Server Quality Checklist

Tool Scores

• ask_perplexity_for_instructionsA
  3.5/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries full responsibility. It mentions that Sonar models have internet access and can search, but does not disclose potential side effects, authentication requirements, rate limits, or what happens on failure. The behavioral disclosure is minimal.

  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 structured with a header, optimization note, examples, and model info. Each section serves a purpose, though it could be slightly more concise. Overall, it is well-organized and front-loaded.

  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 and the presence of an output schema, the description covers basic purpose and examples. However, it lacks details on instruction format, limitations, or additional context needed for safe use. It is adequate but not fully comprehensive.

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

  Parameters2/5

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

  Schema coverage is 0%, yet the description only lists parameter names and a model list without explaining differences or providing validation guidance. It adds minimal value beyond the bare schema, failing to compensate for the lack of 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 sends a prompt to Perplexity and returns step-by-step instructions executable by the AI agent. It distinguishes itself from siblings like 'ask_perplexity' and 'ask_perplexity_exact_response' by emphasizing multi-step tasks and executable format.

  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 examples of good prompts (e.g., 'How do I set up a Python FastAPI project with Docker?') and states the tool is optimized for multi-step tasks. It does not explicitly exclude scenarios or name alternatives, but the context makes usage clear.

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

• ask_perplexityA
  4/5.0
  Behavior3/5

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

  Mentions that Sonar models have internet access and can perform searches, which is a behavioral trait. However, no annotations are provided, and the description does not disclose potential side effects, rate limits, or output format beyond what is implied.

  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 bullet points and examples. Every sentence adds value. 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 the tool's complexity (query tool), the description adequately covers usage, parameters, and model selection. The existence of an output schema means return values are handled externally. Minor gaps: no mention of required authentication or rate limits.

  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 0%, so the description must compensate. It lists model options and their uses in the guidelines, but does not explain the 'prompt' parameter's semantics beyond being a prompt/question. The baseline is 3 with 2 parameters.

  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 verb 'send a prompt to Perplexity' and the resource 'return the response'. It provides specific use cases (research, current events, comparisons) but does not directly differentiate from siblings like ask_perplexity_exact_response.

  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?

  Explicitly states when to use the tool (research, current events, comparisons) and when to use alternative models (reasoning for complex tasks, research by default). Provides examples of good prompts.

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

• ask_perplexity_to_learnA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It mentions internet access and a teaching approach but does not disclose whether the tool is read-only, has rate limits, or any restrictions. As a learning tool, it is likely read-only, but this is not explicitly stated.

  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 structured with bullet points and examples, making it easy to scan. It front-loads the purpose. While slightly lengthy, every sentence adds value, earning a score of 4.

  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 presence of an output schema, the description does not need to detail return values. It covers purpose, usage guidelines, parameter semantics, and even mentions internet access. For a learning tool, it is comprehensive and well-rounded.

  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?

  With 0% schema description coverage, the description adds significant meaning by explaining the 'prompt' parameter as the question and the 'model' parameter as a Sonar model with options listed. It provides helpful context beyond the schema's basic type information.

  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: 'Learn complex concepts with pedagogical explanations, examples, and analogies.' It uses a specific verb ('learn') and resource ('concepts'), and distinguishes itself from siblings like 'ask_perplexity' by emphasizing a teaching-optimized approach.

  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 clear context for when to use this tool, stating it is 'teaching-optimized' and listing examples of good prompts. However, it does not explicitly state when not to use it or mention alternative tools, but the implied usage is clear enough for an agent.

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

• get_examplesA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It states the return value but does not disclose whether the tool is read-only, has side effects, or requires authentication. Adequate but minimal.

  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?

  Three concise sentences with no redundancies. The first sentence states the core purpose, the second adds context, and the third documents the return value. Well-structured and 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?

  For a simple tool with no parameters and no annotations, the description adequately explains what the tool does and returns. It could mention safety or that it's a discovery tool, but it is largely complete given the low complexity.

  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 baseline score is 4. The description appropriately adds no parameter information, as none is needed.

  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 retrieves examples of prompts and usage patterns, distinguishing it from sibling tools that all perform queries. The verb 'Get' and resource 'examples' are specific and 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 implies use when seeking examples or guidance on using the server. While it doesn't explicitly state when not to use it or provide alternatives, the context is clear given sibling tool names.

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

• ask_perplexity_exact_responseA
  4.3/5.0
  Behavior4/5

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

  Discloses that the tool returns exact response without metadata and that Sonar models have internet access. With no annotations, the description adequately covers behavioral traits.

  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?

  Front-loaded with purpose, followed by usage guidance and examples. Concise but comprehensive; every sentence adds 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 an output schema exists and the tool is straightforward, the description covers purpose, usage, parameters, and model choices adequately.

  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?

  With 0% schema description coverage, the description adds value by listing model options and providing example prompts. For the 'model' parameter, it enumerates valid values beyond 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?

  Description clearly states the tool sends a prompt to Perplexity and returns the exact response unchanged. It distinguishes itself from siblings by emphasizing raw output with no modifications.

  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 guidance ('Use this when you need the raw, unmodified output') and examples. Mentions alternative models for different tasks, but does not explicitly name sibling tools.

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