LinqAlpha MCP

LinqAlpha MCP gives AI assistants direct access to institutional-grade financial data for fundamental research. Through the Model Context Protocol (MCP), your AI can query company fundamentals, earnings estimates, stock prices, economic indicators, SEC filings, and earnings transcripts — all from a single endpoint.

Supports JSON-RPC 2.0 protocol.

Available methods:

• initialize — Initialize MCP session
• ping — Health check
• tools/list — List available financial data tools
• tools/call — Execute a financial data tool

Setup (Claude Desktop):

{
  "mcpServers": {
    "linqalpha": {
      "url": "https://api.eu.linqalpha.com/v1/mcp",
      "headers": { "x-api-key": "<your-api-key>" }
    }
  }
}

POST /v1/mcp

LinqAlpha MCP

cURL

curl --request POST \
  --url https://api.eu.linqalpha.com/v1/mcp \
  --header 'Content-Type: application/json' \
  --header 'X-API-KEY: <api-key>' \
  --data '
{
  "jsonrpc": "2.0",
  "id": 123,
  "params": {}
}
'

200

{
  "jsonrpc": "2.0",
  "id": 123,
  "result": {},
  "error": {
    "code": 123,
    "message": "<string>"
  }
}

Overview

LinqAlpha MCP gives AI assistants direct access to institutional-grade financial data for fundamental research. Through the Model Context Protocol, your AI (Claude, Cursor, etc.) can query company fundamentals, earnings estimates, stock prices, economic indicators, SEC filings, and earnings transcripts — all from a single endpoint.

Quick Start

If you already have an existing API key, you must request a new key with MCP permissions enabled. Existing keys issued before the MCP endpoint release do not have MCP access. Contact [e-mail] to request a new key.

Add the following to your MCP client configuration:

Claude Desktop

{
  "mcpServers": {
    "linqalpha": {
      "url": "https://api.eu.linqalpha.com/v1/mcp",
      "headers": { "x-api-key": "<your-api-key>" }
    }
  }
}

Use via Completion API

You can use LinqAlpha MCP tools directly from any LLM completion API (OpenAI, Anthropic, etc.) by making standard HTTP requests to our MCP endpoint. This lets you integrate LinqAlpha’s financial data tools into your own AI workflows and applications.

Step 1: List Available Tools

First, fetch the tool definitions to get their names and input schemas:

Python

import requests

MCP_URL = "https://api.eu.linqalpha.com/v1/mcp"
HEADERS = {
    "Content-Type": "application/json",
    "x-api-key": "<your-api-key>"
}

# List all available tools
response = requests.post(MCP_URL, headers=HEADERS, json={
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/list",
    "params": {}
})

tools = response.json()["result"]["tools"]

Step 2: Convert to OpenAI Tool Format

Transform MCP tool definitions into the format expected by OpenAI’s API:

Python

def mcp_to_openai_tools(mcp_tools):
    """Convert MCP tool definitions to OpenAI function calling format."""
    openai_tools = []
    for tool in mcp_tools:
        openai_tools.append({
            "type": "function",
            "function": {
                "name": tool["name"],
                "description": tool["description"],
                "parameters": tool["inputSchema"]
            }
        })
    return openai_tools

openai_tools = mcp_to_openai_tools(tools)

Step 3: Chat with Tool Calling

Use the converted tools in your OpenAI completion request, then execute any tool calls against LinqAlpha MCP:

Python

from openai import OpenAI
import json

client = OpenAI()

messages = [
    {"role": "user", "content": "What was NVIDIA's revenue for the last 4 quarters?"}
]

# 1. Send request with tools
response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=openai_tools,
)

message = response.choices[0].message

# 2. If the model calls a tool, execute it via LinqAlpha MCP
if message.tool_calls:
    messages.append(message)

    for tool_call in message.tool_calls:
        # Execute tool call against LinqAlpha MCP
        mcp_response = requests.post(MCP_URL, headers=HEADERS, json={
            "jsonrpc": "2.0",
            "id": 1,
            "method": "tools/call",
            "params": {
                "name": tool_call.function.name,
                "arguments": json.loads(tool_call.function.arguments)
            }
        })

        tool_result = mcp_response.json()["result"]["content"][0]["text"]

        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": tool_result
        })

    # 3. Get final response with tool results
    final_response = client.chat.completions.create(
        model="gpt-4o",
        messages=messages,
        tools=openai_tools,
    )

    print(final_response.choices[0].message.content)

This pattern works with any LLM that supports function/tool calling — including Anthropic Claude API, Google Gemini, and open-source models. Just adapt the tool format conversion for your provider.

Available Tools

LinqAlpha exposes 20 financial data tools organized by category.

Fundamentals & Estimates

Tool	Description
fundamentals_data_query	Query financial datasets including fundamentals, estimates, and ownership data
fundamentals_data_docs	Browse financial data schema documentation and available fields
stock_prices	Get historical and real-time stock price data

Economic Data

Tool	Description
economic_data_query	Query macroeconomic data (GDP, CPI, employment, Treasury yields, VIX, and more)
economic_indicators	List available economic indicators grouped by category
economic_indicator_data	Get economic indicator time series data (GDP, CPI, unemployment)
economic_calendar	Get upcoming economic data release schedule

Market Data

Tool	Description
forex_rates	Get foreign exchange currency pair rates and history
commodity_prices	Get commodity market data (energy, metals, grains, softs)
treasury_rates	Get US Treasury yield curve data across all maturities (1M-30Y)
market_symbols	List available market symbols for forex, commodities, and economic indicators
market_risk_premium	Get country-level market risk premium for CAPM calculations

Equity & Search

Tool	Description
equity_database_query	Query equity market database (stocks, events, documents)
transcript_search	Full-text search across earnings call transcripts and SEC filings. Requires a Manticore SQL query in the sql parameter (not natural language). Call read_guide('data/manticore') for the SQL dialect and schema

Research & Citations

Tool	Description
cite_filing_source	Create a citation for a filing/transcript excerpt found via transcript_search. Requires chat_session_id (from create_research_session) and chunk_id (the id field of a transcript_search result). This tool does not search — run transcript_search first
cite_web_source	Create a web-based research citation
create_research_session	Create a new research session for citation tracking
get_citations	Retrieve all citations created in the current research session
web_search	Search the web with date range filtering for recent financial news and analysis

Platform

Tool	Description
my_platform_data	Query your organization’s LinqAlpha platform data
list_guides	List available financial data guides and documentation
read_guide	Read a specific financial data guide

Protocol

This endpoint implements JSON-RPC 2.0 over HTTP, following the MCP specification.

Supported Methods

Method	Description
initialize	Initialize MCP session and receive server capabilities
ping	Health check
notifications/initialized	Client notification after initialization
tools/list	List all available tools with their input schemas
tools/call	Execute a tool with the given arguments

Example: List Tools

Request

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list",
  "params": {}
}

Response

{
  "jsonrpc": "2.0",
  "id": 1,
  "result": {
    "tools": [
      {
        "name": "stock_prices",
        "description": "Get historical and real-time stock price data",
        "inputSchema": { ... }
      }
    ]
  }
}

Example: Call a Tool

Request

{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "fundamentals_data_query",
    "arguments": {
      "sql": "SELECT ticker, revenue, net_income FROM financials WHERE ticker = 'AAPL' ORDER BY date DESC LIMIT 4"
    }
  }
}

Response

{
  "jsonrpc": "2.0",
  "id": 2,
  "result": {
    "content": [
      {
        "type": "text",
        "text": "Query returned 4 rows..."
      }
    ]
  }
}

Rate Limits

• 60 requests per minute per user
• Exceeding the limit returns a JSON-RPC error with code -32000

Error Codes

Code	Meaning
-32600	Invalid JSON-RPC request
-32601	Method not found
-32602	Invalid params (e.g., unknown tool name)
-32603	Internal server error
-32000	Rate limit exceeded

Authorizations

X-API-KEY · string · header · required

Body

application/json

jsonrpc · enum<string> · required

JSON-RPC version

Available options: 2.0

method · enum<string> · required

MCP method to invoke

Available options: initialize, ping, notifications/initialized, tools/list, tools/call

id · integer

Request identifier

params · object

Method-specific parameters. For tools/call: { name: string, arguments: object }

Response

200

application/json

JSON-RPC 2.0 response

jsonrpc · enum<string>

Available options: 2.0

id · integer | null

Request identifier (null for error responses)

result · object

Method result (present on success)

error · object

Error object (present on failure)

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