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delian

codeguide-mcp

by delian
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusing it with other tools. The purpose is singular and clear.

    Naming Consistency5/5

    The single tool uses a clear verb_noun convention ('get_guide'), which is consistent and readable. There are no competing naming patterns.

    Tool Count2/5

    The server has only one tool, which is too few for the apparent scope of a code guide service. Users would typically need additional tools such as listing or searching guides.

    Completeness2/5

    The tool set lacks any discovery mechanism. An agent must know the exact guide identifier upfront, with no way to enumerate or search available guides, leading to significant gaps in functionality.

  • Average 3.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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.

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

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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 full burden of disclosing behavior. 'Get the content' implies a read-only operation with no side effects, but it does not mention error handling, authentication requirements, or anything about the response format. It meets the minimum but lacks depth.

    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 one short sentence with zero wasted words. It is front-loaded and directly states the core purpose, making it highly concise and easy to parse.

    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?

    The tool is simple (1 parameter) and has an output schema, so return values are covered. However, the description is too sparse to be considered complete: it does not explain how to specify guide_name, nor does it provide any usage context or caveats. An agent would struggle to invoke this tool correctly without additional information.

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

    Parameters1/5

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

    The only parameter, guide_name, has no description in the schema (0% coverage) and the tool description does not clarify what format the name should take, whether it must be exact, case-sensitive, or how to discover valid guide names. The description provides absolutely no additional meaning beyond the parameter name.

    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 action (get) and the resource (content of a specific coding guide). It is unambiguous and distinguishes the tool as a retrieval operation, even though there are no sibling tools to differentiate from.

    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 the tool is used when you need the content of a guide, but it does not provide explicit context on when to use it versus alternatives, mention prerequisites, or state any exclusions. With no sibling tools, the lack of explicit guidance is acceptable but still leaves room for improvement.

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

GitHub Badge

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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