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Send prompts to multiple AIs in parallel and receive a synthesized comparison with agreements, differences, and recommended actions.

Instructions

Send a prompt to multiple AIs in parallel and receive a synthesized comparison. Attach a file to have all selected AIs analyse the same document, image, or code. Perplexity only receives text/code files; images and PDFs are forwarded to Claude and OpenAI only. When synthesize is true (the default), Claude produces a final summary identifying agreements, differences, and a recommended course of action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoOptional path to a local file. Absolute paths are recommended to avoid ambiguity about the working directory. Supported types: code and text files (.js .ts .php .py .md .json etc.), images (.png .jpg .jpeg .gif .webp), and PDFs (.pdf). Example: /Users/you/project/auth.php
promptYesThe question or instruction sent to all selected AIs.
systemNoOptional system prompt applied to all targets.
targetsNoWhich AIs to query. Defaults to ['claude', 'codex'] if omitted.
synthesizeNoWhen true (default), Claude synthesizes all responses into one summary. When false, each AI's raw response is returned side by side.
Behavior4/5

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

With no annotations, the description carries full burden for behavioral disclosure. It details parallel execution, file handling differences between AIs, and the synthesize feature (default true, role of Claude). Missing info on rate limits, cost, or error handling, but the core behaviors are adequately transparent.

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 no fluff. First sentence defines core purpose, then file attachment, then a critical limitation, then synthesis behavior. Each sentence adds distinct value and the structure is 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?

The description covers main features but omits details about the return format (beyond 'synthesized comparison' or 'raw responses') and error handling (e.g., if one AI fails). With no output schema, more details on the response structure would improve completeness 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%, so baseline is 3. The description adds value by explaining file type restrictions for Perplexity, default values for targets and synthesize, and the effect of synthesize=true/false. This goes beyond the schema's basic property 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 it sends a prompt to multiple AIs in parallel and synthesizes a comparison. It distinguishes from sibling tools (ask_claude, ask_codex, ask_perplexity) which are single-AI tools, making the purpose specific and well-defined.

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 parallel AI querying and synthesis) and provides a key exclusion (Perplexity cannot handle images/PDFs). It implicitly suggests using sibling tools for single-AI queries, though it could be more explicit about alternatives.

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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