Server Quality Checklist

Tool Scores

• wiki_parserA
  4.4/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the full transparency burden and performs well: it discloses behaviors like optional file saving, caching for repeated requests, custom depth control, and nested section extraction. It does not cover error handling or authentication requirements, but the core behavioral traits are transparently described.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is long but well-structured with clear sections: overview, when-to-use, key features, and usage examples. It is front-loaded with the core purpose and every section adds value for a tool with 7 parameters, though some repetition exists across sections.

  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 relatively complex tool with no output schema and no annotations, the description covers actions, parameters, examples, and even file-saving behavior. It lacks explicit return-value descriptions, but given the detailed examples and 100% schema coverage, it is substantially complete.

  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%, and the description adds substantial semantics beyond the schema. Usage examples clarify the 'Chapter##Section' format, per-chapter depth overrides via chapterDepths, saveToFile options, and how action values map to behavior, making parameter usage significantly easier to understand.

  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 parses and extracts content from DeepWiki documentation pages, with specific verbs ('navigate', 'extract', 'structure') and a defined resource. It distinguishes itself from the sibling wiki_question tool by focusing on structured extraction rather than Q&A.

  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 a clear 'When to use this tool' list with concrete scenarios like getting a table of contents, extracting chapters, and gathering offline documentation. It does not explicitly mention alternatives or when not to use it, but the usage contexts are specific and helpful.

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

• wiki_questionA
  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 fully discloses behavioral traits. It explains that deep research takes 3-15 minutes, that referencesNumbers returns exact snippets while contextFiles returns full files, that goDeeper creates a new queryId, and that includeFullConversation controls response visibility. These details go far beyond basic operation and set clear expectations.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness4/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is long but well-structured with clear section headings, bullet points, and numbered examples. It is front-loaded with purpose and usage context. Some redundancy exists between 'Key features,' 'Usage examples,' and 'Important notes,' but each section adds value for a complex 16-parameter tool.

  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 complexity (16 parameters, nested objects, no output schema, no annotations), the description is remarkably complete. It covers all major workflows, warns about pitfalls, explains response-related parameters, and includes guidance on background execution for long operations. It leaves little ambiguity about how to invoke the tool correctly.

  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?

  Although schema coverage is 100%, the description adds substantial meaning beyond field descriptions. It clarifies the semantic difference between referencesNumbers and contextFiles, explains the behavior of includeFullConversation with defaults, and provides concrete examples for parameters like contextRanges, saveToFile, and followUpQuestion. This is far more than a restatement of the 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 tool's purpose: 'asking questions about GitHub repositories using DeepWiki's AI analysis.' It also explicitly lists capabilities like retrieving code snippets and file contents, and differentiates from the sibling tool by focusing on Q&A and deep analysis rather than parsing.

  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 provides explicit 'When to use this tool' scenarios, a critical distinction between using queryId vs new question, and multiple usage examples covering key parameter combinations. It also warns against asking for specific files in new questions, giving clear guidance on when not to use certain approaches.

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