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rputasi

mcp-swarm-router

by rputasi
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TypeScript
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one provides an inventory of available agents, the other executes a task with a specified agent. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (get_agent_roster, delegate_task), using snake_case and clear action verbs. The naming is uniform and predictable.

    Tool Count3/5

    With only 2 tools, the set is on the thin side but still covers the core workflow of a router: discovering agents and delegating to them. It feels slightly sparse for a 'swarm' concept, but each tool is essential.

    Completeness4/5

    The tool pair covers the full delegation lifecycle needed: learn who is available, then execute a task. Missing features like parallel delegation or cancellation are minor gaps for a simple router implementation, and agents can work around them by calling delegate_task repeatedly.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • 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.

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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, the description carries the full burden. It discloses the spawn-wait-return mechanism and the return payload (stdout/stderr/exit code), adding real behavioral detail beyond the tool name. However, it does not warn about potential side effects of running an arbitrary agent CLI in the workspace, which is an important unmentioned behavior.

    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?

    Two sentences, front-loaded with the definitive action and outcome, with the follow-up usage hint. No wasted words or redundancy.

    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?

    For a tool with no output schema and moderate complexity, the description sufficiently covers purpose, return values, and prerequisite usage. It omits timeout failure behavior, but the timeout parameter's schema description partially addresses it. Overall, the context is adequate.

    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 input schema covers 100% of parameters with descriptions, so the baseline is 3. The description paraphrases how workspace_path and prompt are used but does not add semantic details beyond the schema.

    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 states a specific verb ('spawns') and resource ('agent's CLI'), and clearly distinguishes the tool from the sibling by describing execution rather than roster lookup. It explains the full lifecycle: spawn, wait, and return output/exit code.

    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?

    It explicitly directs the user to call get_agent_roster first to choose the correct agent_name, which provides clear usage context. It does not explicitly state when not to use this tool, but the sequencing guidance is unambiguous.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    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 clearly states the output (list with id and specialization) and implies a read-only operation via 'Returns'. It does not mention edge cases like empty roster or authentication, but for a simple getter the core behavior is transparent.

    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?

    Two sentences, front-loaded with the main purpose, followed by usage guidance. Every word earns its place with no fluff or repetition.

    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?

    For a simple no-parameter list getter with no output schema, the description covers the return fields and the use case. It could briefly mention what happens if no agents are available, but the provided context is sufficient for most scenarios. The sibling relationship is clearly defined.

    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?

    The tool has zero parameters, so the schema coverage is trivially 100%. Per the rubric, 0 params gives a baseline of 4, and the description does not need to explain parameters. No additional info is necessary.

    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 starts with 'Returns the list of local agents available for delegation', which is a specific verb and resource. It also mentions the returned fields (id and specialization), clearly distinguishing it from the sibling tool delegate_task.

    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?

    Explicitly states 'Call this before delegate_task to decide which agent_name fits a given subtask.' This gives a clear when-to-use directive and names the alternative tool, providing unambiguous usage guidance.

    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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  • Evaluate tool definition quality.

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