Skip to main content
Glama
Xenodocs

xenodocs-mcp-server

Official
by Xenodocs
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.1.5

  • Disambiguation5/5

    Each tool has a clearly distinct role: one identifies the canonical library name and version, the other retrieves the matching documentation. There is no functional overlap; they are designed to be used sequentially.

    Naming Consistency5/5

    Both tool names follow a consistent 'search_target' verb_noun pattern: 'search_library_name' and 'search_latest_documentation'. The style is uniform and readable.

    Tool Count4/5

    With only two tools, the server feels minimal but appropriately scoped for a narrow purpose: resolving library names and retrieving docs. It is slightly thin but not unreasonable for a focused utility.

    Completeness4/5

    The two tools cover the core workflow of finding a library and getting its documentation. A minor gap is the lack of version-specific documentation retrieval, as the second tool only targets the latest docs.

  • Average 4.3/5 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
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior4/5

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

    With no annotations, the description carries the full burden. It discloses that results are 'official, up-to-date' and warns that internal training data may be outdated, adding meaningful context about the tool's authority and freshness. It does not describe error behavior or rate limits, but for a read-only retrieval tool this is sufficient.

    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 front-loaded with the primary verb and resource, followed by concise usage guidance. Every sentence earns its place, with no redundant filler. It is appropriately sized for the tool's simplicity.

    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 the tool has two simple parameters, full schema descriptions, and an output schema (so return values are documented elsewhere), the description sufficiently covers purpose, usage, and data freshness. It lacks explicit handling of cases like library not found, but that is not essential for tool selection and invocation.

    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 ('query' and 'library_name'), so the schema already provides full parameter meaning. The description adds no additional parameter-level detail, only restating that the tool is for a library.

    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: 'Retrieve official, up-to-date code examples and API references for a library.' This clearly states what the tool does and differentiates it from the sibling 'search_library_name' by focusing on documentation content rather than library name lookup.

    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?

    It explicitly instructs when to use the tool: 'Use this tool to find the exact syntax, function signatures, and usage patterns' and provides a strong directive to 'always verify with this tool' instead of relying on training data. It does not explicitly name alternatives, but the sibling context and schema reference to 'search_library_name' make the boundary clear.

    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?

    No annotations are present, so the description carries the burden of disclosure. It indicates the tool prevents hallucinations and yields canonical library info, but does not describe return format, error behavior, or limitations. For a simple lookup this is adequate but not rich.

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

    Conciseness4/5

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

    The description is moderately long but well-structured: a CRITICAL directive, a concise purpose statement, and a numbered usage strategy. It avoids fluff, though the purpose statement slightly overlaps with the usage steps.

    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 tool is simple and has an output schema (not shown) to cover return values. The description explains its role in a two-step workflow and explicitly names the next tool, making the context complete for an agent. It omits edge cases but these are less critical here.

    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?

    Schema coverage is 100%, so parameters are documented. The description adds value by giving a concrete example (mapping 'langchain' to the specific package) and clarifying that query holds version constraints, going beyond the schema's field descriptions.

    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 'identifies the correct library version and official documentation source,' positioning it as a disambiguation/lookup resource. It also explicitly instructs to use it first, distinguishing it from the sibling search_latest_documentation.

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

    Usage Guidelines5/5

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

    Provides explicit when-to-use guidance: 'Always use this tool FIRST before generating code for any third-party library.' It also includes a usage strategy that directs the agent to call search_latest_documentation with the result, establishing a clear workflow and alternative.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

xenodocs-mcp-server MCP server

Copy to your README.md:

Score Badge

xenodocs-mcp-server MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Xenodocs/xenodocs-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server