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0xHumban

perplexity-mcp

by 0xHumban

ask_perplexity_exact_response

Send a prompt to Perplexity and receive the raw, unmodified output. Use for exact documentation, API responses, or research without added metadata.

Instructions

Send a prompt to Perplexity and return the exact response without 
changing anything (no additional metadata or suggestions).

Use this when you need the raw, unmodified output from Perplexity.

Examples of good prompts:
- "Give me the exact documentation for FastAPI's dependency injection"
- "What does the official Next.js docs say about App Router?"
- "Show me the raw API response format for OpenAI's latest models"

If the user wants to execute or learn complex tasks, use the reasoning model (sonar-reasoning)
If the user wants development work requiring real-time documentation lookup, research-intensive coding, use the reasoning model (sonar-reasoning).
But by default, use the research model (sonar).

Sonar models have internet access and can perform searches.

Args:
    prompt: The prompt/question to send
    model: Sonar model (sonar, sonar-pro, sonar-deep-research, 
           sonar-reasoning, sonar-reasoning-pro)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
modelNosonar

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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.

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