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PandaNePanda

Hyperagent MCP

by PandaNePanda
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TypeScript
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Server Quality Checklist

58%
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: hyperagent_models lists supported models, and hyperagent_chat handles chat interactions. There is no functional overlap between them.

    Naming Consistency5/5

    Both tool names follow a consistent snake_case pattern with the 'hyperagent_' prefix, making them easy to understand and predict.

    Tool Count4/5

    With only 2 tools, the server feels minimal but still covers the core functionality of listing models and chatting. A few more management tools (e.g., thread listing) would be welcome, but the count is not unreasonable for a focused bridge.

    Completeness4/5

    The essential operations (list models, send prompts, manage threads implicitly via thread_id) are covered. Minor gaps include the lack of explicit thread management tools like list or delete threads, but the core workflow is supported.

  • Average 4.1/5 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

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  • 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It mentions thread creation and continuation but lacks details on authentication, side effects, rate limits, or any other behavioral traits beyond the obvious send operation.

    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 with no wasted words. The verb 'sends' and models are front-loaded, immediately conveying the core action.

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

    Completeness3/5

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

    The description covers core usage (send, create/continue thread) but does not explain parameters like system_prompt or timeout_seconds, nor does it mention return values. Given no output schema, more detail would improve completeness.

    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?

    Schema coverage is 100%, so baseline is 3. The description adds value by explaining the thread_id creation/continuation pattern, which goes beyond the schema's parameter descriptions.

    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 sends a prompt and specifies models (Fable 5, GPT-5.6 Sol). It differentiates between creating a new thread and continuing an existing one, which distinguishes it from the sibling tool hyperagent_models.

    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?

    Provides explicit guidance: omit thread_id to create a thread, reuse it to continue. This tells the agent when to use different parameter combinations, though it does not list exclusions or when not to use the tool.

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

  • Behavior3/5

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

    No annotations are provided; the description mentions 'supported by this local bridge', which gives some context, but lacks details on any side effects, caching, or performance characteristics.

    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?

    Single sentence with no wasted words, front-loaded with the verb 'Lists'.

    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 listing tool without an output schema, the description is sufficient; it states the exact number of models and their provenance.

    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?

    No parameters exist, so the description does not need to add parameter meaning; baseline 4 applies.

    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 uses the specific verb 'Lists' and identifies the resource as 'Hyperagent models' with a precise count of two, clearly distinguishing from the sibling tool 'hyperagent_chat'.

    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 clearly states what the tool does, but no explicit guidance on when to use versus alternatives; however, as a simple listing tool with no parameters, usage context is implicit.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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