Server Quality Checklist

Tool Scores

• search_redditA
  3.9/5.0
  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 queries run in parallel, results are capped at 10 per query, and all queries auto-add 'site:reddit.com'. It also describes the search operators and pro tips. However, it does not mention rate limits, authentication requirements, or response structure. Given the absence of annotations, it provides moderate transparency but could be more explicit about non-mutating behavior.

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

  Conciseness2/5

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

  The description is excessively long (over 500 words) with repeated emphasis on '10+ queries' and multiple examples. While well-structured with headings and bullet points, it could be significantly trimmed without losing essential guidance. The pro tips and workflow section, though helpful, contribute to verbosity. A more concise version would retain all critical information.

  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?

  Given the tool's complexity (multi-query, parallel execution) and the absence of an output schema, the description should explain return format, pagination, and how to access full post content. It mentions 'results' and budget (10 per query) but omits details on result structure, potential errors, or empty results. The workflow hints at get_reddit_post for details, partially compensating. Overall, it is adequate but not fully 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% with a detailed description for 'queries' including min/max items and a checklist. The tool description adds substantial value by introducing the 10-category query formula, examples, and the concept of consensus analysis. This goes far beyond the schema's static description. The second parameter 'date_after' is simple but adequately described.

  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 identifies the tool as a Reddit search for consensus analysis, with a specific verb ('search') and resource ('Reddit'). It distinguishes from siblings like 'web_search' (general web) and 'get_reddit_post' (single post retrieval) by emphasizing multi-query diversity and consensus detection. The title and description together make the purpose unmistakable.

  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 explicit usage context: it is intended for multi-angle consensus analysis with a minimum of 10 queries. It includes a workflow (search_reddit → sequentialthinking → get_reddit_post or search again) and contrasts with alternatives (e.g., deep_research). However, it does not explicitly state when not to use the tool (e.g., for a single simple query), but the strong emphasis on 10+ queries implies it.

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

• web_searchA
  4.1/5.0
  Behavior4/5

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

  With no annotations, the description carries full burden. It discloses parallel execution, results budget (10 per keyword), critical workflow step (must scrape after search), and that search results are not full content. It does not mention rate limits or authentication, but given the read-only nature, this is adequate.

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

  Conciseness3/5

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

  The description is well-structured with headings, emojis, and examples, but it is overly verbose. Key points like minimum keywords are repeated multiple times. While front-loaded, the length could be reduced without losing 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 one parameter with full schema coverage and no output schema, the description compensates by explaining the workflow and the need to scrape for full content. It implies the output is a list of results with URLs, but does not formally describe the response structure. This is adequate for practical use.

  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?

  The schema describes the 'keywords' parameter with min and max ranges. The description adds substantial value: minimum 3 keywords, recommended 5-7, 7-perspective formula, search operators, and use-case-specific patterns. This far exceeds the schema's description.

  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: 'searches up to 100 keywords IN PARALLEL via Google'. It provides specific verb (search), resource (Google), and scope (parallel keywords). The extensive examples further clarify its function.

  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 provides detailed usage guidelines, including minimum keywords, keyword formulas, and search operators. However, it lacks explicit guidance on when to use this tool versus sibling tools like deep_research or search_reddit, which is a gap in tool selection advice.

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

• get_reddit_postA
  4.3/5.0
  Behavior4/5

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

  The description discloses the comment budget (1000 total), automatic allocation formula, and effects of parameters like max_comments and use_llm. It mentions cost and requirement for use_llm and recommends fetch_comments=true. However, it does not address rate limits, error handling, authentication, or how input validation works (e.g., invalid URLs). Since no annotations exist, the description carries the burden, but it is still largely transparent.

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

  Conciseness3/5

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

  The description is structured with bold headings, bullet points, and an example section, making it scannable. However, it is quite long (multiple paragraphs, pro tips, trade-off tables). While every sentence adds value, the overall length could be trimmed for conciseness without losing essential information.

  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?

  Given the 5 parameters and no output schema, the description thoroughly explains input behavior and workflow. However, it does not describe the output structure or return format (e.g., what fields the response contains). An agent would need to infer or assume the output shape. This is a notable gap, especially with no output schema provided.

  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 description coverage is 100%. The description adds value beyond the schema by explaining the automatic calculation for max_comments, cost and environment variable for use_llm, and extraction instructions for what_to_extract. It also provides examples for what_to_extract and pro tips for fetch_comments. This enriches the agent's understanding without redundancy.

  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 fetches Reddit posts with smart comment allocation, specifying the resource (Reddit posts) and action (fetch). It distinguishes itself from sibling tools like search_reddit, which finds posts, and deep_research, web_search, scrape_links, which serve different purposes.

  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 guidance on when to use different post counts (2-5 for deep dive, 10-15 balanced, 20-30 recommended, 40-50 controversial), trade-offs, and workflow linking to search_reddit. It also gives a concrete example of a bad vs good URL array, and pro tips for subreddit mixing.

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

• scrape_linksA
  4.3/5.0
  Behavior4/5

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

  With no annotations, the description carries full burden. It discloses two operational modes, token budget, cost estimation, automatic fallback process, and batching limit. However, it does not mention error handling or rate limiting behavior.

  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 headers, lists, and bold keywords. Front-loaded with essential info. Some repetition of 'ALWAYS use_llm=true' could be trimmed. Overall, every section earns its place.

  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 (two modes, token budget, extraction prompts), the description covers all critical aspects: parameters, usage scenarios, best practices, and fallback. Without an output schema, it could detail return structure more, but the provided information is sufficient for an agent.

  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%, baseline 3. Description adds substantial value by explaining the use_llm mode, recommended URL counts, extraction prompt structure, and templates. This goes beyond the schema's description fields.

  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: scraping 1-50 URLs with two modes (basic and AI-powered). It distinguishes itself from sibling tools like web_search and deep_research by focusing on extracting content from specific URLs rather than searching or deep research.

  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?

  Extensive usage guidance is provided: recommendation of 3-5 URLs, token budget breakdown, extraction templates, and examples of good vs bad usage. However, it lacks explicit instructions on when not to use this tool compared to siblings (e.g., when web_search is more appropriate).

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

• deep_researchA
  4.7/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It explains parallel execution, token distribution (32,000 total), and mandatory file attachments for code questions. However, it does not explicitly state failure modes (e.g., token overrun) or output format, leaving minor gaps.

  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-organized with headings, bullet points, examples, and pro tips. It front-loads key information (parallelism, recommended count) and uses markdown for clarity. Some redundancy (e.g., 'recommended 5+' is repeated) could be trimmed without losing substance.

  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 (batch research, token budget, file attachments) and absence of output schema, the description covers input format, usage constraints, file attachment requirements, and iterative workflow. It includes comprehensive examples and edge cases (e.g., scope expansion triggers).

  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%, so baseline is 3, but the description vastly enriches the single parameter (questions) with a detailed structured template, examples, file attachment guidance, and token allocation details. This goes well beyond the schema's own description.

  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 2-10 questions in parallel for AI-powered research, and distinguishes it from sibling tools (e.g., web_search, search_reddit) by focusing on multi-perspective, batch research with a shared token budget.

  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 scenarios (multi-perspective analysis, domain research from multiple angles) and includes a suggested workflow with sequentialthinking. It also gives concrete examples and recommends 5-10 questions to maximize capacity.

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