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kinhunt

MCP AI Gateway

by kinhunt
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'chat_completion' has a clear, distinct purpose of sending chat completion requests to an AI API provider.

    Naming Consistency5/5

    The single tool name 'chat_completion' follows a clear and consistent verb_noun pattern. Since there is only one tool, there is no inconsistency to evaluate, and the naming convention is appropriate for its function.

    Tool Count2/5

    A single tool is generally too few for a server's purpose, as it limits functionality and may indicate an incomplete or overly narrow scope. For an 'MCP AI Gateway' that presumably handles AI interactions, one tool feels insufficient for typical enterprise use cases.

    Completeness2/5

    The tool surface is severely incomplete for an AI gateway domain. While 'chat_completion' covers basic chat requests, there are obvious gaps such as tools for managing models, handling different API providers, processing responses, or supporting other AI tasks beyond chat completions.

  • Average 3.1/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • This repository is licensed under MIT License.

  • 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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It adds some context: it specifies the API provider (ANTHROPIC), notes it returns 'raw response without format conversion,' and mentions 'enterprise use.' However, it lacks critical details like authentication requirements, rate limits, error handling, or whether it's a read/write operation. The description doesn't contradict annotations (none exist), but it's incomplete for a complex tool with 10 parameters.

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

    Conciseness3/5

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

    The description is reasonably concise but poorly structured. The first sentence is clear and front-loaded, but the second sentence ('Custom AI model for enterprise use') feels tacked on and doesn't integrate well with the rest. It could be more cohesive, and some phrases (like 'etc.') are vague. Overall, it's adequate but not optimally organized.

    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?

    Given the complexity (10 parameters, nested objects, no output schema), the description is minimally adequate. It covers the basic purpose and some behavioral aspects, but lacks depth for a tool of this scope—no output details, error handling, or advanced usage notes. With no annotations and no output schema, more context would be beneficial, but it's not completely inadequate.

    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%, so the schema already documents all parameters thoroughly. The description mentions 'supports parameters like model, messages, temperature, max_tokens, stream, etc.' but adds no meaningful semantics beyond what the schema provides (e.g., no explanations of trade-offs or typical values). This meets the baseline of 3 when schema coverage is high, but doesn't compensate with extra insights.

    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 tool 'sends a chat completion request to the configured AI API provider (ANTHROPIC)' and mentions it 'returns the raw response from the API without format conversion.' This specifies the verb (send request), resource (chat completion), and key behavioral trait (raw response). However, it lacks explicit differentiation from siblings (though none exist), and the second sentence about 'Custom AI model for enterprise use' is somewhat vague and disconnected.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It mentions the provider (ANTHROPIC) and that it's for 'enterprise use,' but this is too vague to serve as practical usage guidance. No explicit when/when-not statements or alternative tools are referenced.

    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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