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Fryingpan-Jason

agent-x-search

by Fryingpan-Jason
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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

  • Disambiguation5/5

    With only one tool, there is no possibility of overlap or confusion between tools. The single x_search tool has a clear, singular purpose: searching X/Twitter.

    Naming Consistency5/5

    The tool name x_search uses a clear snake_case verb_noun convention that directly reflects its function. With only one tool, there are no conflicting naming patterns to assess.

    Tool Count4/5

    A single tool is on the thin side, but it is appropriate for a server dedicated exclusively to search functionality. The tool is well-scoped and earns its place.

    Completeness5/5

    For the stated purpose of searching X/Twitter, the tool provides complete coverage of the domain. There are no obvious missing search-related operations, and the description suggests a robust implementation.

  • Average 4.1/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • 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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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

  • Behavior5/5

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

    Beyond the read-only and non-destructive annotations, the description discloses meaningful behavioral details: it performs a single Responses request, provides original-post citations, explicitly reports no-source status, reports usage, does not automatically retry, and depends on subscription or paid API mode. This is substantial context that annotations alone do not provide.

    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 concise and front-loaded with the core purpose, then packs important behavioral caveats into a few short sentences. There is no filler or redundant restatement of schema fields, and every sentence adds distinct value.

    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?

    The description gives strong behavioral context such as citations, no-source status, no retry, and API mode, which helps an agent understand side effects and limits. With no output schema, it does not fully describe the return shape, but it does disclose key output-related behaviors. Given the 8-parameter schema and missing parameter documentation for some fields, a bit more parameter context would make it complete.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is only 50%, leaving query, media, detail, and exclude_handles without descriptions in the schema. The tool description does not compensate for these gaps; it adds no parameter-level meaning beyond stating the search purpose. The schema covers count and dates, but the description itself adds essentially no parameter semantics.

    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 opens with a specific verb and resource: 'Search X/Twitter with native Grok search.' It clearly identifies the tool's function and even distinguishes the mechanism (native Grok search) from generic search. There are no sibling tools to differentiate, so this is fully unambiguous.

    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 usage for searching X/Twitter and notes the operational mode ('configured subscription or paid API mode'), but it does not explicitly state when to choose this tool over alternatives, provide exclusions, or describe typical scenarios. With no sibling tools, alternative routing is not required, but explicit usage guidance is still missing.

    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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