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CoReason-AI

coreason_multi_agent_debate

Official
by CoReason-AI
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JavaScript

Server Quality Checklist

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

  • Latest release: v0.1.7

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools. The tool's purpose is clearly defined.

    Naming Consistency5/5

    The single tool name 'multiagentdebate' is descriptive and uses consistent camelCase. There are no naming conflicts to evaluate.

    Tool Count3/5

    With just one tool, the server is minimal. While the tool is comprehensive for debates, a single tool feels slightly thin for typical MCP servers, but it is borderline acceptable for a niche function.

    Completeness5/5

    The tool covers the full debate lifecycle: registration, argument, rebuttal, and judging. All necessary actions are included, with no obvious gaps for the intended domain.

  • Average 4.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 9 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 failing

  • 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

  • Behavior5/5

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

    With no annotations provided, the description fully discloses the expected behavior: each persona registers once, alternates between argue/rebut, and a judge issues a verdict. It also clarifies the verdict format and termination condition, ensuring transparency about the tool's protocol.

    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 well-structured with a numbered list and bullet points, making it easy to follow. Every sentence adds value without redundancy, achieving high conciseness while covering all necessary details.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description covers the protocol, parameter roles, and termination condition comprehensively. However, it does not mention what the tool returns or any output format, which could be helpful for an agent to interpret results. This minor gap prevents a perfect score.

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

    Parameters5/5

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

    The description adds significant meaning beyond the input schema, explaining each parameter's purpose, constraints (e.g., round >=1, action enum), and context (e.g., targetAgentId only for rebut). It also describes the special formatting for judge verdicts, compensating for the 0% schema coverage.

    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 it is a 'Structured multi‑persona debate tool' and explains the primary purpose and flow of the tool. It specifies the actions (register, argue, rebut, judge) and their sequence, making the purpose distinct and comprehensible.

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

    Usage Guidelines5/5

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

    The description provides a detailed call sequence with steps for each persona, including when to register, argue, rebut, and judge. It explains the verdict format and when to set needsMoreRounds to false, offering clear guidance on proper usage.

    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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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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