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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools, 'ingest' and 'query', have clearly distinct purposes: one adds documents to the wiki, the other retrieves information. There is no ambiguity or overlap in their functionality.

    Naming Consistency5/5

    Both tool names follow a single-verb pattern that directly describes their action ('ingest' and 'query'), making them consistent and predictable.

    Tool Count3/5

    With only two tools, the server feels under-scoped for a wiki knowledge base. While the tools cover core ingest and query actions, a more complete server would include additional management tools.

    Completeness2/5

    The server lacks essential operations such as listing wikis, deleting wikis, or updating ingested content. Users have no way to manage the wiki beyond ingesting and querying, which are significant gaps.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 35 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 passing

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  • 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?

    Describes the process (searches wiki pages, synthesizes answer with citations) but lacks explicit statements about side effects or safety (e.g., read-only, no destructive actions). With no annotations, more explicit behavioral disclosure would improve clarity.

    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?

    Extremely concise – three sentences plus a two-item Args list. Front-loaded with purpose. Every sentence adds value; no redundancy or fluff.

    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?

    Has output schema, so return values are documented. Covers wikilinks citations. Missing details on prerequisites, error handling, or exact wiki name format, but given the simple two-parameter input, the description is nearly complete.

    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?

    Schema description coverage is 0%, but the description's Args section provides clear, concise explanations for both parameters ('wiki_name: Name of the wiki to query', 'question: The question to ask'), adding meaning beyond the bare parameter 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 clearly states the tool queries a wiki knowledge base and returns an AI-synthesized answer with wikilinks citations. It distinguishes from sibling tool 'ingest' by focusing on querying rather than adding data.

    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?

    No explicit guidance on when to use this tool versus alternatives. The sibling tool 'ingest' is mentioned but not contrasted. There is no 'when not to use' or prerequisite information.

    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, description covers main behaviors: fetching source, saving, processing by agent. Mentions wiki_name creates if doesn't exist. Could add more on side effects or failure modes, but 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?

    Two well-structured paragraphs: purpose and process followed by Args list. No redundant sentences, efficient and clear.

    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 output schema exists, return values need not be described. Input and process are explained. Lacks details on expected output after ingestion, but still sufficient without annotations.

    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?

    Schema coverage is 0%, so description fully explains both parameters: wiki_name (target wiki, auto-created) and source (local path or URL). Adds meaning beyond schema type 'string'.

    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 function: 'Ingest a document into a wiki knowledge base.' It details the process steps (fetch, save, process) and distinguishes from the sibling 'query' tool by focusing on ingestion.

    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?

    Usage is implied (add documents to wiki) but no explicit guidance on when to use this vs. alternatives like 'query'. No when-not-to-use or prerequisites stated.

    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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