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uamcp

Understand-Anything MCP Server

by uamcp
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Server Quality Checklist

75%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one provides a module-level architecture breakdown, the other gives a high-level statistical summary of the graph. There is no overlap in their outputs, so an agent can reliably choose between them.

    Naming Consistency5/5

    Both tools follow a consistent 'ua_<descriptive_name>' pattern in snake_case. The naming is predictable and clearly conveys each tool's function.

    Tool Count2/5

    With only two tools, the server feels too limited for its stated purpose of codebase understanding. A well-scoped server for this domain would typically offer at least 5–10 tools to cover key operations like querying, searching, and exploring relationships.

    Completeness2/5

    The tool surface provides only high-level summaries (architecture and statistics). Essential operations for understanding a codebase—such as searching for specific nodes, exploring dependencies, or querying relationships—are missing, leaving significant gaps.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 30 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

  • 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
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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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description's additional context (use of knowledge graph, return of breakdown) is helpful but does not significantly expand behavioral disclosure beyond what is in the annotations.

    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, well-formed sentence that front-loads the core purpose. It contains no wasted words and is appropriately concise.

    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 no output schema, the description explains the return type (breakdown of files per directory). It covers the tool's function and output, though it could be more complete by mentioning any prerequisites or limitations of the knowledge graph.

    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?

    The tool has 0 parameters, and schema coverage is 100% (vacuously). Per the rubric, a baseline of 4 applies. The description does not need to add parameter information.

    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?

    Description clearly states the verb (Generate) and resource (module-level architecture report from the knowledge graph), and specifies the output (breakdown of files per directory). However, it does not explicitly distinguish itself from the sibling tool ua_graph_summary, so it scores a 4 rather than a 5.

    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 guidance on when to use this tool versus alternatives is provided. The description simply states what the tool does, leaving the agent to infer usage context.

    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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by disclosing the specific return content (node and edge counts), which goes beyond what annotations provide. However, it does not mention any side effects or further behavioral traits.

    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 two sentences long with no extraneous information. The first sentence front-loads the purpose, and the second adds output detail. Every word earns its place.

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

    Completeness5/5

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

    Given zero parameters, no output schema, and rich annotations, the description fully explains what the tool does and what it returns. It is complete for its simplicity and purpose.

    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?

    The input schema is empty with 100% coverage, so the description does not repeat parameters. It adds meaning by explaining the purpose and output of the tool, which the schema alone cannot convey. This goes beyond the baseline of 3.

    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 gets a 'high-level statistical summary' of the codebase knowledge graph, specifying it returns node and edge counts. This verb+resource combination uniquely identifies it from the sibling tool 'ua_architecture_report' which likely provides a different kind of report.

    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 use for 'gauging project size' but does not explicitly state when to use this tool vs. the sibling tool 'ua_architecture_report'. No exclusions or alternatives are mentioned, so guidance is solely implied.

    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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  • 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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