Perplexity AI MCP Server
Integrates with Perplexity AI's API to provide advanced search capabilities through the perplexity_search tool, supporting multiple Sonar models including sonar-reasoning-pro, sonar-reasoning, sonar-pro, and sonar
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 AI MCP Serversearch for latest developments in quantum computing with 3 results"
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 AI MCP Server
An MCP server implementation that integrates Perplexity AI's API, providing advanced search capabilities with multiple model options.
Features
Search using Perplexity AI's models
Support for all official Sonar models
Configurable result count
Detailed error handling and logging
MCP Inspector compatible
Related MCP server: PPX-MCP
Available Models
sonar-reasoning-pro (127k context)
sonar-reasoning (127k context)
sonar-pro (200k context)
sonar (127k context)
Installation
pnpm install
pnpm buildConfiguration
API Key
Sign up for a Perplexity AI account
Get your API key from the dashboard
Set the environment variable:
export PERPLEXITY_API_KEY=your_api_key_hereUsage
Direct Execution
node dist/index.js
# or if you made it executable
./dist/index.jsDevelopment
pnpm devTesting with MCP Inspector
pnpm inspectTool Reference
perplexity_search
Performs a search using Perplexity AI's models.
Parameters:
query(string, required): The search querymodel(string, optional): Model to use (default: "sonar")Available options: sonar-reasoning-pro, sonar-reasoning, sonar-pro, sonar
count(number, optional): Number of results (1-10, default: 5)
Development
Project Structure
src/perplexity/
├── index.ts # Main server implementation
├── lib/
│ └── logger.ts # Logging configuration
└── adr.md # Architectural decisionsLogging
Logs are written to perplexity-mcp.log in the project root directory.
License
MIT
Contributing
Fork the repository
Create your feature branch
Commit your changes
Push to the branch
Create a new Pull Request
Available Tools
1 toolperplexity_searchC
Search using Perplexity AI's models with context-aware responses and citations
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| model | No | Model to use (sonar-reasoning-pro, sonar-reasoning, sonar-pro, sonar) | sonar |
| count | No |
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 'context-aware responses and citations', which adds some value about output characteristics, but fails to address critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error handling. For a search tool with zero annotation coverage, this leaves significant gaps.
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 conveys the core functionality without unnecessary words. It's appropriately sized and front-loaded with the essential information.
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 complexity of a search tool with 3 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (beyond mentioning 'responses and citations'), doesn't cover parameter meanings beyond what little the schema provides, and leaves behavioral aspects unclear. This is inadequate for proper tool selection and invocation.
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?
Schema description coverage is only 33% (only the 'model' parameter has a description), so the description needs to compensate but doesn't mention any parameters. The baseline would be lower, but since there are only 3 parameters and one is well-documented in the schema, the description's failure to add parameter context results in a minimal viable score.
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 action ('Search') and the resource ('Perplexity AI's models'), specifying it provides 'context-aware responses and citations'. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest score.
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 exclusions. It only states what the tool does without contextual usage information.
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
perplexity_search
TDQS
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as searching using Perplexity AI's models, making it distinct by default.
The single tool name 'perplexity_search' follows a clear and consistent verb_noun pattern (search as the verb, perplexity as a modifier). With only one tool, naming consistency is inherently perfect as there are no other tools to compare against.
A single tool is generally too few for most server purposes, as it limits functionality and flexibility. For a search-focused server, additional tools like filtering, citation management, or model selection might be expected to provide a more complete experience.
The server's domain appears to be search using Perplexity AI, but with only one search tool, there are significant gaps. Missing operations could include advanced search parameters, result refinement, citation handling, or integration with other Perplexity features, making the surface incomplete for typical search workflows.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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