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sixees

cURL MCP Server

by sixees
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Server Quality Checklist

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

  • Latest release: v3.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: curl_execute handles HTTP requests, while jq_query queries saved JSON files. There is no functional overlap, making selection unambiguous.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern in snake_case (curl_execute, jq_query). The style is uniform, promoting predictability.

    Tool Count3/5

    With only two tools, the server feels minimal for its intended scope. While it covers core HTTP request and JSON querying functionality, additional tools (e.g., manage saved files, list responses) could enhance the surface without overloading it.

    Completeness3/5

    The curl_execute tool is comprehensive for HTTP requests, but the server lacks auxiliary tools such as file management (list, delete saved files) or a way to retrieve response metadata separately. This leaves minor but notable gaps for certain workflows.

  • Average 4.7/5 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.

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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?

    Annotations declare read-only, non-destructive, idempotent, closed world. Description adds security path restrictions, max file size (10MB), auto-save behavior for large results, and filter limitations (no negative indices). No contradictions.

    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?

    Well-structured with sections: purpose, use cases, args, filter syntax, security, examples. Slightly long but all content is relevant. Front-loaded with purpose.

    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?

    Covers all 5 parameters, filter syntax, security constraints, size limits, examples, and behavioral details. No output schema, but return behavior (inline vs file) is explained.

    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%, baseline 3. Description adds detailed filter syntax with examples, security path rules, and default behaviors for max_result_size and save_to_file. This goes beyond schema 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 'Query an existing JSON file with a jq-like filter expression.' It distinguishes from sibling tool curl_execute by noting it avoids new HTTP requests. The verb 'query' and resource 'JSON file' are specific.

    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 lists use cases: extracting fields from saved responses, multiple queries, processing local files. It implicitly suggests use after HTTP fetch but lacks explicit 'when not to use' or direct alternative comparison beyond the sibling name.

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

  • Behavior5/5

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

    The description goes well beyond annotations, detailing automatic URL encoding, header formatting, response processing, error handling with exit codes, temp file lifecycle, and defaults like user-agent and method selection. No contradictions with annotations.

    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 long but well-structured with sections (Args, jq_filter Syntax, Examples, Error Handling, Temp File Lifecycle). It front-loads purpose and organizes information logically, though it could be slightly more concise.

    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 tool's complexity (20 parameters), the description thoroughly covers all aspects: return format, error handling, temp file lifecycle, jq_filter syntax with validation, and multiple examples. No output schema is needed as the description explains the response structure.

    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?

    Despite 100% schema coverage, the description adds significant value with examples, detailed jq_filter syntax, default behavior explanations (e.g., method defaults to POST if data provided), and clarifications on auto-saving to file. It enriches understanding beyond the 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 first sentence clearly states 'Execute an HTTP request using cURL with structured parameters', specifying the verb and resource. It distinguishes from sibling jq_query by focusing on HTTP requests rather than JSON querying.

    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 context for when to use the tool: making safe, structured HTTP requests. It offers examples and explains default behaviors, but does not explicitly contrast with alternatives or provide when-not-to-use guidance.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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