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Perplexity MCP Server

by laodev1
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TypeScript
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Perplexity MCP Server

Overview

The Perplexity MCP Server is a Node.js implementation of Anthropic's Model Context Protocol (MCP) that enables Claude to interact with Perplexity's language models. This server provides a secure bridge between Claude and Perplexity AI's capabilities, allowing for enhanced AI interactions through tool use.

Related MCP server: Perplexity MCP Server

Available Tools

The server currently implements two main tools:

1. perplexity_chat

Advanced chat completion tool with full message history support.

{
  "name": "perplexity_chat",
  "description": "Generate a chat completion using Perplexity AI",
  "parameters": {
    "model": "string (optional) - One of: llama-3.1-sonar-small-128k-online, llama-3.1-sonar-large-128k-online, llama-3.1-sonar-huge-128k-online",
    "messages": "array of {role, content} objects - The conversation history",
    "temperature": "number (optional) - Sampling temperature between 0-2"
  }
}

2. perplexity_ask

Simplified single-query interface for quick questions.

{
  "name": "perplexity_ask",
  "description": "Send a simple query to Perplexity AI",
  "parameters": {
    "query": "string - The question or prompt to send",
    "model": "string (optional) - One of: llama-3.1-sonar-small-128k-online, llama-3.1-sonar-large-128k-online, llama-3.1-sonar-huge-128k-online"
  }
}

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/perplexity-mcp-server.git
cd perplexity-mcp-server

  2. Install dependencies:

    npm install

  3. Create .env file:

    PERPLEXITY_API_KEY=your-api-key-here

  4. Build the project:

    npm run build

Claude Desktop Configuration

To add this server to Claude Desktop, update your claude_desktop_config.json:

{
  "mcpServers": {
    //more servers...
    "perplexity": {
      "command": "node",
      "args": ["path\\to\\perplexity-mcp-server\\dist\\index.js"],
      "env": {
        "PERPLEXITY_API_KEY": "YOUR_PERPLEXITY_API_KEY"
      }
    }
    //more servers...
  }
}

The configuration file is typically located at:

  • Windows: %APPDATA%/Claude/config/claude_desktop_config.json

  • macOS: ~/Library/Application Support/Claude/config/claude_desktop_config.json

  • Linux: ~/.config/Claude/config/claude_desktop_config.json

Development

Start the development server with automatic recompilation:

npm run dev

The server uses TypeScript and implements the MCP protocol using the @modelcontextprotocol/sdk package.

Architecture

Core Components

  1. PerplexityServer Class

    • Implements MCP server protocol

    • Handles tool registration and execution

    • Manages error handling and server lifecycle

  2. Tools System

    • Modular tool definitions

    • Type-safe tool handlers

    • Structured input validation

Technical Details

  • Built with TypeScript for type safety

  • Uses @modelcontextprotocol/sdk for MCP implementation

  • Communicates via stdio transport

  • Environment-based configuration

Error Handling

The server implements comprehensive error handling:

  • API error reporting

  • Invalid tool requests handling

  • Connection error management

  • Process signal handling

Dependencies

  • @modelcontextprotocol/sdk: ^1.0.3

  • dotenv: ^16.4.7

  • isomorphic-fetch: ^3.0.0

Contributing

  1. Fork the repository

  2. Create your feature branch (git checkout -b feature/amazing-feature)

  3. Commit your changes (git commit -m 'Add some amazing feature')

  4. Push to the branch (git push origin feature/amazing-feature)

  5. Open a Pull Request

Security

  • API keys are managed through environment variables

  • Input validation for all tool parameters

  • Error messages are sanitized before output

  • Process isolation through MCP protocol

License

This project is licensed under the ISC License.

Troubleshooting

Common issues and solutions:

  1. Server Not Found

    • Verify the path in claude_desktop_config.json is correct

    • Ensure the server is built (npm run build)

    • Check if Node.js is in your PATH

  2. Authentication Errors

    • Verify your Perplexity API key in .env

    • Check if the API key has the required permissions

  3. Tool Execution Errors

    • Verify the tool parameters match the schema

    • Check network connectivity

    • Review server logs for detailed error messages

Available Tools

2 tools
perplexity_askB

Send a simple query to Perplexity AI

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoThe model to use for completion
queryYesThe question or prompt to send

TDQS

B3.1/5.0
Behavior2/5

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

With no annotations and a minimal description, the tool's behavior is opaque. It does not disclose whether the query is synchronous, what response format to expect, or any safety/reliability 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?

The description is a single clear sentence that immediately conveys the tool's purpose. It is appropriately front-loaded and concise, though it could incorporate more detail without becoming wordy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple query tool with no output schema, the description lacks information about the response (e.g., text output, confidence scores). The agent would need to infer or discover the return type from usage.

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 coverage is 100%, so both parameters have descriptions. The description adds no extra value beyond the schema, but baseline 3 is appropriate as the schema already documents parameters adequately.

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 clearly states the action ('Send a simple query') and the target resource ('Perplexity AI'). It distinguishes from the sibling tool 'perplexity_chat' by implying this is for a single query, while 'chat' likely involves multi-turn conversation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the sibling 'perplexity_chat'. There is no mention of prerequisites, limitations, or alternatives, leaving the agent to infer usage context.

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

perplexity_chatC

Generate a chat completion using Perplexity AI

ParametersJSON Schema
NameRequiredDescriptionDefault
modelNoThe model to use for completion
messagesYesArray of messages in the conversation
temperatureNoSampling temperature (0-2)

TDQS

C2.8/5.0
Behavior2/5

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

No behavioral traits are disclosed beyond the minimal description. The schema includes a model with 'sonar-small-online' suggesting internet access, but this is not mentioned. With no annotations, the description should provide more context about side effects, permissions, or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, which is concise, but it is too brief to convey necessary information. It could be expanded without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters and a sibling tool, the description is insufficient. It does not explain the role of Perplexity AI, the meaning of different models, or how this tool differs from 'perplexity_ask'. The lack of output schema increases the need for a more complete description.

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 all parameters are documented in the schema. The description adds no additional meaning beyond what is already in the schema. Baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: generating a chat completion using Perplexity AI. However, it does not differentiate from the sibling tool 'perplexity_ask', which likely performs a similar but distinct function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 the sibling tool 'perplexity_ask'. The context hints at a distinction (chat vs. ask), but it is not explicitly stated.

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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TDQS

C2.9/5.0
Disambiguation2/5

Both tools involve querying Perplexity AI, with only subtle differences in description ('simple query' vs 'chat completion'). An agent would likely struggle to decide which to use, as the boundaries are unclear.

Naming Consistency4/5

Both tools follow a consistent 'perplexity_<verb>' pattern. The verbs 'ask' and 'chat' are different but semantically related, and no mixing of naming conventions is present.

Tool Count3/5

With only 2 tools, the server feels minimal for a service like Perplexity AI, which typically offers more nuanced capabilities (e.g., different models, streaming). However, it covers basic query and chat needs.

Completeness3/5

The tool surface covers basic query and chat interactions but lacks operations such as specifying model parameters, context handling, or result streaming. This may limit agent flexibility.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

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