Server Quality Checklist

Tool Scores

• gpt_searchA
  4.4/5.0
  Behavior5/5

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

  With no annotations, the description fully discloses behavioral traits: it sends the prompt to ChatGPT, handles file output with directory creation, details the return_output default logic, and explains output_json post-processing including failure recovery. This is comprehensive and leaves no ambiguity.

  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 well-structured with an Args section and clear parameter explanations. It is informative without being verbose, though some redundancy (e.g., repeating 'default') could be trimmed. Overall, it effectively communicates key details.

  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 (5 parameters, multiple behaviors) and the presence of an output schema (not shown but indicated), the description covers all necessary aspects: parameter usage, alternatives, defaults, and error handling. It is complete enough for an AI agent to use 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?

  The input schema has 0% description coverage, so the description carries full burden. It provides detailed semantics for all five parameters: query vs. prompt_file exclusivity, output_file with directory creation, return_output defaulting based on output_file, and output_json with fallback behavior. This significantly enhances schema understanding.

  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 'Search the web or research a topic using ChatGPT.' It identifies the tool's core function and the two input options (query or prompt_file). However, it does not explicitly differentiate from sibling tools like gpt_image_gen or gpt_search_batch, though the purpose is distinct enough.

  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 guidance on parameter usage: 'Provide either `query` or `prompt_file`.' It also explains default behaviors for return_output and output_json. It lacks explicit when-to-use vs. alternatives, but the context from sibling names and the detailed parameter logic compensates.

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

• gpt_image_genA
  4.6/5.0
  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: serialization behavior, default save directory, naming conventions for single vs. multiple images, and the effect of embed_images on the response. No contradictions.

  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 well-structured with separate paragraphs for the core function, usage notes, and parameter details. It is reasonably concise, though the details about naming and defaults could be slightly more streamlined.

  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 zero schema coverage, no output schema, and no annotations, the description covers the return format (list of MCP content blocks, text summary, optional images) and key defaults. It lacks explicit output schema, but the qualitative description suffices for the agent.

  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 description coverage is 0%, so the description must add meaning for all parameters. It explains prompt, filename_prefix (default naming), save_dir (default location), and embed_images (response impact). This fully compensates 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's purpose: 'Generate one or more images via ChatGPT image gen and save them to disk.' It uses a specific verb and resource, and distinguishes from the sibling tool gpt_image_gen_batch which handles parallel distinct prompts.

  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 guidance on when to use this tool versus gpt_image_gen_batch (parallel runs), and notes that sequential calls via the MCP harness are serial. However, it does not explicitly contrast with the search siblings.

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

• gpt_search_batchA
  4.7/5.0
  Behavior5/5

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

  No annotations are provided, so the description carries full burden. It thoroughly discloses concurrency behavior, internal queuing, server limits, file path resolution, output handling, and JSON post-processing with potential overwriting, providing comprehensive behavioral transparency.

  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 front-loaded with the primary purpose and is dense with necessary information, but it is somewhat lengthy. Every sentence adds value, but slight trimming could improve conciseness.

  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 processing, multiple optional overrides, concurrency limits, file handling), the description covers all essential aspects. The output schema exists but the description still explains the return format (markdown summary with per-request headings), leaving no obvious gaps.

  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 input schema has 0% description coverage, but the description fully compensates by explaining each key in the requests array (query, prompt_file, output_file, label, return_output, output_json) with defaults and behavior, and also clarifies batch-level overrides.

  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 'Run multiple ChatGPT search/research prompts concurrently,' specifying the verb, resource, and concurrency aspect, and distinguishes from siblings by mentioning gpt_image_gen_batch as the image equivalent and implying it's the batch version of gpt_search.

  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 explains when to use this tool (for concurrent batch requests) and notes the server limit of 3 tabs. It indirectly suggests using gpt_search for single queries by naming the sibling, but does not explicitly state when not to use this tool.

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

• gpt_image_gen_batchA
  4.9/5.0
  Behavior5/5

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

  Discloses key behaviors: concurrent execution capped at 3 tabs, queueing for larger batches, error handling (one fails, others complete), rate limit caveat, and batch-level embed_images behavior. No annotations provided, but description fully compensates.

  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?

  Description is informative without being overly verbose. Each sentence adds value, though slightly longer than necessary. Front-loaded with purpose and key usage note.

  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 no output schema and two parameters, the description covers all necessary context: parallel execution details, error behavior, return format (list of MCP content blocks with text and optionally images), and rate limits.

  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?

  Adds rich meaning beyond schema: details each key in requests (prompt, filename_prefix, save_dir) and the batch-level embed_images flag. Schema coverage was 0%, so description carries full burden and executes well.

  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: 'Run multiple image-gen prompts in parallel via ChatGPT image gen.' It distinguishes from sibling gpt_image_gen by explaining parallelization advantage over serialized calls.

  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 says when to use this tool over alternatives: 'This is how you actually parallelize image gen... issuing multiple separate gpt_image_gen tool calls... gets serialized... gpt_image_gen_batch fans out internally.' Also advises setting embed_images to false during long loops.

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