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scarletkc

vexor

by scarletkc
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Server Quality Checklist

75%
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  • Latest release: v0.28.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one for searching and one for indexing. There is no overlap or confusion between them.

    Naming Consistency5/5

    Both tools use the consistent 'vexor_' prefix followed by a clear single-word noun ('search', 'index'), forming a predictable verb-less pattern.

    Tool Count3/5

    With only 2 tools, the server feels thin for a typical MCP server, but the narrow scope of semantic search may justify this. It is at the low end of what is considered acceptable.

    Completeness4/5

    The two tools cover the core cycle of index and search, and the search tool returns source text, reducing the need for additional file reads. Minor gaps exist (e.g., no status or configuration tools), but for the stated purpose, it is largely complete.

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

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

    • 2 of 2 community issues answered or closed in the last 6 months
    • 60 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.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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

  • Behavior2/5

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

    No annotations are provided, so the description must fully disclose behavioral traits. It only says 'Build or refresh,' lacking details on destructiveness (e.g., whether refresh overwrites), auth requirements, rate limits, or side effects. This leaves significant gaps for an AI agent.

    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, very concise, and front-loaded with the core purpose. Every word earns its place with no fluff.

    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 short but covers the basic purpose. With 8 parameters and an output schema, it could mention how the output is structured or reference the sibling tool for fuller context. It is adequate but not thorough.

    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%, so the schema already documents all parameters. The tool description adds no additional parameter information beyond what is in the schema, meeting the baseline but not exceeding it.

    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 states 'Build or refresh the semantic index for a directory,' which is a specific verb+resource. It adds context with 'warm the cache or when auto_index is disabled,' but does not explicitly distinguish from its sibling tool 'vexor_search,' though the purpose seems distinct enough.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear usage scenarios (warm cache, auto_index disabled), giving context for when to use the tool. However, it does not mention when not to use it or alternatives like the sibling tool, which keeps it from a 5.

    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 transparency burden. It discloses that results include source text, reducing the need for a follow-up read—a helpful behavioral detail. However, it omits many behavioral traits: possible side effects of the `no_cache` parameter (temporary in-memory index), the conditional availability of content based on `content_budget`, and the fact that `include_content` can return 'content_unavailable'. The description gives partial insight but is not comprehensive enough to fully replace 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 two sentences and immediately states the core action. Every word serves a purpose: the first sentence defines the verb+noun, the second completes the picture by listing return fields and a practical implication (reducing follow-ups). No fluff, adequately front-loaded.

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

    Completeness2/5

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

    Despite an output schema existing (reducing the need to document returns), the description fails to provide context for the 12-parameter complexity. It does not mention the `mode`, `path`, `extensions`, `exclude_patterns`, or `no_cache` parameters that significantly alter behavior. No guidance on when to use this vs. `vexor_index`. For a tool with this many knobs and a sibling, the description is too terse to be considered complete.

    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%, so the baseline is 3. The tool description adds no additional parameter context beyond what the schema already provides—it does not mention any parameters or their roles. The description earns credit only for its overall purpose, not for parameter semantics, thus meeting but not exceeding the baseline.

    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 starts with 'Find files or code from a natural-language description,' a specific verb+resource pair that clearly states the tool's goal. It distinguishes from the sibling 'vexor_index' (which likely manages an index) by focusing on retrieval, not indexing. The return value summary further solidifies purpose.

    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 the tool should be used for natural-language file searches, but provides no explicit when-to-use or when-not-to-use guidance. It does not mention the sibling 'vexor_index' as an alternative or prerequisite, and offers no exclusions. The context is acceptable but minimal, leaving the agent to infer usage without direction.

    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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