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cma0232

react-perf-mcp

by cma0232
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools are clearly differentiated by input: one accepts a code snippet directly, the other reads a file from disk. While they share the same review purpose, the distinct input mechanisms prevent meaningful confusion.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun pattern (review_code, review_file), making the naming predictable and easy to understand.

    Tool Count3/5

    With only two tools, the server feels minimal for its intended scope. The count is borderline but acceptable for a focused performance-review utility, though it could benefit from additional entry points.

    Completeness3/5

    The tool surface covers two common input modes but lacks broader workflow support, such as analyzing a project directory, comparing branches, or generating reports. Agents may need to work around these missing capabilities.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 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?

    There are no annotations, so the description must disclose behavior. It states 'by reading it from disk', which indicates a read-only operation and no modification to the file. However, it does not disclose return value format, potential side effects, or required permissions/context.

    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, direct sentence that immediately conveys purpose and method. It is concise with no unnecessary words and well-front-loaded.

    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 tool is simple with one parameter, but no output schema exists and the description does not specify what the analysis returns (e.g., a report, list of issues). Given the read/analysis nature, this return value gap makes it incomplete.

    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 coverage is 100%, so the schema already fully defines the 'path' parameter. The description adds minimal additional meaning, just reinforcing that it's a React/TypeScript file, which is already in the schema. Baseline 3 applies.

    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 a specific verb ('Analyze'), a specific resource ('React/TypeScript file'), and the focus ('performance issues'). It also distinguishes itself from sibling 'review_code' by specifying file-level analysis via reading from disk.

    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 is provided on when to use this tool versus the sibling 'review_code'. The description implies usage for performance analysis of a file, but does not explicitly state when to prefer this tool or any alternatives.

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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It does disclose a notable behavioral trait ('using parallel AI agents'), which adds transparency. However, it lacks other behavioral details such as whether it is read-only, how results are returned, or any side effects. The mention of parallel agents is useful but incomplete.

    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, efficient sentence that front-loads the core action and then lists specific areas of analysis. There is no redundant or extra information.

    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 tool has no output schema or annotations, so the description is the main source of context. It covers what the tool does and the primary focus areas, but it does not describe the output format, prerequisites, or limitations. For a simple two-parameter tool, it is adequate but leaves some gaps.

    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 100% for both parameters, so the baseline is 3. The description does not add any additional semantics beyond what the schema already provides for 'code' and 'filePath'. It does not explain how the parameters interact or when to use them.

    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 a specific action ('Analyze a React/TypeScript code snippet') and resource (React/TypeScript code), with explicit focus areas (re-renders, memoization, bundle size, profiling patterns). It distinguishes from sibling 'review_file' by targeting snippets rather than files.

    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 use case is implied: it's for analyzing code snippets, while 'review_file' presumably handles full files. However, there is no explicit 'when to use' or 'when not to use' guidance, and no alternative is named. The implication is moderate but not explicit.

    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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