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Roxanne299

GPT Proxy MCP Server

by Roxanne299
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

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 are clearly distinct: one returns a complete response, the other streams. An agent can easily choose based on whether streaming is needed.

    Naming Consistency5/5

    Both tools use a consistent 'gpt_chat_' prefix with an underscore, and the streaming variant is differentiated by '_stream'. Naming is predictable and clear.

    Tool Count3/5

    With only 2 tools, the set is minimal. While it covers the core chat functionality, it feels slightly thin for a server named 'GPT Proxy' which might imply broader capabilities like model selection or configuration.

    Completeness2/5

    The tool surface is incomplete for a general GPT proxy: it lacks tools for listing models, setting system prompts, managing conversations, or adjusting parameters. Agents have no way to configure or extend interactions beyond basic chat.

  • Average 3.3/5 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, the description carries full burden. It only states the basic behavior without disclosing traits like synchronous vs streaming, latency, or error handling. The behavior is straightforward but incomplete.

    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, clear sentence with no wasted words. It is appropriately short given the simplicity of the tool.

    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?

    For a simple chat tool with no output schema and a sibling tool, the description is adequate but could mention the streaming alternative or limitations (e.g., synchronous response). It is minimally complete.

    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 100%, and each parameter has a description. The tool description adds no additional meaning beyond what the schema provides, so baseline score of 3 is appropriate.

    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) and resource (a message to GPT) with outcome (get a response). It is specific but does not distinguish from the sibling tool 'gpt_chat_stream', which likely streams responses.

    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 on when to use this tool over alternatives like 'gpt_chat_stream'. The description lacks context about use cases or warnings about when not to use it.

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

  • Behavior3/5

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

    With no annotations, the description must disclose key behaviors. It reveals streaming as a major trait, but omits other relevant details like authentication, rate limits, or that it initiates a write operation (sending data).

    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 a single, front-loaded sentence with no superfluous words, achieving conciseness while communicating the core purpose.

    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 5 parameters, no output schema, and no annotations, the description is too sparse. It fails to specify how the streaming response is delivered (e.g., SSE, chunks), authentication needs, or error handling, leaving the agent underinformed.

    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?

    All 5 parameters are described in the schema (100% coverage), so baseline is 3. The description does not add extra context beyond the schema parameter 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 explicitly states the action (send a message) and the resource (GPT) with a clear distinction from sibling gpt_chat by adding 'streaming response', differentiating the streaming nature of this tool.

    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 for real-time responses due to 'streaming', but does not explicitly state when to use this over gpt_chat or list alternatives/exclusions.

    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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