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dmmulroy

opensrc-mcp

by dmmulroy
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.3.0

  • Disambiguation1/5

    There is only one tool named 'execute', so disambiguation is not applicable. However, the tool description indicates it handles both querying and mutating source code, which could be seen as combining multiple distinct operations into a single tool, potentially causing confusion about its scope.

    Naming Consistency5/5

    With only one tool, naming consistency is trivially perfect. The tool name 'execute' follows a clear verb pattern, and there are no other tools to cause inconsistency.

    Tool Count2/5

    The server has only one tool, which is too few for its apparent purpose of managing and analyzing source code across multiple ecosystems (npm, pypi, crates, repos). The tool 'execute' bundles many operations (list, fetch, read, grep, etc.), making the surface overly simplistic for the domain's complexity.

    Completeness2/5

    The tool surface is severely incomplete. While the underlying API (opensrc) provides comprehensive operations (e.g., list, fetch, read, grep, astGrep, remove), the MCP server exposes only a single 'execute' tool, forcing all functionality through one interface. This creates significant gaps in discoverability and usability, as agents cannot directly access specific operations like fetching or searching without interpreting the tool's description and examples.

  • Average 3.2/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description mentions that data stays server-side, which is a key behavioral trait. It also indicates the tool supports both query and mutation operations. However, without annotations, it lacks details on side effects, error handling, or resource limits. The extensive description of the opensrc API indirectly clarifies behavior but is not focused on the tool itself.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is excessively long, containing type definitions, a full API declare block, and many examples. While the first two sentences are front-loaded and clear, the rest is verbose and repetitive. Many details could be moved to reference documentation, making the tool description hard to parse quickly.

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

    Completeness3/5

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

    The description is comprehensive about the environment (opensrc API) but lacks explicit information about the tool's return value, error handling, or execution context. Given the complexity of the runtime, the description covers the code execution environment well, but not the tool's own behavior completely. The output schema is absent, so some completeness is lost.

    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?

    The input schema has 100% coverage with a description for the only parameter ('code' as JavaScript async arrow function). The description adds value by providing extensive examples of valid code snippets, but it does not explain parameter semantics beyond what the schema already states. The baseline of 3 is appropriate since schema coverage is complete.

    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 that the tool queries and mutates fetched source code and that data stays server-side. This provides a clear verb+resource description, distinguishing the tool from hypothetical others that might expose data. However, the purpose could be slightly more specific about executing user-provided JavaScript code.

    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 does not explicitly state when to use this tool versus alternatives, but since there are no siblings, this is less critical. It provides examples of common use cases like fetching and exploring code, which imply usage. However, there is no guidance on when not to use it (e.g., for non-code tasks).

    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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