Server Quality Checklist

Tool Scores

• lyn_usageA
  4.2/5.0
  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 explicitly states 'Free; no side effects,' which discloses that the operation is read-only and has no impact. This is meaningful behavioral context beyond the verb 'check.' It does not describe the return format, but for a balance check this is arguably self-evident. The disclosure is sufficient for the tool's simplicity.

  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 core purpose, then usage context, then a side-effect note. Every clause earns its place. No fluff, no repetition. The structure is optimal for an agent to quickly understand the tool's function and applicability.

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

  Completeness5/5

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

  For a zero-parameter, no-output-schema tool, the description is complete. It covers purpose, when to use, and that it is free and side-effect-free. There are no prerequisites, permissions, or return-value details needed beyond what is obvious. The description fully equips an agent to decide whether and how to invoke it correctly.

  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 description does not need to explain any. The schema has no properties, and the description adds no parameter-specific meaning. Baseline for 0 params is 4, and there is nothing else to say. The description's focus on usage and side effects is appropriate; no parameters to document.

  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 action ('Check the Lynceus account's remaining credits') with a specific verb and resource. It aligns with the title. However, it does not explicitly differentiate from sibling tools (lyn_search, lyn_extract), though the resource and verb make it distinct. It could have named the siblings, but the purpose is 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 gives clear context for when to use the tool: 'when the user asks about balance/credits' and 'after a 402 insufficient_credits error to confirm the situation.' It does not explicitly mention alternatives or exclusions, but the use cases are specific and the 'Free; no side effects' note provides additional guidance. No need to exclude since the operation is standalone.

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

• lyn_searchA
  4.9/5.0
  Behavior5/5

  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 returns (numbered list with rank, title, URL, snippet, published date), snippet brevity and the need to call lyn_extract for full text, cost (1 credit per request, cached results still return data), and failure modes with handling (engine_upstream retry once, 401 bad key, 402 out of credits). This is comprehensive for a read-only search 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 organized into logical sections (WHEN TO USE, ARGUMENTS, COST, RETURNS, FAILURES) with front-loaded purpose and usage. Every sentence carries information, and it avoids redundancy with the schema. Despite being longer than average, the structure makes it efficient to parse.

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

  Completeness5/5

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

  Given the tool's complexity (3 parameters, no output schema, no annotations), the description covers all essential aspects: purpose, usage context, parameter semantics, return format, errors, cost, and sibling relationship. Nothing an agent needs to call it correctly is missing. The output format is described even without an output schema.

  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 meaningful guidance beyond the schema: query suggests 1–10 words, natural language is fine, and locale alignment; freshness explains enum meanings with defaults; max_results gives a default. It also explains expected behavior with omitted optional parameters. This elevates it above baseline.

  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 ('Search the live web via Lynceus'), a resource (Lynceus with Yandex index), and explicitly distinguishes from the sibling lyn_extract ('Use BEFORE lyn_extract when you don't yet have the URLs'). It also notes its RU-web-first strength, clarifying its niche versus Google-based tools. This fully differentiates it.

  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?

  There is a dedicated 'WHEN TO USE' section that lists concrete scenarios (news, docs, prices, people, Russian sites) and explicitly names the alternative tool and the condition that selects it ('Use BEFORE lyn_extract when you don't yet have the URLs'). It also mentions when NOT to use it implicitly by pointing to lyn_extract for full-text extraction.

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

• lyn_extractA
  5/5.0
  Behavior5/5

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

  No annotations are present, so the description carries the full burden. It discloses the 4-tier anti-bot ladder, cost model (1 credit per success, 25 for captcha, free cache hits, no charge for failures), and response statuses (needs_browser/skipped_browser/error). It also explains that per-URL errors don't fail the batch, providing complete behavioral clarity.

  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?

  While long, the description is highly structured with clear section headers (WHEN TO USE, ARGUMENTS, COST, RETURNS, FAILURES) and every sentence carries useful information. It's front-loaded with purpose and usage, then details arguments, cost, and failure modes in a logical order without redundancy.

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

  Completeness5/5

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

  This is a complex tool with 4 parameters, cost implications, and a multi-tier process. The description covers everything needed for correct invocation: parameter semantics, cost rules, return format, retry logic (needs_browser), and error handling. Without an output schema, it fully explains the response structure. Nothing essential is missing.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema coverage is 100%, but the description adds significant extra semantics: batching URLs is cheaper/faster, allow_browser is only for JS-rendered pages and costs time, allow_captcha requires explicit user consent and premium cost, and format differences (markdown keeps links vs plain text). This goes well beyond the schema 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 fetches web pages and returns clean Markdown, with a specific verb+resource. It also distinguishes from naive fetchers and siblings (lyn_search is search, lyn_usage is usage) by focusing on content extraction. The mention of handling 403/paywall-shell pages adds specificity that sets it apart.

  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 'WHEN TO USE' section explicitly says to use this when you have URLs and need actual text, and directly says 'Prefer this over your built-in fetch' with reasons. It provides clear conditions for toggling allow_browser (only when needs_browser/skipped_browser) and allow_captcha (with user consent), leaving no ambiguity about when to select this tool.

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