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praveenc

MCP Server Builder

by praveenc
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: search returns ranked snippets, fetch retrieves full document content. No overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern ('search_mcp_docs', 'fetch_mcp_doc'), making them predictable and easy to understand.

    Tool Count4/5

    With only two tools, the server is lean but covers the core documentation retrieval workflow (search and fetch). The name 'MCP Server Builder' might imply broader functionality, but the tool set is appropriate for a focused documentation assistant.

    Completeness4/5

    The two tools cover the essential search and retrieve cycle for MCP documentation. A minor gap is the lack of a tool to list available documentation sections or browse structure, but the current set is functional with no dead ends.

  • Average 4.4/5 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 transparently describes the operation (fetching content), supported sources, return structure, and error handling. It does not mention potential network delays or rate limits, but the tool's read-only nature and simple behavior are well-covered.

    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 well-structured with sections, bullet points, and clear guidance. It is front-loaded with the core purpose. Minor redundancy in the bullet list could be trimmed, but overall it is readable and efficient.

    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's simplicity (one required parameter, no annotations, output described), the description covers purpose, usage context, parameters, and return values adequately. It lacks details like authentication requirements or URL format validation, but these are not critical for a straightforward fetch tool.

    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 0%, so the description must compensate. It explicitly documents the 'uri' parameter, specifies supported domains, and implies it expects HTTP/HTTPS URLs. This adds meaningful context beyond the schema's bare type definition.

    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 fetches full document content from specific domains (MCP and FastMCP), with a clear verb-resource pairing. It distinguishes itself from sibling 'search_mcp_docs' by indicating it retrieves full pages rather than snippets.

    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 explains when to use the tool ('when search snippets aren't sufficient'), lists supported domains, and mentions that URLs can come from search results or be provided directly. It lacks an explicit 'when not to use' statement but strongly implies the alternative.

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

  • Behavior4/5

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

    With no annotations, the description must disclose behavior. It explains that the tool searches two sources, returns ranked results, and allows filtering. It details the output format (url, title, score, snippet, source). No side effects are mentioned, but for a search tool, this is acceptable.

    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 well-structured with bullet points and clear sections, front-loading the purpose. However, it is slightly verbose; a few sentences could be trimmed without losing meaning.

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

    Completeness5/5

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

    Given the presence of an output schema, the description needn't explain return values, but it does. It covers all relevant aspects: purpose, parameters, sources, output format, and context of building MCP servers. Sibling tool existence is noted. The description is complete.

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

    Parameters5/5

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

    Schema description coverage is 0%, so the description must compensate. It explains 'query' with examples, 'k' with default 5, and 'source' with available values. This adds substantial meaning beyond the raw schema.

    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 searches MCP protocol and FastMCP framework documentation, distinguishing it from the sibling tool 'fetch_mcp_doc' which likely retrieves a specific document. The verb 'Search' and resource 'documentation' are precise.

    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 gives explicit context: 'Use this to find documentation for building MCP servers...' and implies when not to use (fetch_mcp_doc). However, it lacks explicit 'when not to use' statement or direct alternative naming.

    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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