perplexity-mcp-server

gomcpgo/perplexity-mcp-server

3.3

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The Perplexity MCP Server allows LLMs like Claude to perform internet research using the Perplexity API, providing real-time information with source citations.

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Perplexity MCP Server

An MCP (Model Context Protocol) server that provides access to Perplexity AI's powerful search capabilities, including web search, academic research, financial data, and advanced filtering options.

Features

The Perplexity MCP server offers six functions for comprehensive search and result management:

Search Functions (4)

Each optimized for different use cases. All functions automatically return source URLs and save results locally if caching is enabled.

  1. perplexity_search: General web search with real-time information. Best for current events, general knowledge, and quick facts.

  2. perplexity_academic_search: Automatically filters to academic sources (arxiv.org, pubmed, journals). Best for research papers, scientific studies, and scholarly content.

  3. perplexity_financial_search: Optimized for financial domains and recent data. Best for stock analysis, earnings reports, SEC filings, and market trends.

  4. perplexity_filtered_search: Advanced search with multiple filtering options. Best when you need specific domain filtering, content types, or location-based results.

Cache Management Functions (2)

Manage previously saved search results for easy reference and reuse.

  1. list_previous: List all previous search queries with unique IDs, sorted by recency. Returns JSON array with query details.

  2. get_previous_result: Retrieve a previously cached search result by its unique 10-character ID.

Installation

  1. Ensure you have Go 1.23 or later installed
  2. Clone this repository
  3. Build the server: 
    ./run.sh build

Configuration

The server requires a Perplexity API key and supports various configuration options through environment variables:

Required

  • PERPLEXITY_API_KEY: Your Perplexity AI API key

Optional

  • PERPLEXITY_DEFAULT_MODEL: Default model to use (default: "sonar")
    • sonar: Fast, cost-effective search for quick facts
    • sonar-pro: Comprehensive search with better depth and coverage
  • PERPLEXITY_MAX_TOKENS: Maximum tokens in response (default: 1024)
  • PERPLEXITY_TEMPERATURE: Response randomness 0-2 (default: 0.2)
  • PERPLEXITY_TOP_P: Nucleus sampling parameter (default: 0.9)
  • PERPLEXITY_TOP_K: Top-k sampling parameter (default: 0)
  • PERPLEXITY_TIMEOUT: Request timeout duration (default: 30s)
  • PERPLEXITY_RETURN_IMAGES: Include images by default (default: false)
  • PERPLEXITY_RETURN_RELATED: Include related questions by default (default: false)
  • PERPLEXITY_RESULTS_ROOT_FOLDER: Directory to store cached search results (default: empty/disabled)

Usage

MCP Server Mode

Run the server in MCP mode (default):

export PERPLEXITY_API_KEY="your-api-key"
./run.sh run
# or directly: ./perplexity

Terminal Mode (CLI Testing)

Test individual functions directly from the command line:

export PERPLEXITY_API_KEY="your-api-key"

# Test different search types
./run.sh search "latest AI news" sonar-pro
./run.sh academic "quantum computing" sonar-pro
./run.sh financial "AAPL earnings" sonar-pro
./run.sh filtered "renewable energy" sonar-pro

# Cache management
./run.sh list                    # List previous queries
./run.sh get ABC123XYZ0         # Get cached result by ID

Integration Tests

Run integration tests against the real Perplexity API:

export PERPLEXITY_API_KEY="your-api-key"
./run.sh integration-test

MCP Client Configuration

To use this server with an MCP client, add it to your client configuration:

{
  "servers": {
    "perplexity": {
      "command": "path/to/perplexity",
      "env": {
        "PERPLEXITY_API_KEY": "your-api-key"
      }
    }
  }
}

Local Result Caching

The server automatically caches search results when PERPLEXITY_RESULTS_ROOT_FOLDER is configured:

  • Storage: Each result is saved in /unique_id/result.md with metadata in /unique_id/metadata.yaml
  • Unique IDs: 10-character alphanumeric identifiers (e.g., A1B2C3D4E5)
  • Result ID: When caching is enabled, search responses include **Result ID:** ABC123XYZ0
  • No Reuse: Each search creates a new cached entry, even for identical queries
  • LLM Integration: Perfect for LLMs to reference previous searches in conversations

Cache Management Examples

# List previous searches
echo '{"method": "tools/call", "params": {"name": "list_previous", "arguments": {}}}' | ./perplexity

# Get specific result
echo '{"method": "tools/call", "params": {"name": "get_previous_result", "arguments": {"unique_id": "A1B2C3D4E5"}}}' | ./perplexity

Function Reference

perplexity_search

Perform a general web search.

Parameters:

  • query (required): The search query
  • model: Choose 'sonar' for quick searches or 'sonar-pro' for comprehensive results (default: sonar)
  • search_domain_filter: Array of domains to include
  • search_exclude_domains: Array of domains to exclude
  • search_recency_filter: Time filter (hour, day, week, month, year)
  • return_images: Include images
  • return_related_questions: Include related questions
  • max_tokens: Maximum response tokens
  • temperature: Response randomness (0-2)
  • date_range_start: Start date (YYYY-MM-DD)
  • date_range_end: End date (YYYY-MM-DD)
  • location: Geo-specific search location

Example:

{
  "query": "latest AI developments",
  "model": "sonar-pro",
  "search_recency_filter": "week",
  "return_citations": true
}

perplexity_academic_search

Search academic papers and scholarly content.

Parameters:

  • query (required): The academic search query
  • subject_area: Academic subject (e.g., "Physics", "Computer Science")
  • model: Defaults to 'sonar-pro' for comprehensive academic results
  • search_domain_filter: Array of academic domains
  • search_recency_filter: Time filter
  • max_tokens: Maximum response tokens
  • temperature: Response randomness

Example:

{
  "query": "quantum computing applications",
  "subject_area": "Physics",
  "search_recency_filter": "year"
}

perplexity_financial_search

Search financial data and SEC filings.

Parameters:

  • query (required): The financial search query
  • ticker: Stock ticker symbol (e.g., "AAPL")
  • company_name: Company name
  • report_type: Financial report type (e.g., "10-K", "10-Q", "8-K")
  • model: Defaults to 'sonar-pro' for comprehensive financial data
  • search_recency_filter: Time filter
  • date_range_start: Report start date
  • date_range_end: Report end date
  • max_tokens: Maximum response tokens

Example:

{
  "query": "quarterly earnings",
  "ticker": "MSFT",
  "report_type": "10-Q",
  "search_recency_filter": "month"
}

perplexity_filtered_search

Advanced search with comprehensive filtering.

Parameters:

  • query (required): The search query
  • model: Choose based on needs (defaults to sonar-pro)
  • search_domain_filter: Array of domains to include
  • search_exclude_domains: Array of domains to exclude
  • search_recency_filter: Time filter
  • content_type: Type of content (news, academic, blog, etc.)
  • file_type: File type filter (pdf, doc, html, etc.)
  • language: Language filter
  • country: Country for geo-specific search
  • date_range_start: Start date
  • date_range_end: End date
  • return_citations: Include citations
  • return_images: Include images
  • return_related_questions: Include related questions
  • max_tokens: Maximum response tokens
  • temperature: Response randomness
  • custom_filters: Object with additional key-value filters

Example:

{
  "query": "renewable energy innovations",
  "content_type": "news",
  "language": "English",
  "country": "Germany",
  "search_recency_filter": "month",
  "custom_filters": {
    "industry": "energy",
    "technology": "solar"
  }
}

list_previous

List all previous search queries with metadata.

Parameters: None

Response: JSON array with query history, sorted by recency (most recent first).

Example:

[
  {
    "query": "latest AI developments",
    "unique_id": "A1B2C3D4E5",
    "datetime": "2025-01-15T10:30:45Z",
    "search_type": "general"
  },
  {
    "query": "quantum computing research",
    "unique_id": "X9Y8Z7W6V5",
    "datetime": "2025-01-15T09:15:30Z",
    "search_type": "academic"
  }
]

get_previous_result

Retrieve a cached search result by unique ID.

Parameters:

  • unique_id (required): The 10-character alphanumeric ID of the cached result

Returns: The complete markdown result from the cached search.

Example:

{
  "unique_id": "A1B2C3D4E5"
}

Response Format

All search functions return responses in the following format:

  1. Main Content: The search results and answer
  2. Source URLs: A list of source URLs that the LLM can fetch for more details
  3. Detailed Sources (if available): Title, URL, and snippet for each source
  4. Related Questions (if requested): Suggested follow-up questions
  5. Result ID (if caching enabled): Unique 10-character ID for retrieving this result later

Example response structure:

[Main search results content...]

## Source URLs
1. https://example.com/article1
2. https://example.com/article2
3. https://example.com/article3

## Detailed Sources
1. **Article Title**
   URL: https://example.com/article1
   Snippet: Brief excerpt from the article...

## Related Questions
- What are the latest developments?
- How does this compare to...?

**Result ID:** A1B2C3D4E5

Development

Running Tests

Run unit tests:

./run.sh test

Run integration tests with real API:

./run.sh integration-test

Project Structure

The server follows clean architecture principles with separation of concerns:

perplexity/
ā”œā”€ā”€ cmd/
ā”‚   ā””ā”€ā”€ main.go              # Thin entry point with terminal mode (~200 lines)
ā”œā”€ā”€ pkg/
ā”‚   ā”œā”€ā”€ handler/             # MCP protocol layer
ā”‚   ā”‚   ā”œā”€ā”€ handler.go       # Main MCP handler  
ā”‚   ā”‚   ā”œā”€ā”€ tools.go         # Tool definitions
ā”‚   ā”‚   ā””ā”€ā”€ search_handlers.go # Parameter extraction
ā”‚   ā”œā”€ā”€ search/              # Core business logic
ā”‚   ā”‚   ā”œā”€ā”€ types.go         # Local search types
ā”‚   ā”‚   ā”œā”€ā”€ search.go        # Strongly-typed search functions
ā”‚   ā”‚   ā””ā”€ā”€ client.go        # Perplexity API client
ā”‚   ā”œā”€ā”€ cache/               # Result caching system
ā”‚   ā”œā”€ā”€ config/              # Configuration management
ā”‚   ā””ā”€ā”€ types/               # Perplexity API types
ā”œā”€ā”€ test/
ā”‚   ā””ā”€ā”€ test.go             # Integration tests
ā””ā”€ā”€ README.md

Architecture Benefits

  • Thin main.go: Reduced from 360 to 197 lines (45% reduction)
  • Terminal mode: Direct CLI testing without MCP protocol overhead
  • Separation of concerns: MCP protocol handling separate from business logic
  • Strongly-typed: Core functions use proper Go structs instead of map[string]interface{}
  • Local types: Each package owns its types, preventing circular dependencies
  • Easy testing: Business logic can be tested independently

Error Handling

The server handles various error conditions:

  • Invalid or missing API key (401)
  • Rate limiting (429)
  • Invalid parameters (400)
  • Server errors (500)

Errors are returned with descriptive messages to help diagnose issues.

License

MIT License - see LICENSE file for details.