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Glama
0xHumban

perplexity-mcp

by 0xHumban
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
PERPLEXITY_API_KEYYesAPI Key for Perplexity AI

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_examplesA
Get examples of prompts and usage patterns for this Perplexity MCP server.

This tool helps you discover what you can do with this server and provides
concrete examples of effective prompts for each tool.

Returns:
    Detailed examples and usage guide for all available tools
ask_perplexityA
Send a prompt to Perplexity and return the response.
Sonar models have internet access and can perform searches.

Use this for: research, current events, comparisons, finding information online.

Examples of good prompts:
- "What are the latest developments in quantum computing in 2025?"
- "Compare Python vs Rust for web development"
- "Explain the recent changes in EU privacy laws"
- "Find the best practices for React Server Components"

If the user wants to execute or learn complex tasks, use the reasoning model (sonar-reasoning)
If the user wants development work requiring real-time documentation lookup, research-intensive coding, use the reasoning model (sonar-reasoning).
But by default, use the research model (sonar).

Args:
    prompt: The prompt/question to send
    model: Sonar model (sonar, sonar-pro, sonar-deep-research, 
           sonar-reasoning, sonar-reasoning-pro)
ask_perplexity_exact_responseA
Send a prompt to Perplexity and return the exact response without 
changing anything (no additional metadata or suggestions).

Use this when you need the raw, unmodified output from Perplexity.

Examples of good prompts:
- "Give me the exact documentation for FastAPI's dependency injection"
- "What does the official Next.js docs say about App Router?"
- "Show me the raw API response format for OpenAI's latest models"

If the user wants to execute or learn complex tasks, use the reasoning model (sonar-reasoning)
If the user wants development work requiring real-time documentation lookup, research-intensive coding, use the reasoning model (sonar-reasoning).
But by default, use the research model (sonar).

Sonar models have internet access and can perform searches.

Args:
    prompt: The prompt/question to send
    model: Sonar model (sonar, sonar-pro, sonar-deep-research, 
           sonar-reasoning, sonar-reasoning-pro)
ask_perplexity_for_instructionsA
Send a prompt to Perplexity and get step-by-step instructions that 
Copilot can execute in agent mode.

This tool is optimized for tasks that require multiple steps or actions.
The response will be formatted as executable instructions for the AI agent.

Examples of good prompts:
- "How do I set up a Python FastAPI project with Docker?"
- "Create a React app with TypeScript and Tailwind CSS"
- "Set up CI/CD pipeline for a Node.js application"
- "Configure a PostgreSQL database with Redis caching"

Sonar models have internet access and can perform searches.

Args:
    prompt: The prompt/question to send
    model: Sonar model (sonar, sonar-pro, sonar-deep-research, 
           sonar-reasoning, sonar-reasoning-pro)
ask_perplexity_to_learnA
Learn complex concepts with pedagogical explanations, examples, and analogies.

This tool uses a teaching-optimized approach with:
- Simple overviews and context
- Step-by-step breakdowns
- Concrete examples and analogies
- Code snippets when relevant
- Clear summaries and next steps

Examples of good prompts:
- "Explain how async/await works in Python"
- "Teach me about Docker containers and why they're useful"
- "What are React hooks and how do I use them?"
- "Explain database indexing with practical examples"
- "How does JWT authentication work?"

Sonar models have internet access and can perform searches.

Args:
    prompt: The prompt/question to send
    model: Sonar model (sonar, sonar-pro, sonar-deep-research, 
           sonar-reasoning, sonar-reasoning-pro)

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 5 tools

Disambiguation4/5

The four 'ask_perplexity_*' tools are differentiated by response style (general, exact, instructions, learning) with clear descriptions. The 'get_examples' tool is distinct. Some overlap between instructions and learning could cause minor ambiguity, but overall well-differentiated.

Naming Consistency4/5

All primary tools follow a consistent 'ask_perplexity_<purpose>' pattern. 'get_examples' uses a different verb-noun structure, but as a utility tool, this deviation is minor and acceptable.

Tool Count5/5

With 5 tools, the server is well-scoped for its purpose of querying Perplexity in different modes. Each tool earns its place without being excessive or insufficient.

Completeness5/5

The tool set covers a comprehensive range of use cases: general research, exact responses, step-by-step instructions, pedagogical learning, and an examples guide. No obvious gaps for the intended domain.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources