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code-wgl

MCP Server with OpenAI Integration

by code-wgl
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TypeScript
Remote

Server Quality Checklist

50%
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: knowledge_search retrieves information from a knowledge base, while text_summarizer condenses provided text. There is no overlap in functionality, and an agent would easily differentiate between them.

    Naming Consistency4/5

    Both tools use snake_case naming, which is consistent. However, knowledge_search follows a noun_verb pattern (knowledge_search), while text_summarizer uses a noun_verb pattern with a suffix (text_summarizer), showing a minor deviation in naming style.

    Tool Count2/5

    With only 2 tools, the server feels thin for an 'OpenAI Integration' purpose, which typically implies broader capabilities like text generation, translation, or analysis. This limited set may not adequately cover the expected scope.

    Completeness2/5

    Given the server's name suggests OpenAI integration, there are significant gaps: no tools for text generation, translation, sentiment analysis, or other common LLM tasks. The surface is incomplete, likely causing agent failures for broader use cases.

  • Average 2.9/5 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. While it mentions the tool uses 'the configured LLM,' it doesn't disclose important behavioral traits like rate limits, authentication requirements, cost implications, or what happens with very long inputs. The description is minimal and lacks operational context.

    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 extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place, and it's front-loaded with the essential information. No wasted words or 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 has no annotations, no output schema, and 0% schema description coverage, the description is inadequate. It doesn't explain what the tool returns, how the summarization works, quality expectations, or error conditions. For a tool that processes text with an LLM, this leaves significant gaps in understanding.

    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. It mentions 'configured LLM' which relates to the 'model' parameter, but doesn't explain the 'text' parameter's requirements or constraints. The description adds minimal value beyond what the bare schema provides, failing to adequately compensate for the 0% 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 tool's purpose: 'Summarizes long text into concise bullet points using the configured LLM.' It specifies the verb ('summarizes'), resource ('long text'), and output format ('concise bullet points'). However, it doesn't explicitly differentiate from the sibling tool 'knowledge_search' (which likely searches rather than summarizes).

    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. It doesn't mention the sibling tool 'knowledge_search' or any other summarization methods. There's no context about when this tool is appropriate versus other approaches.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches a knowledge base, implying a read-only operation, but doesn't disclose any behavioral traits like what happens with no results, whether it supports pagination or sorting, or any rate limits or authentication needs. This leaves significant gaps for an AI agent.

    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 purpose without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand quickly.

    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 (a search tool with no annotations, no output schema, and low schema coverage), the description is incomplete. It lacks information on behavioral traits, usage guidelines, and parameter details, making it insufficient for an AI agent to fully understand how to invoke and interpret results from this tool.

    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 schema description coverage is 0%, so the description must compensate for the lack of parameter documentation. It mentions 'query' implicitly by describing the search action, but doesn't add specific meaning beyond what the schema provides (e.g., no details on query syntax, expected formats, or examples). With one parameter and low coverage, this is a minimal baseline.

    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 ('Searches') and the resource ('curated knowledge base snippets stored as markdown files'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from its sibling 'text_summarizer', which is a different function but could be related in some contexts.

    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, such as the sibling 'text_summarizer'. It lacks any context about when this search tool is appropriate or when other tools might be better suited, offering only a basic functional statement.

    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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