Perplexity Deep Research MCP
Provides web search capabilities using Perplexity's chat completions API with the sonar-deep-research model, allowing AI assistants to perform detailed web searches with optional recency filtering.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Perplexity Deep Research MCPfind recent studies on quantum computing breakthroughs"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Perplexity Deep Research MCP
A Model Context Protocol (MCP) server for Perplexity AI API with web search capabilities. This server allows AI assistants to perform web searches using Perplexity's chat completions API with the sonar-deep-research model.
Features
Provides a
deep_researchtool for AI assistants to perform web searchesUses Perplexity's chat completions API with the sonar-deep-research model
Related MCP server: Perplexity MCP Server
Tool: deep_research
The server provides a deep_research tool with the following input parameters:
query(required): The search query to performsearch_recency_filter(optional): Filter search results by recency (options: month, week, day, hour)
Configuration
Environment Variables
PERPLEXITY_API_KEY: Your Perplexity API key (required)
Response Format
The response from the deep_research tool includes:
Content of search results
Citations for the information
Installing via Smithery
To install perplexity-deep-research for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install @arjunkmrm/perplexity-deep-research --client claudeLicense
MIT
Available Tools
1 tooldeep_researchB
Perform a comprehensive web search using Perplexity's Deep Research API, which provides detailed and contextually relevant results with citations.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query to perform | |
| search_recency_filter | No | Filter search results by recency (options: month, week, day, hour) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool provides 'detailed and contextually relevant results with citations,' which hints at output behavior, but lacks critical details like rate limits, authentication requirements, error handling, or whether it's a read-only operation. For a web search tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function and key features without unnecessary words. It is appropriately sized and front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (web search with two parameters) and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on output format, error cases, or behavioral constraints. With no annotations and incomplete behavioral transparency, it meets the minimum viable threshold but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, clearly documenting both parameters. The description does not add any meaning beyond what the schema provides—it doesn't explain parameter interactions, default behaviors, or usage examples. Baseline 3 is appropriate when the schema does all the work.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a 'comprehensive web search' using a specific API (Perplexity's Deep Research API), which is a specific verb+resource combination. However, with no sibling tools mentioned, there's no opportunity to distinguish from alternatives, so it cannot achieve a perfect score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or specific contexts. It mentions the API provides 'detailed and contextually relevant results with citations,' but this is a feature description rather than usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v1.0.0- First observed
deep_research
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'deep_research' has a clearly distinct and singular purpose, making it impossible for an agent to misselect among alternatives.
A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The name 'deep_research' follows a clear and descriptive pattern without any deviations or mixed conventions.
A single tool is generally too few for most server purposes, as it limits functionality and flexibility. While the tool is well-described for deep web research, the server's scope feels thin and underdeveloped, which could hinder agent workflows that require more granular operations.
The server is severely incomplete for a research domain, as it only offers a single broad search tool. There are obvious gaps, such as no tools for filtering results, managing queries, accessing specific sources, or performing follow-up actions, which will likely cause agent failures in complex research tasks.
Related MCP Connectors
Enable AI assistants to perform web searches using Perplexity's Sonar Pro.
Real-time web search, reasoning, and research through Perplexity's API
Provides AI assistants with access to Seltz's powerful Web Search capabilities.
The best web search for your AI Agent
Related MCP Servers
- AlicenseBqualityFmaintenanceA server facilitating web search functionality by utilizing Perplexity AI's API, designed to integrate with the Claude desktop client for enhanced search queries.1308MIT
- AlicenseBqualityDmaintenanceThis server allows users to perform web searches using Perplexity AI, providing a tool for retrieving search results through a simple API interface.1753Apache 2.0
- AlicenseNot gradedqualityCmaintenanceHigh-performance server enabling AI assistants to access web scraping, crawling, and deep research capabilities through Model Context Protocol.29MIT
- AlicenseNot gradedqualityDmaintenanceProvides AI-powered search, research, and reasoning capabilities through integration with Perplexity.ai, offering three specialized tools: general conversational AI, deep research with citations, and advanced reasoning.132MIT
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