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prefrontal-systems

Sub-Agents MCP

by prefrontal-systems
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Server Quality Checklist

67%
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  • Latest release: v0.4.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'run_agent' has a clearly defined and distinct purpose of delegating complex tasks to autonomous agents.

    Naming Consistency5/5

    The naming pattern cannot be inconsistent with only one tool. The tool name 'run_agent' follows a clear verb_noun pattern, which would be consistent if more tools existed.

    Tool Count2/5

    A single tool is too few for a server named 'Sub-Agents MCP', which implies functionality for managing or interacting with multiple agents. The scope suggests operations like listing agents, checking status, or controlling execution, but only delegation is provided.

    Completeness2/5

    The tool surface is severely incomplete for the implied domain of sub-agent management. While 'run_agent' handles task delegation, there are obvious gaps such as listing available agents, monitoring agent status, terminating agents, or configuring agent parameters, which are essential for a coherent agent management system.

  • 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
    • 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.

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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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's a delegation tool that returns a session_id for maintaining conversation context across executions. It explains the autonomous execution nature and the importance of reusing session_id, though it could mention potential side effects like resource consumption or execution time.

    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 efficiently structured in two sentences: the first states the purpose and examples, the second explains the return value and context continuity. Every sentence adds value with zero waste, making it easy to parse and understand quickly.

    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 complexity of a delegation tool with no annotations and no output schema, the description does well by explaining the tool's purpose, usage context, and key behavioral aspects like session continuity. It could be more complete by detailing output format or error handling, but it covers the essentials adequately for an agent to use it correctly.

    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 schema already documents all parameters thoroughly. The description adds some context by mentioning session_id reuse for continuity, but doesn't provide additional meaning beyond what's in the schema descriptions (e.g., how 'agent' relates to 'list_agents', or practical examples for 'extra_args'). Baseline 3 is appropriate when schema does the heavy lifting.

    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 with specific verbs ('delegate', 'returns') and resources ('autonomous agent', 'session_id'), and provides concrete examples of tasks (refactoring, fixing test failures, analysis, batch operations). It distinguishes this as a delegation tool for complex multi-step tasks, which is unambiguous even without sibling tools.

    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 explicitly states when to use this tool ('complex, multi-step, or specialized tasks') with helpful examples, and mentions context continuity via session_id. However, it doesn't specify when NOT to use it or alternatives, and with no sibling tools, this limitation is acceptable but prevents a perfect score.

    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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