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sideEyeLabs

PearClaw

by sideEyeLabs
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JavaScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: consult for decisions, get for polling messages, notify for one-way updates. No overlap, clearly differentiated by description.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (consult_supervisor, get_supervisor_message, notify_supervisor).

    Tool Count4/5

    Three tools is on the low end but appropriate for a focused supervisor communication interface. It covers the essential actions without being excessive.

    Completeness4/5

    The set covers the core interactions with a supervisor: asking for approval, polling for messages, and sending updates. No obvious gaps for the intended purpose.

  • Average 4.2/5 across 3 of 3 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 status not available

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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 fully shoulders behavioral disclosure. It reveals that the tool returns a decision object with 'approve', 'block', or 'modify', along with a reason and optional suggestion. This transparency about the blocking nature and the interaction with an external agent is sufficient for a simple approval tool. No contradictions with missing annotations.

    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 extremely concise: two sentences covering purpose, usage context, and return format. Every sentence adds value without redundancy. The structure is front-loaded with the core purpose and then immediate usage guidance.

    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 approval consultation tool, the description provides necessary context: when to use, what to expect in return. The schema covers parameters well. While it doesn't mention potential side effects or failure modes, the return format and purpose are clear. Slightly more detail on what happens if the supervisor is unavailable would improve completeness, but it's 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 has 4 parameters with 100% description coverage, so the schema already provides detailed semantics for each parameter. The tool description adds no extra information about the parameters beyond what the schema offers. Baseline of 3 is appropriate as the 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: consulting a supervisor agent before specific actions (writing files, running commands, making decisions). It distinguishes from siblings like 'get_supervisor_message' which is for retrieving messages, and 'notify_supervisor' for notifications, by focusing on approval. The verb 'consult' and resource 'supervisor agent' are specific and unambiguous.

    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 clear when-to-use guidance: 'before writing files, running commands, or making architectural decisions.' It does not explicitly list when not to use or alternatives, but the context makes it clear that this is the go-to for approval-seeking. The return format hints at the possible outcomes (approve/block/modify), which aids in decision-making.

    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 discloses that Hedy may inject context and specifies the return format. It is a read-only poll, so no destructive behavior is expected, and the description covers the key behavioral traits.

    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?

    Three sentences, each adding value: purpose, frequency guidance, and return format. No unnecessary words. Front-loaded with the verb and resource.

    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 no output schema, the description explains the return value well. It addresses polling frequency and context for use. Missing error handling details or consequences of not polling, but overall sufficient for a simple tool.

    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 coverage is 100% for the single optional parameter 'context'. The description does not add extra meaning beyond the schema's brief description. Baseline 3 is appropriate as the schema already documents the parameter well.

    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 it polls for proactive messages from the supervisor, with a specific verb (poll) and resource. It distinguishes from siblings: 'consult_supervisor' might initiate a conversation, 'notify_supervisor' sends messages, but this one only retrieves.

    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 recommends calling every ~5 tool calls and instructs to read a non-null message before continuing. This provides clear context for when to use it, though it doesn't explicitly state when not to use it.

    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 provided, the description carries full burden. It discloses the one-way nature and lack of response, which are key behavioral traits. Missing details like error handling or reliability, but sufficient for a simple notification tool.

    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 two sentences: the first states the purpose, the second provides usage scenarios and behavioral note. It is front-loaded, concise, and every sentence adds value.

    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 simplicity (3 params, 2 required, no output schema), the description covers purpose, usage, and key behavior. It could mention that no return value is sent, but 'does not wait for a response' implies that. No gaps in context for an agent to misuse.

    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 fully documents each parameter. The description does not add new meaning beyond the schema, meeting the baseline score of 3 as per guidelines.

    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 it sends a one-way update to the supervisor, with a specific verb and resource. It distinguishes from siblings by emphasizing one-way communication (vs. consult_supervisor for two-way, get_supervisor_message for retrieval).

    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 lists when to use the tool: after task completion, error, or session end. It also notes that it does not wait for a response, which sets expectations. It does not explicitly state when not to use, but sibling names provide contrast.

    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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