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albinjal

multi-agent-debate-mcp

by albinjal
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

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 risk of confusion with other tools. The tool's purpose is clearly singular.

    Naming Consistency5/5

    With a single tool, naming consistency is trivially maintained. The name 'multiagentdebate' is descriptive and unambiguous.

    Tool Count3/5

    One tool for a multi-step debate process feels thin. While the tool is comprehensive in its actions, it would benefit from splitting into separate tools for registration, arguing, and judging to improve modularity.

    Completeness2/5

    The tool covers the core debate actions but lacks tools for listing debates, retrieving histories, or managing multiple concurrent debates. This creates significant gaps for an agent trying to orchestrate complex workflows.

  • Average 4.2/5 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
    • Last stable release on
    • 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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It explains the protocol and verdict format, but does not explicitly disclose state management, side effects, or error behavior, which are relevant for such a tool.

    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 well-structured with numbered steps and a parameter list, making it easy to follow. It is appropriately detailed, though slightly lengthy; could be condensed without losing clarity.

    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?

    Given the tool's complexity and the absence of an output schema, the description covers the essential protocol and parameter semantics. It does not detail return values or error handling, but the sequence and usage are sufficiently explained.

    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?

    With 0% schema description coverage, the description fully compensates by listing each parameter with explanations, including the enum values for action, the meaning of content and targetAgentId, and the role of needsMoreRounds.

    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 as a 'Structured multi-persona debate tool' and explains the call sequence with specific actions, making it distinct even without siblings.

    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 provides a typical call sequence (steps 1-4) and explains when to use each action (register, argue, rebut, judge) and when to set needsMoreRounds to false. However, it lacks explicit when-not-to-use guidance or alternatives.

    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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