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groq

groq-compound-mcp-server

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by groq
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.7

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: one for code execution, the other for real-time information. No overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent 'ask_with_' prefix pattern, making it predictable for agents to select the right tool.

    Tool Count4/5

    Two tools is slightly thin for a 'compound' server, but each tool covers a distinct, specialized need. The count is reasonable but could be expanded.

    Completeness3/5

    The server covers code execution and real-time info, but lacks a general knowledge query tool. This is a notable gap for a question-answering server.

  • Average 3.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 for behavioral disclosure. It states the tool uses a Groq model but does not explain whether it performs web searches, has latency, or requires network access. The presence of include/exclude domains suggests search functionality, but this is not explicitly stated, leading to potential misuse.

    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 concise, one sentence long, with the core purpose front-loaded. It avoids unnecessary words and is easy to parse. Minor improvement could be splitting into two sentences for better structure.

    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 lack of an output schema and the complexity of 5 parameters (some with enums), the description should provide more context, such as what the tool returns (e.g., an answer, possibly with citations) and when it might fail (e.g., non-real-time questions). It currently omits these details.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, providing baseline clarity. The description adds meaningful context beyond the schema, such as recommending compound-beta-mini for quick answers and advising verbose mode only when necessary, which helps the agent choose appropriate parameter values.

    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 clearly states the tool's purpose: asking a question requiring real-time information using a Groq model, with examples like news and current events. It distinguishes itself from the sibling tool 'ask_with_code_execution' by focusing on real-time data needs.

    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 does not provide guidance on when to use this tool versus alternatives. It neither mentions when not to use it nor contrasts with the sibling tool. The lack of usage context leaves the agent to infer when real-time information is needed.

    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 must fully disclose behavioral traits. It mentions Python REPL interaction but fails to describe execution details (e.g., sandbox, persistence, side effects, rate limits). The behavior of the code execution remains opaque.

    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, front-loaded sentence with no wasted words. Every part contributes to explaining the tool's purpose and ideal use case.

    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 5 parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain the purpose of key parameters like 'model' or 'mode', nor does it clarify what the tool returns after execution.

    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 baseline is 3. The tool description does not add meaning beyond the schema; it merely reiterates the code execution context without elaborating on how parameters like 'model' or 'mode' affect the tool's behavior.

    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 uses a specific verb ('Ask') and specifies the resource ('questions that benefit from Python REPL interaction'). It clearly distinguishes from the sibling tool 'ask_with_realtime_information' by highlighting code execution capabilities.

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

    Usage Guidelines4/5

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

    The description states that the tool is for questions benefiting from Python REPL interaction, which gives clear context on when to use it. However, it does not explicitly explain when not to use it or directly compare with the sibling tool, missing 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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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