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pavex

mcp-http-request

by pavex
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    fetch_text and http_request serve clearly distinct purposes: one extracts clean text from HTML pages, the other is a general HTTP client returning full response details. There is no ambiguity in selecting between them.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case verb_noun pattern: fetch_text and http_request. This pattern is predictable and clear.

    Tool Count4/5

    With only 2 tools, the set is slightly minimal but still appropriate for the server's focused purpose of HTTP requests and text extraction. It covers the core needs without bloat.

    Completeness4/5

    The tools cover the essential HTTP methods and provide a specialized text extraction feature. Minor gaps like file uploads or streaming exist, but for the stated purpose of 'HTTP requests' and 'fetching text', the surface is reasonably complete.

  • Average 3.9/5 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

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  • This repository includes a README.md file.

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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?

    No annotations provided, so description carries full burden. It discloses that it works with internal network addresses (security implication) and returns status/headers/body/elapsed time. Missing traits: error handling, idempotency, rate limits, authentication, redirect behavior. Moderate transparency but gaps remain.

    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?

    Four concise sentences, each serving a purpose: purpose and methods, body options, scope, return values. No redundancy, front-loaded with essential information. Highly efficient.

    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?

    Covers methods, body options, and return structure. However, missing details on timeout_ms, max_bytes, error behavior, and security considerations (e.g., auth, rate limits). Given 7 parameters and no output schema, the description is adequate but not fully 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 0%, so description must add meaning. It explains body_json vs body and auto Content-Type setting, but does not describe parameters like url, method, headers, timeout_ms, max_bytes. Only 2 of 7 parameters are elaborated, insufficient given no schema descriptions.

    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?

    Description clearly states it's a universal HTTP client performing various methods. It lists GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS. However, it does not explicitly differentiate from sibling 'fetch_text', which likely is a subset. The purpose is clear but sibling distinction is implicit.

    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?

    Provides explicit guidance on when to use body_json vs body for JSON APIs versus raw payloads. Mentions scope (remote, localhost, internal). However, no direct comparison with fetch_text or exclusions. The context is clear but alternative use cases are not fully addressed.

    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?

    Discloses key behaviors: stripping HTML, extracting title, truncating to max_chars, handling non-HTML as-is. Lacks details on error handling, rate limits, or auth, but sufficient given no 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?

    Three front-loaded sentences with no redundancy; each sentence adds value. Efficient and well-structured.

    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?

    For a read tool with no output schema and no annotations, it adequately describes behavior and return type (clean text). However, missing details on errors and timeout behavior.

    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 0%, so description must explain parameters. Explains url implicitly and max_chars with default, but does not mention timeout_ms parameter.

    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 it fetches a URL and returns clean readable text, distinguishing itself from the sibling http_request by emphasizing noise removal. Specific verb and resource.

    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?

    States ideal use for articles and web pages without context bloat, but does not explicitly exclude cases where raw HTML is needed or mention alternatives beyond the sibling.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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