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regenrek

Deepwiki MCP Server

by regenrek
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as fetching a Deepwiki repo and returning Markdown, making it impossible to confuse with any other tool.

    Naming Consistency5/5

    The single tool name follows a consistent verb_noun pattern (deepwiki_fetch), and with only one tool, there is no inconsistency to evaluate. The naming is clear and adheres to a predictable structure.

    Tool Count2/5

    A single tool is generally too few for a server's purpose, as it limits functionality and may indicate an incomplete surface. For a Deepwiki server, one tool feels thin and under-scoped, lacking operations like search, update, or list repos that might be expected.

    Completeness2/5

    The server is severely incomplete for interacting with Deepwiki repos. It only provides a fetch operation, missing essential CRUD/lifecycle coverage such as creating, updating, deleting, or searching repos, which are likely needed for full agent workflows in this domain.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • 0 of 1 community issues answered or closed 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 is failing

  • 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions fetching and returning Markdown but omits critical details like authentication requirements, rate limits, error handling, or whether this is a read-only operation. For a tool with no annotation coverage, this is insufficient.

    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 extremely concise—a single sentence that directly states the tool's purpose. Every word earns its place, with no unnecessary elaboration. It's front-loaded and efficiently communicates the core functionality.

    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 tool's complexity (4 parameters, 50% schema coverage, no output schema, no annotations), the description is inadequate. It doesn't explain what 'fetching' entails, how the Markdown is structured, error conditions, or usage constraints. For a tool with significant undocumented aspects, more context is needed.

    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?

    Schema description coverage is 50%, with only the 'url' parameter well-documented in the schema. The description adds no parameter-specific information beyond what the schema provides. It doesn't explain the meaning of 'maxDepth', 'mode', or 'verbose' parameters, leaving gaps in understanding.

    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 action ('fetch') and resource ('deepwiki.com repo'), and specifies the output format ('return Markdown'). It distinguishes the tool by mentioning the specific domain (deepwiki.com) and output type. However, without sibling tools, there's no explicit differentiation from alternatives.

    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 versus other methods or tools. It lacks context about prerequisites, typical use cases, or limitations. With no sibling tools mentioned, it doesn't address alternatives within the server.

    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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