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picjhoe-dev

binance-market-mcp-server

by picjhoe-dev
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TypeScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one retrieves historical OHLC candles, the other fetches current price and 24h stats. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow the identical binance_ verb_noun pattern (get_historical_ohlc and get_current_price), making the naming predictable and clear.

    Tool Count4/5

    At 2 tools, the server is minimal but well-scoped for the stated market-data purpose. Each tool covers a fundamental need (current quote vs. historical series), so the count is appropriate though slightly thin.

    Completeness4/5

    The two tools cover the core market-data needs: live price snapshot and historical candle data. Minor gaps exist (e.g., no order book, no multi-symbol endpoint), but the surface is coherent and sufficient for many use cases.

  • Average 4.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds meaningful behavioral context: it is a 'live snapshot', will 'NOT return historical data', and error handling returns 'Error: ...' text when the symbol is invalid or Binance is rate-limiting. This goes beyond the annotations and clarifies the tool's runtime behavior.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with sections for Args, Returns, Examples, and Error Handling. It is front-loaded with the main purpose, and each section serves a clear function. Despite being longer than a one-liner, every part is necessary given the lack of an output schema.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With no output schema, the description fully documents the return object shape, including field names and comments. It also covers error handling, parameter defaults, and explicitly differentiates from the sibling tool. For a tool with a single optional parameter, this is complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, with a clear description of 'symbol' including default and pattern. The description's Args section largely repeats this, but the Examples section adds concrete usage-to-value mappings (e.g., 'check ETH price' -> symbol='ETHUSDT'), which helps an agent map natural language to the parameter. This adds modest value over the schema, so a 4 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states a clear verb and resource: 'Get the current price and 24-hour trading stats for a crypto trading pair from Binance.' It explicitly distinguishes from the sibling tool by stating 'It does NOT return historical data — use binance_get_historical_ohlc for that.' This is specific and unambiguous.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit when-to-use examples ('what's the current BTC price', 'check ETH price') and a clear exclusion: 'Don't use when: you need price history over time (use binance_get_historical_ohlc).' This gives strong selection guidance relative to the sibling tool.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds major behavioral context: automatic pagination past Binance's 1000-candle limit, a 2000-candle cap with a 'truncated' flag, error handling for malformed dates/rate limits, and behavior for dates before available data. No contradiction with annotations.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is well-structured with clearly labeled sections (Args, Returns, Examples, Error Handling). Every sentence serves a purpose—pagination, truncation, and error behavior are all disclosed without fluff. Though lengthy, it is information-dense and front-loaded with the core purpose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    With no output schema, the description provides a detailed Returns block showing exact fields and types, plus a note field explanation. It covers edge cases (truncation, pre-2017 data) and error conditions, making invocation reliable. This is unusually complete for a read-only tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema descriptions already cover all four parameters (100% coverage), giving baseline 3. The description adds value through usage examples that map natural language queries to specific parameter values, and offers interval guidance ('1d' for most momentum/volatility backtesting). It also clarifies inclusive dates and data availability, though it largely restates schema info.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description opens with a specific verb and resource: 'Get historical OHLC candles for a crypto trading pair from Binance.' It clearly distinguishes from the sibling tool via the contrast with binance_get_current_price, explicitly stating 'Don't use when: you just need the current price.'

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides explicit 'Use when' examples for price series construction and explicitly contrasts with the current-price tool. It also recommends appropriate intervals for backtesting, such as '1d' for momentum/volatility. This goes beyond generic guidance to actionable selection criteria.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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