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fastmcp-me

Financial Data MCP Server

by fastmcp-me
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools, getStockQuote and getHistoricalData, are clearly distinct: one provides current real-time data while the other provides historical time-series data. There is no overlap or ambiguity in their purposes.

    Naming Consistency5/5

    Both tool names follow the same pattern: 'get' plus a descriptive noun in camelCase. This is consistent and predictable, making it easy for an agent to infer the action and resource.

    Tool Count3/5

    With only two tools, the server feels thin for a financial data service. While the two tools cover basic stock quote and historical data needs, the count is at the lower boundary of what is considered reasonable for a domain-focused server.

    Completeness3/5

    The server covers fundamental stock data retrieval (current and historical), but lacks other common financial data operations such as company fundamentals, options chains, or market indices. The surface is functional but minimal.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • 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

  • Behavior2/5

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

    There are no annotations, so the description carries the full burden of behavioral disclosure. It only states 'Get historical data' without mentioning rate limits, output format, data granularity, or any other behavioral traits, leaving significant gaps for a data-retrieval tool.

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

    Conciseness4/5

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

    The description is a single concise sentence that is easy to parse and front-loaded. While it is minimal, it avoids redundancy and clearly names the primary action and resource, though it could benefit from slightly more detail.

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

    Completeness2/5

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

    Given the lack of annotations and output schema, the description is incomplete. It does not explain the return format, data types, or any caveats around intervals or output size. A more complete description would clarify what 'historical data' entails and what the response contains.

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

    Parameters3/5

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

    The input schema fully covers all three parameters (symbol, interval, outputsize) with descriptions, so the baseline is 3. The tool description adds no additional parameter context beyond the schema, but it doesn't need to since the schema is self-explanatory.

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

    Purpose4/5

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

    The description clearly states the tool's purpose: retrieve historical data for a stock. While it doesn't explicitly distinguish from the sibling tool getStockQuote, the word 'historical' implies a contrast with current quote data, making the purpose understandable.

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

    Usage Guidelines2/5

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

    The description gives no guidance on when to use this tool versus alternatives like getStockQuote. It does not mention typical use cases, prerequisites, or exclusions, so the agent must infer usage solely from the name and input schema.

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

  • Behavior2/5

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

    There are no annotations, so the description carries full responsibility for disclosing behavior. It only states that it returns a 'current quote' but does not describe the response format, whether it includes fields beyond price, potential delays, authentication requirements, or any side effects. With no output schema, this lack of detail leaves the agent underinformed about what to expect.

    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 a single, concise sentence that front-loads the core purpose. It contains no fluff or redundant information, and every word earns its place. It is appropriately sized for the tool's simplicity.

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

    Completeness3/5

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

    The tool is low-complexity (one well-documented parameter, no annotations, no output schema). The description adequately states what it does, but without an output schema or any behavioral details, the agent cannot know the full set of fields returned by a 'quote' or handle edge cases. It is minimally viable but leaves clear gaps.

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

    Parameters3/5

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

    The input schema fully documents the single parameter 'symbol' with an example ('AAPL') and a clear description. The tool description adds only the word 'stock,' which provides no substantial new meaning beyond what the schema already states. Since schema coverage is 100%, the baseline of 3 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 clearly states the tool's purpose: 'Get the current quote for a stock.' It uses a specific verb (Get), names the resource (current quote), and the word 'current' distinguishes it from the sibling tool getHistoricalData. This is unambiguous and effective.

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

    Usage Guidelines4/5

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

    The description gives clear context that this tool is for current quote data, which implies a use case distinct from historical data. However, it does not explicitly mention when not to use it or name the alternative getHistoricalData as a fallback. The context is clear but lacks explicit exclusion.

    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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