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liyanfu896-cpu

hermes-local-agent-lanes

by liyanfu896-cpu
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools are clearly distinct: one is for querying a local model with restrictions, the other is a health check for endpoint connectivity. No overlap in purpose.

    Naming Consistency4/5

    Both tools share the prefix 'local_support', creating a consistent pattern. The health tool appends '_health', which is predictable but not a strict verb_noun structure.

    Tool Count3/5

    With only two tools, the surface is minimal. While a focused health-check server might justify this count, the support tool covers broad tasks, suggesting additional tools (e.g., configuration) could be expected.

    Completeness3/5

    The set provides a basic query interface and health check, covering primary needs. However, missing tools for configuration, model listing, or error handling leaves notable gaps for a local model server.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit in the last 12 weeks
    • No stable releases found
    • 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.

  • 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

  • Behavior2/5

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

    No annotations provided, so the description must carry full burden. It only states a connectivity check without detailing behavior like timeouts, error handling, or return format. Minimal transparency beyond verb and object.

    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 sentence, 8 words, with no redundancy. It is appropriately concise and front-loaded.

    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 zero-parameter tool with no output schema, the description is largely adequate. It could mention return type (e.g., boolean or status object), but the simplicity allows for a high score.

    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?

    There are no parameters, so the description adds value by clarifying the tool's purpose. With 100% schema coverage (vacuously), baseline is 4; the description does not need to compensate for missing parameter details.

    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 checks endpoint reachability, with a specific verb and resource. It implicitly distinguishes from the sibling tool 'local_support' by focusing on health rather than general support operations.

    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?

    No explicit guidance on when to use this tool versus 'local_support' or other alternatives. The description implies a readiness check but lacks context about prerequisites or follow-up actions.

    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?

    With no annotations, the description carries full burden. It discloses that risky mutations are blocked, adding behavioral context beyond the input schema. However, it lacks details on auth, rate limits, or what exactly happens when a prompt is blocked.

    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 purpose and examples, concise without wasted words. Every sentence earns its place.

    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 tool with two parameters and no output schema, the description is fairly complete, covering purpose, constraints, and examples. Minor missing detail on blocked behavior behavior but overall sufficient.

    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 baseline is 3. The description adds no additional meaning beyond the schema for either prompt or max_tokens parameters.

    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 asks a local model for low-risk support, listing specific tasks (summary, classification, critique, draft, simple code explanation, read-only long-context compression), distinguishing it from the sibling tool local_support_health.

    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 says to use for low-risk support and lists example tasks, while also stating risky mutation prompts are blocked. However, it does not explicitly mention alternatives or when to use the sibling tool.

    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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