metrics API

Quantmetrics API: Measure Risk & Reward in One Call

Sam Monac
5 min
MIN

Most traders see price—quants see probabilities. The Quantmetrics API turns raw performance into risk-adjusted stats like Sharpe, Sortino, volatility, drawdown, and CAGR so you can compare tokens objectively and build smarter bots and dashboards. In minutes, you’ll query /v2/quantmetrics, render a clear performance snapshot, and ship a feature that customers trust. Start by grabbing your key at Get API Key, Run Hello-TM to verify your first call, then Clone a Template to go live fast.

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What You’ll Build in 2 Minutes

  • A minimal script that fetches Quantmetrics for a token via /v2/quantmetrics (e.g., BTC, ETH, SOL).

  • A smoke-test curl you can paste into your terminal.

  • A UI pattern that displays Sharpe, Sortino, volatility, max drawdown, CAGR, and lookback window.

  • Endpoints to add next: /v2/tm-grade (one-score signal), /v2/trading-signals / /v2/hourly-trading-signals (timing), /v2/resistance-support (risk placement), /v2/price-prediction (scenario planning).

Why This Matters

Risk-adjusted truth beats hype. Price alone hides tail risk and whipsaws. Quantmetrics compresses edge, risk, and consistency into metrics that travel across assets and timeframes—so you can rank universes, size positions, and communicate performance like a pro.

Built for dev speed. A clean REST schema, predictable latency, and easy auth mean you can plug Sharpe/Sortino into bots, dashboards, and screeners without maintaining your own analytics pipeline. Pair with caching and batching to serve fast pages at scale.

Where to Find 

The Quant Metrics cURL request is located in the top right of the API Reference, allowing you to easily integrate it with your application.

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👉 Keep momentum: Get API Key • Run Hello-TM • Clone a Template

Live Demo & Templates

  • Risk Snapshot Widget (Dashboard): Show Sharpe, Sortino, volatility, and drawdown per token; color-code by thresholds.

  • Allocator Screener: Rank tokens by Sharpe, filter by drawdown < X%, and surface a top-N list.

  • Bot Sizer: Use Quantmetrics to scale position sizes (e.g., lower risk = larger size), combined with Trading Signals for entries/exits.

Kick off from quickstarts in the docs—fork a dashboard or screener template, plug your key, and deploy in minutes. Validate your environment with Run Hello-TM; when you need more throughput or webhooks, compare API plans.

How It Works (Under the Hood)

Quantmetrics computes risk-adjusted performance over a chosen lookback (e.g., 30d, 90d, 1y). You’ll receive a JSON snapshot with core statistics:

  • Sharpe ratio: excess return per unit of total volatility.

  • Sortino ratio: penalizes downside volatility more than upside.

  • Volatility: standard deviation of returns over the window.

  • Max drawdown: worst peak-to-trough decline.

  • CAGR / performance snapshot: geometric growth rate and best/worst periods.

Call /v2/quantmetrics?symbol=<ASSET>&window=<LOOKBACK> to fetch the current snapshot. For dashboards spanning many tokens, batch symbols and apply short-TTL caching. If you generate alerts (e.g., “Sharpe crossed 1.5”), run a scheduled job and queue notifications to avoid bursty polling.

Production Checklist

  • Rate limits: Understand your tier caps; add client-side throttling and queues.

  • Retries & backoff: Exponential backoff with jitter; treat 429/5xx as transient.

  • Idempotency: Prevent duplicate downstream actions on retried jobs.

  • Caching: Memory/Redis/KV with short TTLs; pre-warm popular symbols and windows.

  • Batching: Fetch multiple symbols per cycle; parallelize carefully within limits.

  • Error catalog: Map 4xx/5xx to clear remediation; log request IDs for tracing.

  • Observability: Track p95/p99 latency and error rates; alert on drift.

  • Security: Store API keys in secrets managers; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Gate entries by Sharpe ≥ threshold and drawdown ≤ limit, then trigger with /v2/trading-signals; size by inverse volatility.

  • Dashboard Builder (Product): Add a Quantmetrics panel to token pages; allow switching lookbacks (30d/90d/1y) and export CSV.

  • Screener Maker (Lightweight Tools): Top-N by Sortino with filters for volatility and sector; add alert toggles when thresholds cross.

  • Allocator/PM Tools: Blend CAGR, Sharpe, drawdown into a composite score to rank reallocations; show methodology for trust.

  • Research/Reporting: Weekly digest of tokens with Sharpe ↑, drawdown ↓, and volatility ↓.

Next Steps

  • Get API Key — start free and generate a key in seconds.

  • Run Hello-TM — verify your first successful call.

  • Clone a Template — deploy a screener or dashboard today.

  • Watch the demo: VIDEO_URL_HERE

  • Compare plans: Scale with API plans.

FAQs

1) What does the Quantmetrics API return?
A JSON snapshot of risk-adjusted metrics (e.g., Sharpe, Sortino, volatility, max drawdown, CAGR) for a symbol and lookback window—ideal for ranking, sizing, and dashboards.

2) How fresh are the stats? What about latency/SLOs?
Responses are engineered for predictable latency. For heavy UI usage, add short-TTL caching and batch requests; for alerts, use scheduled jobs or webhooks where available.

3) Can I use Quantmetrics to size positions in a live bot?
Yes—many quants size inversely to volatility or require Sharpe ≥ X to trade. Always backtest and paper-trade before going live; past results are illustrative, not guarantees.

4) Which lookback window should I choose?
Short windows (30–90d) adapt faster but are noisier; longer windows (6–12m) are steadier but slower to react. Offer users a toggle and cache each window.

5) Do you provide SDKs or examples?
REST is straightforward (JS/Python above). Docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for quant alerts?
Dashboards usually use cached polling. For threshold alerts (e.g., Sharpe crosses 1.0), run scheduled jobs and queue notifications to keep usage smooth and idempotent.

7) Pricing, limits, and enterprise SLAs?
Begin free and scale up. See API plans for rate limits and enterprise SLA options.

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Token Metrics API

Crypto Trading Signals API: Put Bullish/Bearish Calls Right in Your App

Sam Monac
7 min
MIN

Timing makes or breaks every trade. The crypto trading signals API from Token Metrics lets you surface bullish and bearish calls directly in your product—no spreadsheet wrangling, no chart gymnastics. In this guide, you’ll hit the /v2/trading-signals endpoint, display actionable signals on a token (e.g., SOL, BTC, ETH), and ship a conversion-ready feature for bots, dashboards, or Discord. Start by creating a key on Get API Key, then Run Hello-TM and Clone a Template to go live fast.

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What You’ll Build in 2 Minutes

  • A minimal script that fetches Trading Signals via /v2/trading-signals for one symbol (e.g., SOL).

  • A copy-paste curl to smoke-test your key.

  • A UI pattern to render signal, confidence/score, and timestamp in your dashboard or bot.

  • Endpoints to add next: /v2/hourly-trading-signals (intraday updates), /v2/resistance-support (risk placement), /v2/tm-grade (one-score view), /v2/quantmetrics (risk/return context).

Why This Matters

Action over analysis paralysis. Traders don’t need more lines on a chart—they need an opinionated call they can automate. The trading signals API compresses technical momentum and regime reads into Bullish/Bearish events you can rank, alert on, and route into strategies.

Built for dev speed and reliability. A clean schema, predictable performance, and straightforward auth make it easy to wire signals into bots, dashboards, and community tools. Pair with short-TTL caching or webhooks to minimize polling and keep latency low.

Where to Find 

You can find the cURL request for Crypto Trading Signals in the top right corner of the API Reference. Use it to access the latest signals!

👉 Keep momentum: Get API Key • Run Hello-TM • Clone a Template

Live Demo & Templates

  • Trading Bot Starter: Use Bullish/Bearish calls to trigger paper trades; add take-profit/stop rules with Support/Resistance.

  • Dashboard Signal Panel: Show the latest call, confidence, and last-updated time; add a history table for context.

  • Discord/Telegram Alerts: Post signal changes to a channel with a link back to your app.

Fork a quickstart from the docs, plug your key, and deploy. Validate your environment by Running Hello-TM. When you need more throughput or webhooks, compare API plans.

How It Works (Under the Hood)

Trading Signals distill model evidence (e.g., momentum regimes and pattern detections) into Bullish or Bearish calls with metadata such as confidence/score and timestamp. You request /v2/trading-signals?symbol=<ASSET> and render the most recent event, or a small history, in your UI.

For intraday workflows, use /v2/hourly-trading-signals to update positions or alerts more frequently. Dashboards typically use short-TTL caching or batched fetches; headless bots lean on webhooks, queues, or short polling with backoff to avoid spiky API usage.

Production Checklist

  • Rate limits: Know your tier caps; add client-side throttling and queues.

  • Retries/backoff: Exponential backoff with jitter; treat 429/5xx as transient.

  • Idempotency: Guard downstream actions (don’t double-trade on retries).

  • Caching: Memory/Redis/KV with short TTLs for reads; pre-warm popular symbols.

  • Webhooks & jobs: Prefer webhooks or scheduled workers for signal change alerts.

  • Pagination/Bulk: Batch symbols; parallelize with care; respect limits.

  • Error catalog: Map common 4xx/5xx to clear fixes; log request IDs.

  • Observability: Track p95/p99 latency, error rate, and alert delivery success.

  • Security: Keep keys in a secrets manager; rotate regularly.

Use Cases & Patterns

  • Bot Builder (Headless): Route Bullish into candidate entries; confirm with /v2/resistance-support for risk and TM Grade for quality.

  • Dashboard Builder (Product): Add a “Signals” module per token; color-code state and show history for credibility.

  • Screener Maker (Lightweight Tools): Filter lists by Bullish state; sort by confidence/score; add alert toggles.

  • Community/Discord: Post signal changes with links to token pages; throttle to avoid noise.

  • Allocator/PM Tools: Track signal hit rates by sector/timeframe to inform position sizing (paper-trade first).

Next Steps

  • Get API Key — create a key and start free.

  • Run Hello-TM — confirm your first successful call.

  • Clone a Template — deploy a bot, dashboard, or alerting tool today.

  • Watch the demo: VIDEO_URL_HERE

  • Compare plans: Scale usage and unlock higher limits with API plans.

FAQs

1) What does the Trading Signals API return?
A JSON payload with the latest Bullish/Bearish call for a symbol, typically including a confidence/score and generated_at timestamp. You can render the latest call or a recent history for context.

2) Is it real-time? What about latency/SLOs?
Signals are designed for timely, programmatic use with predictable latency. For faster cycles, use /v2/hourly-trading-signals. Add caching and queues/webhooks to reduce round-trips.

3) Can I use the signals in a live trading bot?
Yes—many developers do. A common pattern is: Signals → candidate entry, Support/Resistance → stop/targets, Quantmetrics → risk sizing. Always backtest and paper-trade before going live.

4) How accurate are the signals?
Backtests are illustrative, not guarantees. Treat signals as one input in a broader framework with risk controls. Evaluate hit rates and drawdowns on your universe/timeframe.

5) Do you provide SDKs and examples?
You can integrate via REST using JavaScript and Python snippets above. The docs include quickstarts, Postman collections, and templates—start with Run Hello-TM.

6) Polling vs webhooks for alerts?
Dashboards often use cached polling. For bots/alerts, prefer webhooks or scheduled jobs and keep retries idempotent to avoid duplicate trades or messages.

7) Pricing, limits, and enterprise SLAs?
Begin free and scale as you grow. See API plans for allowances; enterprise SLAs and support are available.

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Token Metrics API

Technology Grade API: Identify Real Innovation and Build Smarter Crypto Apps

Sam Monac
7 min
MIN

Hype is loud, but code is what lasts. The Technology Grade API helps you measure the engineering strength behind a token—scalability, innovation, and real code quality—so you can prioritize serious projects in your bots, dashboards, or research tools. In this guide, you’ll query the /v2/technology-grade endpoint, embed the score in your UI, and ship a feature that turns technical due diligence into a single actionable signal. Start by grabbing your key at Get API Key, Run Hello-TM to validate your first call, then Clone a Template to go live fast.

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What You’ll Build in 2 Minutes

  • A minimal script that fetches Technology Grade for any symbol via /v2/technology-grade.

  • A copy-paste curl to smoke-test your key.

  • A starter UX pattern: display the headline Technology Grade + component breakdown (scalability, innovation, code quality).

  • Endpoints to add next for full context: /v2/fundamental-grade (business quality), /v2/tm-grade (technicals/sentiment/momentum), /v2/trading-signals (timing), /v2/quantmetrics (risk/return).

Why This Matters

Separate hype from substance. Whitepapers and roadmaps are cheap; shipped code, throughput, and upgrade cadence are not. The Technology Grade API rolls engineering reality into a comparable score so you can rank ecosystems, filter listings, and surface projects with staying power.

Faster diligence, clearer decisions. For bot builders, Technology Grade is an upstream filter that keeps low-quality projects out of your universe. For dashboard builders, it adds credibility—users can see why a project ranks well. And for screeners, it’s a one-score signal that’s easy to sort, badge, and alert on with low latency.

Where to Find 

For the Technology Grade information, check the top right of the API Reference. You'll find the cURL request to connect effortlessly.

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👉 Next: Get API Key • Run Hello-TM • Clone a Template

Live Demo & Templates

  • Investor/Due-Diligence Token Page: Show a Technology Grade dial with component bars and a “What improved?” changelog snippet.

  • Screener/Leaderboard: Rank by Technology Grade; add sector and market-cap filters; badge “Rising Tech” week-over-week.

  • Bot Universe Filter: Require a minimum Technology Grade before a token is eligible for strategies; combine with signals for entries/exits.

Kick off from quickstarts in the docs—fork a dashboard or screener and deploy. Validate your environment with Run Hello-TM, then scale usage. When you need higher limits and SLAs, compare API plans.

How It Works (Under the Hood)

Technology Grade synthesizes engineering-centric evidence—such as throughput/scalability, rate of innovation (feature velocity, upgrade cadence), and code quality (maintainability, robustness cues)—into a normalized score and grade (e.g., Strong / Average / Weak). It’s designed to be comparable across projects and stable enough to inform filters, tiers, and badges.

At query time, you request /v2/technology-grade?symbol=<ASSET>. The response includes the headline score and component scores you can display in bars or a radar chart. For dashboards with many assets, use batched calls and short-TTL caching. If you push upgrade/downgrade alerts, queue notifications or use webhooks to avoid bursty polling.

Production Checklist

  • Rate limits: Understand your tier’s caps; add client-side throttling.

  • Retries & backoff: Use exponential backoff with jitter; handle 429/5xx gracefully.

  • Idempotency: Ensure retried fetches don’t double-trigger downstream actions.

  • Caching: Memory/Redis/KV with short TTLs; pre-warm popular symbols; ETag if available.

  • Webhooks & jobs: Prefer queued jobs or webhooks for grade-change alerts.

  • Pagination/Bulk: Batch symbols; parallelize with care; respect limits.

  • Error catalog: Map common 4xx/5xx to remediation steps; log request IDs.

  • Observability: Track p95/p99 latency and error rates per endpoint; alert on drift.

  • Security: Keep API keys in secrets managers; rotate and scope keys.

Use Cases & Patterns

  • Bot Builder (Headless): Apply a Technology Grade threshold to define your tradable universe; then confirm timing with /v2/trading-signals and place risk with /v2/resistance-support.

  • Dashboard Builder (Product): Add a “Tech” tab on token pages with the headline grade, components, and a short narrative for users (“What’s driving this score?”).

  • Screener Maker (Lightweight Tools): Ship a Top-N by Technology Grade leaderboard; add badges for “Rising Tech” based on week-over-week deltas.

  • Listing/Research Teams: Gate listings or research coverage using Technology Grade plus Fundamental Grade for balanced quality screens.

  • Enterprise Due Diligence: Export grades nightly to internal systems; alert on downgrades crossing critical thresholds.

Next Steps

  • Get API Key — create a key and start free.

  • Run Hello-TM — confirm your first successful call.

  • Clone a Template — deploy a screener or token page today.

  • Watch the demo: VIDEO_URL_HERE

  • Compare plans: Scale usage and unlock higher limits with API plans.

FAQs

1) What does the Technology Grade API return?
A JSON payload with an overall score/grade plus component scores (e.g., scalability, innovation, code quality) and timestamps. Use the overall score for ranking and components for explanation.

2) Is this real-time, and what about latency/SLOs?
The endpoint is engineered for predictable latency suitable for dashboards and filters. For frequent refresh or alerts, combine short-TTL caching with queued jobs or webhooks to minimize round-trips.

3) How should I combine Technology Grade with other signals?
A common pattern: Technology Grade (engineering quality) + Fundamental Grade (business quality) + TM Grade (technicals/sentiment) + Trading Signals (timing) + Support/Resistance (risk placement).

4) How “accurate” is Technology Grade?
It’s an opinionated synthesis of engineering evidence, not financial advice. Use it as part of a diversified framework; validate with your own backtests and risk controls.

5) Do you provide SDKs or examples?
You can integrate via REST (JS/Python examples above). The docs include quickstarts, Postman collections, and templates—start by Run Hello-TM.

6) Polling vs webhooks for grade changes?
For UI pages, cached polling is fine. For alerts (upgrades/downgrades), prefer webhooks or scheduled jobs to avoid spiky traffic and rate-limit issues.

7) Pricing, limits, and enterprise SLAs?
Begin free and scale up as needed. See API plans for allowances; enterprise SLAs and support are available.

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