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luis0794

Violett MCP Server

by luis0794
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TypeScript
Remote

Server Quality Checklist

58%
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: fetch_url performs HTTP GET requests to retrieve content, while post_data performs HTTP POST requests to send data. There is no overlap or ambiguity between them, as they target different HTTP methods and actions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: fetch_url and post_data. The naming is predictable and readable, with no deviations in style or convention, making it easy for agents to understand their functions.

    Tool Count2/5

    With only 2 tools, the server feels thin for a general-purpose HTTP client. While it covers basic GET and POST operations, it lacks other common HTTP methods (e.g., PUT, DELETE, PATCH) and related utilities (e.g., headers, authentication), making it under-scoped for typical HTTP tasks.

    Completeness2/5

    The tool surface is severely incomplete for an HTTP server. It only supports GET and POST, missing essential operations like PUT, DELETE, PATCH, and configuration tools for headers or authentication. This will cause agent failures when more complex HTTP interactions are needed.

  • Average 3.1/5 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the HTTP method (POST) but lacks critical details like authentication requirements, error handling, rate limits, response format, or side effects. This is inadequate for a mutation tool with zero annotation coverage.

    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 a single, efficient sentence with zero wasted words. It's appropriately sized and front-loaded, directly stating the tool's function without unnecessary elaboration.

    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?

    Given the tool's complexity (HTTP POST with mutation potential), lack of annotations, and no output schema, the description is insufficient. It doesn't address authentication, error cases, response structure, or behavioral traits needed for safe and effective use, leaving significant gaps.

    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%, with both parameters ('url' and 'data') well-documented in the schema. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints, so it meets the baseline for high schema coverage.

    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 clearly states the action ('Send data') and method ('via HTTP POST request'), providing a specific verb+resource combination. However, it doesn't differentiate from the sibling tool 'fetch_url' (likely a GET request tool), which would require explicit comparison to achieve a perfect score.

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

    Usage Guidelines2/5

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

    No guidance is provided on when to use this tool versus alternatives like 'fetch_url' or other HTTP methods. The description only states what the tool does, not when it's appropriate, leaving the agent to infer usage context without explicit direction.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the HTTP method (GET) but doesn't cover important traits like error handling, timeout behavior, authentication needs, rate limits, or what 'content' includes (e.g., HTML, JSON). This leaves significant gaps for a tool that performs network operations.

    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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to understand at a glance. Every word earns its place.

    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?

    Given the complexity of HTTP operations and the lack of annotations and output schema, the description is incomplete. It doesn't address key aspects like response format, error cases, or behavioral constraints, which are crucial for an agent to use this tool effectively in real-world scenarios.

    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?

    The input schema has 100% description coverage, with the 'url' parameter clearly documented. The description adds minimal value beyond the schema by reinforcing the purpose of fetching from a URL, but doesn't provide additional context like URL format requirements or examples. This meets the baseline for high schema coverage.

    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 clearly states the action ('Fetch content') and resource ('from a URL via HTTP GET request'), making the purpose immediately understandable. It doesn't explicitly distinguish from the sibling 'post_data' tool, but the mention of 'GET request' provides implicit differentiation from POST operations.

    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 usage context through 'via HTTP GET request,' suggesting this is for retrieving content rather than submitting data. However, it doesn't provide explicit guidance on when to use this versus alternatives like 'post_data' or mention any prerequisites, limitations, or specific scenarios where this tool is preferred.

    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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