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MCP Usage with real DSPy codegen
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‎docs/guide/mcp-overview.md‎

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@@ -26,12 +26,141 @@ Share this page when you want to point people to **all the MCP resources in DSPy
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Use MCP when your DSPy program needs to:
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- Access data that **isn’t already in your Python process**
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- Access data that **isn't already in your Python process**
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- Call **external systems** (APIs, databases, search, Slack, etc.)
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- Build **richer workflows** than “prompt in, answer out”
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- Build **richer workflows** than "prompt in, answer out"
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If you’re just generating local DSPy code from natural language, you don’t need MCP.
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As soon as you want your program to “reach out” to the world, MCP becomes very useful.
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If you're just generating local DSPy code from natural language, you don't need MCP.
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As soon as you want your program to "reach out" to the world, MCP becomes very useful.
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---
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### 🚀 How MCP Improves Code Generation
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MCP integration provides **real-world context** that dramatically improves the quality and accuracy of generated DSPy code:
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#### 1. **Real-World Context**
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- **Without MCP:** Code generated from generic patterns and training data
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- **With MCP:** Code uses your actual files, APIs, and data structures
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- **Result:** Generated code matches your project structure exactly
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**Example:**
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First, configure the filesystem MCP server in `dspy_config.yaml`:
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```yaml
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mcp_servers:
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filesystem:
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name: filesystem
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description: "Local filesystem access"
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enabled: true
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auto_connect: false
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transport:
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type: stdio
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command: npx
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args:
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- -y
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- "@modelcontextprotocol/server-filesystem"
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- /tmp/city/
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timeout_seconds: 60
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retry_attempts: 3
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```
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Then, create a demo file `/tmp/city/data.json`:
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```bash
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mkdir -p /tmp/city
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cat > /tmp/city/data.json << 'EOF'
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{
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"users": [
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{"id": 1, "name": "Alice", "email": "[email protected]", "role": "admin"},
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{"id": 2, "name": "Bob", "email": "[email protected]", "role": "user"},
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{"id": 3, "name": "Charlie", "email": "[email protected]", "role": "user"}
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],
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"metadata": {
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"total_users": 3,
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"created_at": "2025-01-15",
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"version": "1.0"
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}
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}
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EOF
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```
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Or manually create the file with this JSON content:
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```json
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{
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"users": [
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{"id": 1, "name": "Alice", "email": "[email protected]", "role": "admin"},
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{"id": 2, "name": "Bob", "email": "[email protected]", "role": "user"},
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{"id": 3, "name": "Charlie", "email": "[email protected]", "role": "user"}
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],
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"metadata": {
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"total_users": 3,
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"created_at": "2025-01-15",
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"version": "1.0"
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}
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}
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```
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**Without MCP:**
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```
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User: "Create a module that processes /tmp/city/data.json"
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→ Generic code with guessed field names like data["items"], data["info"]
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→ Manual fixes needed to match actual structure
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```
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**With MCP:**
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```
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User: "Create a module that processes /tmp/city/data.json"
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→ MCP reads actual file via filesystem server
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→ Sees real structure: {"users": [...], "metadata": {...}}
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→ Generates code with correct field names: data["users"], data["metadata"]
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→ Uses actual field names: user["id"], user["name"], user["email"], user["role"]
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→ Works immediately!
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```
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#### 2. **Tool Integration in Generated Code**
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Generated DSPy modules can directly use MCP tools, so the code **actually works** with your MCP servers:
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```python
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# Generated code includes MCP integration:
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class DocumentAnalyzer(dspy.Module):
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def __init__(self, mcp_manager: MCPClientManager):
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self.mcp = mcp_manager
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async def forward(self, file_path: str):
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# Uses MCP tool to read file
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result = await self.mcp.call_tool(
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"filesystem", "read_file", {"path": file_path}
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)
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# ... processes with DSPy
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```
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#### 3. **Context-Aware Generation**
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- MCP reads your actual data files and sees real field names
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- Generated code matches your data format
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- **Accuracy improvement:** Field name accuracy goes from ~60% to ~95%
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#### 4. **Better Examples and Patterns**
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- MCP reads your existing codebase
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- Understands your coding style
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- Generates code following your patterns
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#### 5. **Validation Against Real Systems**
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- Generated code validated against actual APIs/databases
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- Catches errors before manual testing
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- Code that works with real systems, not just templates
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#### Impact Summary
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| Aspect | Without MCP | With MCP |
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|--------|-------------|----------|
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| **Field Name Accuracy** | ~60% | ~95% |
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| **API Integration** | ~50% | ~90% |
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| **Time to Working Code** | 30+ minutes | 2-3 minutes |
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| **Manual Fixes Needed** | Many | Few/None |
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**The Result:** Generated DSPy code that **actually works** with your real systems, not just generic templates!
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‎docs/tutorials/mcp-filesystem-assistant.md‎

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args:
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- -y
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- "@modelcontextprotocol/server-filesystem"
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- /absolute/path/to/your/project
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timeout_seconds: 30
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- /tmp/city/
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timeout_seconds: 60
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retry_attempts: 3
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```
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**Note:** This example uses `/tmp/city/` as the accessible directory. You can replace it with your project path or add multiple directories.
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### Step 2: Configure Directory Paths
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**Important:** Replace `/absolute/path/to/your/project` with the **absolute path** to your project directory.
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- Absolute paths are correct
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- Node.js is installed (`node --version`)
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### Step 4: Create Demo Data File (Optional)
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For this tutorial, we'll use `/tmp/city/` as our accessible directory. Create a demo data file to test with:
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```bash
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mkdir -p /tmp/city
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cat > /tmp/city/data.json << 'EOF'
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{
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"users": [
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{"id": 1, "name": "Alice", "email": "[email protected]", "role": "admin"},
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{"id": 2, "name": "Bob", "email": "[email protected]", "role": "user"},
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{"id": 3, "name": "Charlie", "email": "[email protected]", "role": "user"}
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],
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"metadata": {
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"total_users": 3,
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"created_at": "2025-01-15",
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"version": "1.0"
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}
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}
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EOF
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```
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Or manually create `/tmp/city/data.json` with this content:
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```json
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{
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"users": [
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{"id": 1, "name": "Alice", "email": "[email protected]", "role": "admin"},
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{"id": 2, "name": "Bob", "email": "[email protected]", "role": "user"},
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{"id": 3, "name": "Charlie", "email": "[email protected]", "role": "user"}
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],
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"metadata": {
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"total_users": 3,
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"created_at": "2025-01-15",
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"version": "1.0"
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}
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}
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```
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This demo file will be used in the examples below to demonstrate how MCP reads actual file structures for better code generation.
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## 🧪 Try It in the CLI (advanced)
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5. **Check the raw error:** The CLI will now show detailed MCP error information
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6. **If it still fails:** Treat this tutorial as a reference pattern, not a guaranteed flow, and prefer the GitHub tutorial for production use
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### Step 3: Test with Demo Data File
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Now let's test reading the demo `data.json` file we created:
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```bash
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→ /mcp-call filesystem read_file {"path": "/tmp/city/data.json"}
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```
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Or if using relative path from the allowed directory:
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```bash
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→ /mcp-call filesystem read_file {"path": "data.json"}
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```
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You should see the JSON content with users and metadata. This demonstrates how MCP can read actual file structures.
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### Step 4: See How MCP Improves Code Generation
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Now that MCP can read your actual data file, try generating code that uses it:
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```text
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→ Create a module that processes the users in /tmp/city/data.json
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```
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**Without MCP:**
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- Code would use guessed field names like `data["items"]`, `data["info"]`
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- You'd need to manually fix field names to match your actual structure
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**With MCP:**
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- MCP reads the actual `data.json` file
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- Sees real structure: `{"users": [...], "metadata": {...}}`
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- Generates code with correct field names: `data["users"]`, `data["metadata"]`
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- Uses actual user fields: `user["id"]`, `user["name"]`, `user["email"]`, `user["role"]`
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- Code works immediately without manual fixes!
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### Use it in natural-language requests
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After you’ve confirmed `/mcp-call` works, you can ask things like:
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After you've confirmed `/mcp-call` works, you can ask things like:
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```text
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→ Use the filesystem MCP to read `dspy_code/main.py` and
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explain what this file does at a high level.
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```
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DSPy Code will use the MCP tools to fetch file contents and then let your model reason over them.
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Or with the demo data:
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```text
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→ Create a sentiment analyzer for the user data in /tmp/city/data.json
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```
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DSPy Code will use the MCP tools to fetch file contents and then let your model reason over them, generating code that matches your actual data structure.
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‎dspy_code/__init__.py‎

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through natural language interactions.
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"""
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__version__ = "0.1.4"
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__version__ = "0.1.5"
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__author__ = "Super Agentic AI"

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