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Rajeev-Shyam

Quellgeist

by Rajeev-Shyam
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Python
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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 confusing it with another tool. The purpose of query_logs is clearly defined and distinct.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (query + logs), which is consistent, descriptive, and follows common conventions.

    Tool Count2/5

    A single tool feels too few for a server focused on incident logs, as typical workflows would likely require additional operations such as fetching a specific log entry or listing available log sources.

    Completeness3/5

    The server provides only a query operation, missing obvious capabilities like retrieving a single log by ID or managing log sources. While querying is the core operation, the lack of additional functions creates notable gaps for common use cases.

  • Average 3.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 126 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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

  • Behavior3/5

    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 reveals a useful behavior: rows carry a stable int id. However, it does not explicitly state that the tool is read-only (though 'Query' implies it) or disclose any side effects, error handling, or parameters' expected formats. This partial transparency is enough for a basic query tool but not comprehensive.

    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, front-loaded sentence that conveys the core purpose, the optional filters, and a key behavior (stable int id) with no redundant words. It is an excellent model of conciseness.

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

    Completeness4/5

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

    Given the tool's simplicity (three optional parameters, no required arguments, and an output schema exists), the description covers the essential aspects: what it does, the filterable parameters, and a row property. It does not explain return values, but that is handled by the output schema. Minor missing details like time-format or log-level enumerations prevent a perfect score.

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

    Parameters2/5

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

    The schema has zero per-property descriptions, and the description only lists the parameter names ('optional since/level/route') without explaining their meaning, formats, or allowed values. 'since' could refer to a timestamp, 'level' to log severity, and 'route' to an API endpoint, but these are not specified. The description adds minimal semantic value beyond the schema's property names.

    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 uses a specific verb ('Query') and a clear resource ('structured incident logs'), making the tool's purpose immediately understandable. Although no siblings are listed, the description provides a precise scope and mentions optional filters, which further clarifies what the tool does.

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

    Usage Guidelines3/5

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

    The description implies usage by mentioning optional filters (since/level/route), but does not provide explicit guidance on when to use this tool versus alternatives, nor does it state any exclusions or prerequisites. With no sibling tools to differentiate from, the lack of explicit when-to-use guidance is a moderate gap.

    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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