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jnilrac
by jnilrac

Server Quality Checklist

75%
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  • Latest release: v0.1.1

  • Disambiguation4/5

    The tools are distinct in purpose: search for web searching, extract_page for content from known URLs, monitor_diff for change detection, and research_pack as a higher-level combined workflow. While research_pack encompasses search and extraction, it is presented as a different service level, so confusion is minimal.

    Naming Consistency4/5

    Three tools follow a verb_noun pattern (extract_page, monitor_diff, research_pack), with 'research_pack' being noun_noun but still clear. 'search' is a single verb, slightly deviating but acceptable. Overall pattern is recognizable and consistent.

    Tool Count5/5

    Four tools is an appropriate number for a research-focused server, covering key operations (search, extraction, monitoring, and a bundled paid workflow) without being overbearing or insufficient.

    Completeness4/5

    Core research workflows (search, extract, monitor, full research pack) are covered. Minor gaps exist, such as lacking a tool to list or retrieve previous research packs, but the set is functional for autonomous agents.

  • Average 4.1/5 across 4 of 4 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
    • Last stable release on
    • 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. It discloses the payment gate (HTTP 402) and the live API source, providing essential behavioral context beyond the schema.

    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 only two sentences, concise and front-loaded with the core action. Every sentence adds value without 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?

    Given the complexity (payment system, 4 params, output schema), the description covers the payment flow clearly. It could mention the output schema or provide sibling differentiation, but existing output schema mitigates the need.

    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%, so the baseline is 3. The description adds no extra meaning to parameters beyond what's in the schema, such as clarifying 'payment_identifier' as idempotency key.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool performs ranked web search, specifying it's for autonomous agents. However, it does not explicitly distinguish from sibling tools like extract_page or research_pack, which could cause ambiguity.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage context through payment requirements (unpaid vs paid), but lacks explicit guidance on when to use this tool over alternatives like extract_page or research_pack.

    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?

    With no annotations, the description carries full burden. It discloses that blocked/failed pages result in extraction errors, but does not mention whether the operation is read-only, side effects, authentication needs, or rate limits. The tool name 'extract' hints at read behavior, but more explicit disclosure is expected.

    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 three sentences, each earning its place: first sentence states action and outputs, second sentence gives usage guidance, third explains error handling. It is front-loaded and efficient with no wasted words.

    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 that an output schema exists (as per context signals), the description does not need to detail return values, but it does list outputs (content, metadata, links, hashes). It explains error handling adequately. However, it could elaborate on the payment parameters and prerequisites for success.

    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%, so baseline is 3. The description does not add new meaning beyond what the input schema already provides (e.g., URLs with service limits, payment details). The context of extracting content indirectly applies to the 'urls' parameter, but no additional parameter-specific guidance.

    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 extracts 'readable content, metadata, links, and hashes' from page URLs. It distinguishes from siblings by specifying 'when the caller already knows which pages matter', implying it's for targeted extraction rather than search or monitoring.

    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 this tool 'when the caller already knows which pages matter', providing clear context. It also mentions that blocked/failed pages return errors, but does not explicitly state when not to use it or suggest alternatives like search or monitor_diff.

    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?

    Discloses that unpaid calls return x402 payment terms, but lacks other behavioral details like read-only nature, caching, or effects of providing hash vs text. No annotations to assist.

    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?

    Concise paragraph of ~60 words, front-loads the main action, and each sentence adds value. No unnecessary repetition or fluff.

    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 presence of an output schema and 5 parameters, the description covers purpose, usage, and payment behavior. It could mention behavior when no previous state is given, but overall is fairly complete.

    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% so baseline is 3. Description adds minimal extra meaning, only the payment caveat for payment-related params. It does not significantly enhance understanding beyond 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 clearly states the verb 'check' and resource 'whether a page changed', with specific use cases like monitoring docs and pricing pages. It effectively distinguishes from sibling tools which are about extraction, research, and search.

    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?

    Explicitly says 'Use this for lightweight monitoring...' and notes the payment requirement. Provides clear context but does not explicitly list cases to avoid or compare with siblings.

    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 the burden of behavioral disclosure. It reveals the paid nature, the x402 payment requirement, and the retry behavior with normalized results. However, it does not mention error handling or what happens after payment failure, which would be helpful.

    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 concise with three sentences, each contributing distinct value: purpose, usage guidance, and payment behavior. It is front-loaded and contains no redundant information.

    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 complexity (5 parameters, payment workflow) and the existence of an output schema, the description covers the main workflow and payment mechanism adequately. It could elaborate on parameter interactions (e.g., how max_results and extract_pages relate) but is sufficient for an agent to understand the tool's role.

    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%, so parameters are already well-described. The description adds overall context about the workflow (search, extraction, JSON, citations) but does not provide additional details for individual parameters beyond what the schema offers. Baseline 3 is appropriate.

    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 runs a full paid research workflow combining search, extraction, JSON, and citations. It distinguishes itself from raw search results and sibling tools like extract_page and search by emphasizing it produces a compact source-backed packet.

    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?

    The description explicitly advises using this tool when an agent needs a compact research packet rather than raw search results. It also clarifies payment behavior: unpaid calls return x402 terms, paid retries return normalized results. This provides clear context and differentiation.

    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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