Pratik Bhadane
126 lines
2.7 KiB
Markdown
126 lines
2.7 KiB
Markdown
# MCP Server — Connect ferro-ta in Cursor
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ferro-ta ships an MCP (Model Context Protocol) server that exposes
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indicators and backtest tools to AI agents. This guide shows how to run
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the server and connect it to Cursor or any MCP-compatible client.
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---
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## Installation
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The MCP server requires no additional dependencies beyond ferro_ta itself.
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For the full MCP SDK integration (recommended), install the optional extra:
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```bash
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pip install "ferro-ta[mcp]"
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```
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or install the `mcp` package separately:
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```bash
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pip install "mcp>=1.0"
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```
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---
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## Running the server
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```bash
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python -m ferro_ta.mcp
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```
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The server listens on stdin/stdout using JSON-RPC 2.0 (the MCP protocol).
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---
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## Connect in Cursor
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1. Open Cursor settings (Command Palette → "Open User Settings (JSON)").
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2. Find or create the `mcpServers` section:
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```json
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{
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"mcpServers": {
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"ferro-ta": {
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"command": "python",
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"args": ["-m", "ferro_ta.mcp"],
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"description": "ferro_ta — Technical Analysis MCP server"
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}
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}
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}
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```
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3. Reload Cursor (Command Palette → "Developer: Reload Window").
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4. The ferro-ta tools will appear in the Tools panel.
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### Workspace-level config
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You can also add the config to your project's `.cursor/mcp.json`:
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```json
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{
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"mcpServers": {
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"ferro-ta": {
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"command": "python",
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"args": ["-m", "ferro_ta.mcp"]
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}
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}
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}
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```
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---
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## Example prompts
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Once connected, you can ask Claude (or any MCP-enabled AI) things like:
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> "Compute RSI(14) on this price series: [100, 102, 101, 105, 108, 104, 107]"
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> "Run a backtest with the rsi_30_70 strategy on [100, 101, 99, 103, 106, 102, 108, 105, 109, 112, 108, 111]"
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> "List all available ferro_ta indicators"
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> "What does the SMA indicator do?"
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---
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## Available tools
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| Tool | Description |
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|------|-------------|
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| `sma` | Simple Moving Average |
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| `ema` | Exponential Moving Average |
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| `rsi` | Relative Strength Index |
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| `macd` | MACD line, signal, histogram |
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| `backtest` | Vectorized backtest (rsi_30_70, sma_crossover, macd_crossover) |
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| `list_indicators` | List all registered indicators |
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| `describe_indicator` | Describe a named indicator |
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---
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## Programmatic use (Python client)
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You can also use the MCP handlers directly in Python without the server:
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```python
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from ferro_ta.mcp import handle_list_tools, handle_call_tool
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import numpy as np
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# List tools
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tools = handle_list_tools()
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print([t["name"] for t in tools["tools"]])
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# Call RSI
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close = list(np.cumprod(1 + np.random.default_rng(0).normal(0, 0.01, 50)) * 100)
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result = handle_call_tool("rsi", {"close": close, "timeperiod": 14})
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print(result)
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```
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---
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## See also
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- `ferro_ta.mcp` — module source.
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- `ferro_ta.tools` — underlying tool functions.
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- `docs/agentic.md` — LangChain and workflow integration.
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