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jfdjaf

mcp-model-proxy

by jfdjaf
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

58%
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 tool 'ask_model' has a clearly distinct and singular purpose, making misselection impossible.

    Naming Consistency5/5

    The single tool name 'ask_model' follows a clear verb_noun pattern, and with only one tool, consistency is inherently perfect as there are no other names to compare or deviate from.

    Tool Count2/5

    A single tool is too few for a server named 'mcp-model-proxy', which suggests a broader scope for model interactions. This minimal set feels thin and likely incomplete for typical proxy functionalities like listing models, managing configurations, or handling different API endpoints.

    Completeness2/5

    The tool surface is severely incomplete for a model proxy domain. While 'ask_model' covers a core operation, there are significant gaps such as listing available models, configuring model parameters, handling streaming responses, or managing API keys, which will likely cause agent failures in real-world scenarios.

  • Average 2.8/5 across 1 of 1 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

  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool sends a prompt and returns a text response, but lacks details on permissions, rate limits, error handling, or other behavioral traits like whether it's read-only or destructive. The description is minimal and does not compensate for the absence of 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?

    The description is concise and front-loaded, consisting of two sentences that directly explain the tool's function without unnecessary details. Every sentence earns its place by defining the action and outcome efficiently.

    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 a tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It does not explain return values, error cases, or provide sufficient context for safe and effective use, leaving significant gaps in understanding the tool's behavior and requirements.

    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 the description must compensate for the lack of parameter documentation. However, it does not mention any parameters or their semantics beyond implying a 'prompt' is sent. With 4 parameters (prompt, system, model, maxTokens) and no explanation in the description, it fails to add meaningful context beyond what the bare schema provides.

    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's purpose: 'Call a model via the local MCP server' specifies the action and target, and 'Sends a prompt to an upstream service compatible with the Claude Messages API and returns the text response' elaborates on the operation and outcome. It distinguishes the tool by mentioning the Claude Messages API compatibility, though there are no sibling tools to differentiate from.

    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 specific contexts. It mentions the Claude Messages API compatibility, which hints at usage with compatible services, but lacks explicit when/when-not instructions or named alternatives.

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

GitHub Badge

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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