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gmoneyn

FinAgent

by gmoneyn
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    market_news and financial_data have clearly distinct purposes: one retrieves news articles, the other retrieves structured financial data. There is no ambiguity about which tool to use for a given request.

    Naming Consistency5/5

    Both tool names follow the same pattern of two lowercase words joined by an underscore, describing a noun (market_news, financial_data). The naming convention is fully consistent.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a financial domain. While the two tools cover distinct functions, the count is on the thin side and may not justify a dedicated server without additional capabilities.

    Completeness3/5

    The tools cover market news and several types of financial data (quotes, statements, estimates), but lack obvious features like historical price data, ticker search or screening, and portfolio tracking. Core reading operations are present, but the surface is not fully complete for broader financial workflows.

  • Average 4.4/5 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 status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior3/5

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

    With no annotations, the description carries the behavioral burden. It provides defaults, valid data_type values, and the return format (JSON string or error), but does not disclose side effects, authentication requirements, or error conditions beyond returning an error object.

    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 compact, well-organized docstring with a clear title, Args, and Returns sections. No filler or redundancy.

    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?

    The combination of parameter documentation, return type, and defaults fully describes the tool for an agent. The output schema handles detailed return structure, and the description covers the rest.

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

    Parameters5/5

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

    With 0% schema coverage, the description fully explains each parameter: ticker with examples, data_type with allowed values, period with options, and limit with default. This adds substantial meaning beyond the bare schema.

    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?

    Description explicitly states 'Retrieve financial data for a stock ticker' with a specific verb and resource, and the data_type options further clarify scope. This distinguishes it from the sibling market_news tool, which is for news.

    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 clearly indicates this tool is for financial data retrieval, which differentiates it from market_news. However, it does not explicitly state when to use it versus alternatives or provide exclusions.

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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states the return format as a JSON string with specific keys, and explains the default behavior for ticker. It does not mention any side effects or rate limits, but for a read-only news fetch, this is adequate.

    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 clear 'Args' and 'Returns' sections, and is concise without redundant information. Every sentence adds value, and the main purpose is front-loaded.

    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?

    Given the presence of an output schema and three params, the description is complete: it explains all parameters, return format, and default behaviors. It provides enough context for an agent to correctly invoke the tool without needing additional information.

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

    Parameters5/5

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

    The description adds significant meaning beyond the schema: it specifies that 'query' searches article titles, explains the optional 'ticker' behavior and its effect on index ETFs, and clarifies the default for 'days_back'. This greatly enhances understanding beyond the raw schema properties.

    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 starts with 'Fetch recent market news articles filtered by keyword,' which clearly states the tool's verb (fetch), resource (market news articles), and primary behavior (filtering by keyword). However, it does not explicitly distinguish itself from the sibling tool 'financial_data' beyond the obvious domain difference.

    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 provides clear context on how to use the tool, including parameter explanations and the default behavior of 'ticker' when omitted ('scans broad-market index ETFs'). It does not mention alternative tools or when not to use this tool, but the usage context is sufficiently clear.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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