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kev489
by kev489

gpt_search_batch

Run multiple ChatGPT search/research prompts in parallel, each in its own tab, and collect responses into a single summary.

Instructions

Run multiple ChatGPT search/research prompts concurrently.

Each request opens its own ChatGPT tab and runs concurrently with the others. This is the text equivalent of gpt_image_gen_batch. The server runs at most 3 ChatGPT tabs at a time across all calls and sessions; larger batches queue internally.

Each item in requests is a dict with keys:

  • query (optional, str): the full prompt

  • prompt_file (optional, str): path to a text file containing the prompt

  • output_file (optional, str): path where the cleaned markdown response should be saved

  • label (optional, str): heading used for this item in the combined response; defaults to output_file, then prompt_file, then request_<n>

  • return_output (optional, bool): overrides the batch-level return_output for this item

  • output_json (optional, bool): overrides the batch-level output_json for this item

Provide either query or prompt_file for each item. Relative file paths resolve from the MCP server process working directory.

return_output is batch-level unless overridden per item. When omitted, each item defaults to returning the full output only if it has no output_file. output_json is batch-level unless overridden per item. When enabled, the response is parsed/repaired after ChatGPT returns. For file outputs, raw output is saved first and overwritten only when JSON post-processing succeeds.

Returns a markdown summary. If outputs are returned, they are grouped under per-request headings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestsYes
return_outputNo
output_jsonNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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.

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