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perplexity_chat: GET /

hasdata_perplexity_chat_getPerplexityAnswer
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Get Perplexity Answer

Sends a question to Perplexity anonymously and returns the answer as markdown text, plus every web page Perplexity searched in sources with its domain, title, snippet, publishedDate and citation number, the follow-up questions in relatedQuestions, a link to the thread on perplexity.ai in url, the model that answered and the country the answer was served from. Perplexity searches the web for every question, so usedWebSearch is normally true and answers come with sources rather than from the model alone. The language of the answer follows the question — write it in the language you want back, or ask for one in it. Set the frame of reference for words like "today" with timezone. Each request is a fresh thread with no memory of earlier ones. Use to answer questions with cited sources, to ground an agent or RAG pipeline in live web results, to summarise a topic with the pages behind it, or to build datasets of answers and their citations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesThe question to send to Perplexity, up to 4000 characters. The question also decides the language of the answer — write it in the language you want back, or ask for one explicitly ("Antworte auf Deutsch").
timezoneNoIANA time zone name, for example `America/Chicago` or `Europe/Berlin`. It sets what Perplexity treats as the current date and time, which matters for questions that say "today", "this week" or "latest".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only cover the safety profile (readOnly/openWorld/non-destructive); the description adds genuinely new behaviour — it runs anonymously, always performs a web search (usedWebSearch normally true), returns sources instead of relying on the model alone, each request is a fresh thread with no memory, and answer language follows the prompt. That is substantial context beyond structured fields.

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 the core action and return shape, and every sentence carries information — the enumeration of return fields is justified because there is no output schema. The single dense paragraph is slightly run-on but not padded.

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?

With no output schema, the description compensates by naming every returned field and its structure, and it covers statelessness, language behaviour and timezone framing. An agent has everything needed to invoke and interpret the tool 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 both parameters are already fully documented in the schema, and the description largely restates them (language follows the question, timezone sets the current date for 'today'/'latest'). No additional syntax or edge-case meaning is added, so the baseline 3 applies.

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?

States a specific verb and resource — sends a question to Perplexity and returns the answer plus sources, related questions, thread URL, model and country. It clearly distinguishes itself from the many SERP/scraper siblings by naming Perplexity and its cited-source output.

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 use cases (cited Q&A, grounding an agent/RAG pipeline in live web results, topic summarisation with sources, building answer+citation datasets) plus the statelessness caveat. It stops short of naming a sibling alternative (e.g. chatgpt_chat_getChatgptAnswer) or when-not-to-use conditions, so 4 rather than 5.

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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