Server Quality Checklist

Tool Scores

• perplexity_searchA
  4.2/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false; the description reinforces them by framing the tool as a pure retrieval operation. It adds value beyond annotations by disclosing 'returns formatted results with no AI synthesis,' which is a meaningful behavioral trait that distinguishes it from perplexity_ask, plus capability disclosures for recency filters and domain restrictions.

  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?

  Four sentences with zero waste: core function first, then use cases, then behavioral trait, then sibling routing. Each sentence earns its place, and the most decision-relevant information (what it returns, what it does not do) is front-loaded.

  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?

  The tool has rich supporting context: an output schema, annotations covering safety (read-only, open-world, non-destructive), and 100% parameter documentation. The description covers purpose, use cases, output format, and one sibling alternative. The only gap is the absence of routing guidance for the other two siblings (perplexity_research, perplexity_reason), which leaves an agent slightly under-informed about the full tool landscape.

  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 already documents all 6 parameters in detail (including the '-' prefix exclusion syntax and enum values). The description only adds marginal confirmation that recency filters and domain restrictions exist, which maps to search_recency_filter and search_domain_filter but adds no new detail beyond the schema. 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?

  States a specific verb and resource: 'Search the web and return a ranked list of results with titles, URLs, snippets, and dates.' It enumerates concrete use cases (finding specific URLs, checking recent news, verifying facts, discovering sources) and explicitly distinguishes itself from perplexity_ask, so an agent can separate it from siblings immediately.

  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 explicit when-to-use context via 'Best for: finding specific URLs, checking recent news, verifying facts, discovering sources' and names one alternative ('For AI-generated answers with citations, use perplexity_ask instead'). However, it never addresses when perplexity_research or perplexity_reason would be preferable, leaving part of the sibling routing to inference.

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

• perplexity_researchA
  4.4/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations by warning that this tool is 'Significantly slower than other tools (can take minutes)' and by noting it returns 'a detailed response with numbered citations.' This gives the agent critical execution-time expectations.

  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 compact and front-loaded, with each sentence earning its place. It covers the core function, use cases, return characteristics, performance caveat, and sibling alternatives without any filler or repetition.

  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?

  The description is complete for an AI agent selecting and invoking the tool: it explains operation, when to use it, performance trade-offs, and the return shape (numbered citations). The presence of an output schema means the structure of the return value does not need to be described here. A small gap is that it does not mention input length or message-format constraints, but the schema already covers the required messages field.

  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%, and the messages parameter is already fully described in the schema as an array of conversation messages. The description's reference to 'a topic' adds only light context and does not substantially enrich the meaning of the messages parameter beyond what the schema provides.

  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 uses a specific verb and resource: 'Conduct deep, multi-source research on a topic' and immediately scopes it with 'Perplexity Agent API, high preset.' It also distinguishes itself from siblings by naming the best-use cases: literature reviews, comprehensive overviews, and investigative queries needing many sources.

  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 guidance with concrete examples: 'Best for: literature reviews, comprehensive overviews, investigative queries needing many sources.' It also gives clear routing instructions by stating when NOT to use it: quick factual questions should use perplexity_ask, and logical analysis should use perplexity_reason.

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

• perplexity_askA
  4.5/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds meaningful behavior beyond that: it is web-grounded, uses the fast preset, returns text with numbered citations, and is the fastest/cheapest option. It doesn't cover rate limits or failure behavior, but the annotation bar is lower and the added details are substantive.

  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 front-loaded with the core purpose and then efficiently packs in use cases, return format, performance characteristics, filtering capabilities, and routing guidance to sibling tools. Every sentence earns its place; there is no filler or 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?

  For a read-only Q&A tool, the description covers purpose, use cases, return shape, filtering options, and explicit alternatives. The input schema fully documents all parameters, annotations cover safety, and an output schema exists, so the description is sufficiently complete for an agent to select and invoke it correctly.

  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 schema covers 100% of parameter descriptions, so the baseline is 3. The description references recency filtering, domain restrictions, and search context size, but it does not add detail beyond what the schema already explains. It adds no new semantic meaning for the parameters.

  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 uses a specific verb ('Answer') and resource ('web-grounded AI / Perplexity Agent API') and clearly positions the tool for quick factual questions, summaries, and general Q&A. It explicitly distinguishes this tool from perplexity_research and perplexity_reason by naming those alternatives and their 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?

  It states exactly when to use this tool ('quick factual questions, summaries, explanations, and general Q&A') and when to use alternatives instead ('in-depth multi-source research' → perplexity_research; 'step-by-step reasoning and analysis' → perplexity_reason). This gives an agent clear routing criteria.

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

• perplexity_reasonA
  4.5/5.0
  Behavior4/5

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

  Annotations already cover read-only and non-destructive behavior. The description adds meaningful context beyond annotations: it returns a reasoned response with numbered citations, uses a medium preset, and mentions web grounding. This transparently sets expectations for the tool's output and behavior.

  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 compact and front-loaded with the core behavior, followed by use cases, return characteristics, filter capabilities, and sibling alternatives. Every sentence serves a purpose, with no filler or 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?

  Given the annotations, full parameter coverage, and output schema, the description provides all essential context: what the tool does, when to use it, what it returns, and how it differs from siblings. Nothing critical is missing for an agent to call it correctly.

  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 input schema already documents all parameters. The description mentions recency, domain restrictions, and search context size, but does not add semantic detail beyond what the schema already provides. Therefore, 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 states a specific verb and resource: 'Analyze a question using step-by-step reasoning with web grounding.' It also differentiates itself from siblings by naming perplexity_ask for quick factual questions and perplexity_research for comprehensive research, so 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 Guidelines5/5

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

  The description explicitly lists when to use this tool: math, logic, comparisons, complex arguments, and chain-of-thought tasks. It also provides clear routing guidance by directing quick factual questions to perplexity_ask and comprehensive multi-source research to perplexity_research.

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