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What is MCP?

The Model Context Protocol (MCP) is an open standard for connecting AI applications to external data sources and tools. Nectr implements MCP in two directions:

Nectr as MCP Server

External agents (Claude Desktop, Linear, Slack bots) can query Nectr’s review data, verdicts, and metrics.

Nectr as MCP Client

Nectr pulls live context from Linear (issues), Sentry (errors), and Slack (messages) during PR reviews.

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Nectr as MCP Server (Outbound)

Nectr exposes its internal review data as an MCP server so external agents can access:
  • Recent PR reviews with AI verdicts
  • Contributor statistics from the Neo4j knowledge graph
  • Per-PR verdict lookup
  • Repository health scores

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Transport Protocol

Nectr’s MCP server uses Server-Sent Events (SSE) over HTTP:
  • SSE Stream: GET /mcp/sse — Server-to-client event stream
  • JSON-RPC: POST /mcp/messages — Client-to-server message ingestion
The MCP server is mounted directly in app/main.py as an ASGI sub-application because FastAPI’s include_router() cannot handle ASGI apps.

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Capabilities

The MCP server exposes:
  • 4 Tools — Callable functions for querying Nectr data
  • 1 Resource — Streaming reviews as serialized JSON

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Server Name

The MCP server identifies itself as "Nectr" during capability exchange.

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Endpoints

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GET /mcp/sse
endpoint
SSE event stream for server-to-client events. Used by MCP clients to receive tool results and server notifications.
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POST /mcp/messages
endpoint
JSON-RPC 2.0 message endpoint for client-to-server tool invocations and queries.

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Connecting External Clients

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Claude Desktop

Add Nectr to your Claude Desktop MCP configuration: 
{
  "mcpServers": {
    "nectr": {
      "url": "https://your-backend.up.railway.app/mcp/sse"
    }
  }
}
Configuration file location:
  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json

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Custom MCP Clients

Use any MCP-compatible client library to connect: 
import httpx
import json

async def call_nectr_tool(tool_name: str, args: dict):
    payload = {
        "jsonrpc": "2.0",
        "id": 1,
        "method": "tools/call",
        "params": {
            "name": tool_name,
            "arguments": args
        }
    }
    
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "https://your-backend.up.railway.app/mcp/messages",
            json=payload
        )
        return response.json()

# Example: Get recent reviews
result = await call_nectr_tool(
    "get_recent_reviews",
    {"repo": "acme/backend", "limit": 10}
)

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Implementation Details

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FastMCP Framework

Nectr uses the FastMCP framework for building the MCP server: 
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Nectr")

@mcp.tool()
async def get_recent_reviews(repo: str, limit: int = 10) -> list[dict]:
    """Get recent PR reviews for a repository."""
    # Implementation
    pass

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Database Access

All MCP tools query Nectr’s PostgreSQL database using async SQLAlchemy:
  • WorkflowRun table — Stores PR review results
  • Neo4j graph — Contributor statistics
  • Graceful degradation if database is unreachable

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Error Handling

All tools implement defensive error handling:
  1. Try to query the database/graph
  2. On exception, log a warning and return empty list/dict
  3. Never crash — external agents always get a valid response
This design ensures that external MCP clients can always connect to Nectr, even if some internal services (Neo4j, Mem0) are temporarily unavailable.

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Authentication

The current MCP server implementation does not require authentication. This is suitable for internal/trusted network deployments only.For production use with untrusted clients, add authentication middleware:
  • JWT bearer token validation
  • API key verification
  • IP allowlist

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Use Cases

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Linear Integration

Query recent PR reviews when a developer mentions a PR in a Linear issue: 
User: "Check if PR #123 in acme/backend was approved"
Linear Bot → Calls get_pr_verdict("acme/backend", 123) → Returns verdict

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Slack Bot

Post daily repository health summaries to a Slack channel: 
Slack Bot → Calls get_repo_health("acme/backend")
           → Posts: "acme/backend health score: 85/100 📊"

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Claude Desktop

Ask Claude about your team’s recent code reviews: 
You: "What were the main issues in our last 10 PRs?"
Claude → Calls get_recent_reviews("acme/backend", 10)
        → Analyzes verdicts and summaries
        → Returns: "The main issues were: ..."

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Custom Dashboards

Build a custom analytics dashboard that pulls live data from Nectr: 
// React component
const { data } = useQuery('repo-health', () =>
  callMCPTool('get_repo_health', { repo: 'acme/backend' })
);

return <HealthScoreCard score={data.health_score} />;

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Source Code

The MCP server implementation is located at: 
app/mcp/server.py
app/mcp/router.py (reference only)
Mounting in FastAPI: 
# app/main.py
from app.mcp.server import mcp

app.mount("/mcp", mcp.sse_app())

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Next Steps

MCP Tools

Explore the 4 callable tools exposed by Nectr’s MCP server

MCP Resources

Learn about streaming resources for bulk data access

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