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DOsinga

MCP Stock Tracker

by DOsinga
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

    With only one tool, there is no possibility of confusion. The tool's purpose is clearly defined for retrieving a stock quote.

    Naming Consistency4/5

    The single tool name follows a clear verb_noun pattern (get_stock_quote), which is consistent and readable. However, with only one tool, pattern consistency across multiple tools cannot be fully assessed.

    Tool Count2/5

    One tool is insufficient for a stock tracker. Users would expect additional functionality such as symbol lookup, historical data, or portfolio management, making the count feel overly minimal.

    Completeness2/5

    The tool surface is severely incomplete. A stock tracker should at least include listing available symbols or fetching historical prices, but only a single quote retrieval is provided.

  • Average 3.5/5 across 1 of 1 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.

  • 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

  • Behavior2/5

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

    With no annotations, the description must fully disclose behavioral traits. It states the tool returns 'real-time' data, which implies a read operation, but does not explicitly confirm it is safe (no side effects) or mention any rate limits, authentication needs, or error conditions. The disclosure is insufficient for a tool that likely interacts with an external API.

    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 well-structured docstring with Args and Returns sections. It is concise (few sentences) yet informative, with no redundant or irrelevant content. Every sentence adds value, and the structure aids quick parsing by an AI agent.

    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?

    Given the low complexity (single parameter, no output schema, no siblings), the description is partially complete. It clearly defines the purpose and parameter, but lacks usage guidance and behavioral transparency. For a minimal viable description, it meets the basics but has clear gaps that could be filled.

    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?

    Despite 0% schema coverage, the description adds substantial meaning to the only parameter: 'symbol: Stock ticker symbol (e.g., AAPL, MSFT, GOOGL)'. It clearly explains the domain and provides examples. This compensates well for the lack of schema descriptions.

    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 real-time stock quote for a symbol'. The verb 'Get' and resource 'stock quote' are specific, and the scope is defined. Since there are no sibling tools, no differentiation is needed. The description is direct 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 Guidelines2/5

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

    The description provides no guidance on when to use this tool or when not to. There are no alternatives mentioned, and no context about prerequisites or limitations (e.g., which exchanges are supported). The agent receives no help in deciding whether to invoke this tool over hypothetical others.

    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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