Perplexity API Platform MCP Server

Advanced Reasoning

perplexity_reason

Read-only

Answers complex questions with step-by-step reasoning and web-grounded evidence, including math, logic, and comparisons. Returns numbered citations with optional recency and domain filters.

Instructions

Analyze a question using step-by-step reasoning with web grounding (Perplexity Agent API, medium preset). Best for: math, logic, comparisons, complex arguments, and tasks requiring chain-of-thought. Returns a reasoned response with numbered citations. Supports filtering by recency (hour/day/week/month/year), domain restrictions, and search context size. For quick factual questions, use perplexity_ask instead. For comprehensive multi-source research, use perplexity_research instead.

Input Schema

TableJSON Schema

Name	Required	Description
`messages`	Yes	Array of conversation messages
`search_context_size`	No	Controls how much web context is retrieved. 'low' is fastest, 'high' provides more comprehensive results.
`search_domain_filter`	No	Restrict search results to specific domains (e.g., ['wikipedia.org', 'arxiv.org']). Use '-' prefix for exclusion (e.g., ['-reddit.com']).
`search_recency_filter`	No	Filter search results by recency. Use 'hour' for very recent news, 'day' for today's updates, 'week' for this week, etc.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`response`	Yes	AI-generated text response with numbered citation references

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.2.1Sep 8, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already cover read-only and non-destructive behavior. The description adds meaningful context beyond annotations: it returns a reasoned response with numbered citations, uses a medium preset, and mentions web grounding. This transparently sets expectations for the tool's output and behavior.

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 compact and front-loaded with the core behavior, followed by use cases, return characteristics, filter capabilities, and sibling alternatives. Every sentence serves a purpose, with no filler or redundancy.

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 annotations, full parameter coverage, and output schema, the description provides all essential context: what the tool does, when to use it, what it returns, and how it differs from siblings. Nothing critical is missing for an agent to call it correctly.

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 100%, so the input schema already documents all parameters. The description mentions recency, domain restrictions, and search context size, but does not add semantic detail beyond what the schema already provides. Therefore, baseline 3 is appropriate.

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 states a specific verb and resource: 'Analyze a question using step-by-step reasoning with web grounding.' It also differentiates itself from siblings by naming perplexity_ask for quick factual questions and perplexity_research for comprehensive research, so the purpose is unambiguous.

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?

The description explicitly lists when to use this tool: math, logic, comparisons, complex arguments, and chain-of-thought tasks. It also provides clear routing guidance by directing quick factual questions to perplexity_ask and comprehensive multi-source research to perplexity_research.

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

Install Server