Project-Based MCP: Targeted on-demand AI Integration

Posted on Author bjdean 0

Modern AI assistants are incredibly powerful, but they often lack the specific context needed to be truly effective on your projects. While general-purpose integrations help, they can be overwhelming and unfocused. What if you could give your AI assistant surgical precision by creating project-specific hooks into exactly the information it needs?

Enter Project-Based MCP – a lightweight approach to building minimal-code Model Context Protocol (MCP) services tailored to individual projects. Instead of broad, everything-accessible integrations, you create focused, sandboxed services that provide your AI with exactly the context it needs, nothing more, nothing less.

The Problem with Generic Integrations

Traditional AI integrations often follow an “all or nothing” approach:

  • email integration gives access to your entire email history (including things like emails that are personal, old, unrelated, similar but perhaps misleading from other projects)
  • Ticket system integration exposes every project’s issues
  • API access might have overly broad permissions – with problems ranging from big security or data integrity issues through to exposing too much of an API (perhaps this project is only able to sue specific API calls).

This creates several problems:

  • Information overload: The AI gets distracted by irrelevant context – bloating the context (reducing space for relevant information) or making hallucination type generation more likely.
  • Security concerns: Broader access than necessary increases risk
  • Reduced accuracy: Important project-specific information gets lost in the noise
  • Cross-project contamination: Details from one project might influence another

Using Project-Based MCP

Using project-Based MCP avoids a lot of these sorts of issues. For each project, you create a lightweight MCP service that provides targeted access to just the information you need.

Because this code is near single-use and may only be used by a single person or team it can be coded in a very lean way – avoiding complex abstractions and test frameworks and instead building tools on demand to solve only the problem at hand. Security remains an important consideration – so good practices like secret/credentials management and sand-boxing should be used.

Some examples with pseudo-implementations:

1. Filtered Email Access

Instead of searching through thousands of emails, create project-specific filters:

# emails_mcp.py - Project Alpha Email Service
import asyncio
from mcp import MCPServer
from mcp.types import Resource, Tool
import imaplib
import email

# Load environment variables - credentials safely stored here
# NOTE the calling LLM has no access to the credentials (only access via the MCP methods)
load_dotenv()

class ProjectEmailMCP:
    def __init__(self, project_folder="INBOX/Project-Alpha"):
        self.project_folder = project_folder
        self.filters = [
            "subject:Project Alpha",
            "from:alpha-team@company.example.com",
            "to:alpha-project@company.example.com"
        ]
    
    async def search_project_emails(self, query: str, limit: int = 10):
        """Search only project-relevant emails"""
        # Connect to IMAP with project-specific folder
        # Apply pre-defined filters
        # Return only relevant emails
        pass
    
    async def get_recent_communications(self, days: int = 7):
        """Get recent project communications"""
        # Filter by date and project criteria
        pass

# Register tools with MCP server
server = MCPServer("project-alpha-emails")

@server.tool("search_project_emails")
async def search_emails(query: str, limit: int = 10):
    """Search Project Alpha emails only"""
    return await email_service.search_project_emails(query, limit)

2. Sandboxed Ticket Access

Create targeted views into your issue tracking system:

# tickets_mcp.py - Project Alpha Tickets
from mcp import MCPServer
import requests
import os

# Load environment variables - credentials safely stored here
# NOTE the calling LLM has no access to the credentials (only access via the MCP methods)
load_dotenv()

class ProjectTicketMCP:
    def __init__(self, project_id="ALPHA"):
        self.project_id = project_id
        self.base_url = "https://tickets.company.example.com"
        # Use project-specific API token with limited scope
        self.api_token = os.getenv("ALPHA_PROJECT_API_TOKEN")
    
    async def get_active_tickets(self):
        """Get tickets for this project only"""
        # Make API call with restricted scope
        # (limited in scope to self.project_id, restricted to open tickets or tickets with activity in the last month etc.)
        pass
    
    async def get_blocking_issues(self):
        """Find tickets that are blocking progress"""
        # Make API call with restricted scope - also now looking for specific types of tickets (blockers in this case)
        # (limited in scope to self.project_id, restricted to open tickets or tickets with activity in the last month etc.)
        pass

server = MCPServer("project-alpha-tickets")

@server.tool("get_blocking_issues")
async def blocking_issues():
    """Get issues blocking Project Alpha progress"""
    return await ticket_service.get_blocking_issues()

3. Safe API Sandboxing

Provide API access with project-specific credentials and environments:

// api_mcp.js - Node.js example for API access
const { MCPServer } = require('@modelcontextprotocol/server-node');
const axios = require('axios');

class ProjectAPIMCP {
    constructor() {
        // Sandbox environment for Project Alpha
        this.baseURL = 'https://alpha-staging.company.example.com/api';
        this.apiKey = process.env.ALPHA_STAGING_KEY; // Limited scope
        this.allowedEndpoints = [
            '/users', '/projects/alpha', '/metrics/alpha'
        ];
    }
    
    async makeAPICall(endpoint, method = 'GET', data = null) {
        // Validate endpoint is in allowed list
        if (!this.allowedEndpoints.some(allowed => endpoint.startsWith(allowed))) {
            throw new Error(`Endpoint ${endpoint} not allowed for this project`);
        }
        
        try {
            const response = await axios({
                method,
                url: `${this.baseURL}${endpoint}`,
                headers: { 'Authorization': `Bearer ${this.apiKey}` },
                data
            });
            return response.data;
        } catch (error) {
            return { error: error.message };
        }
    }
}

const server = new MCPServer('project-alpha-api');

server.addTool('get_project_metrics', {
    description: 'Get Project Alpha performance metrics',
    parameters: {
        type: 'object',
        properties: {
            timeframe: { type: 'string', default: '7d' }
        }
    }
}, async ({ timeframe }) => {
    return await apiService.makeAPICall(`/metrics/alpha?timeframe=${timeframe}`);
});

Building Your Project MCP Service – simply / iteratively / targetted

By building minimal/lean and targeted code you can start using the MCP service straight away. With the first tool deployed you can start coding with it’s assistance (and it may even be useful to build the next MCP service as you identify what you need).

For example…

1. Start Simple

Begin with the most critical integration – often filtered email or ticket access:

# Quick start: 30 lines to get project emails
from mcp import MCPServer
import os
import imaplib

# Load environment variables - credentials safely stored here
# NOTE the calling LLM has no access to the credentials (only access via the MCP methods)
load_dotenv()

server = MCPServer("my-project-emails")

@server.tool("project_emails")
async def get_project_emails(query: str = ""):
    # Simple IMAP connection with hardcoded project filter
    mail = imaplib.IMAP4_SSL(os.getenv('ALPHA_PROJECT_IMAP_SERVER'))
    mail.login(os.getenv('ALPHA_PROJECT_EMAIL'), os.getenv('ALPHA_PROJECT_EMAIL_PASSWORD'))
    mail.select('INBOX')
    
    search_query = f'(SUBJECT "Project Alpha" {query})'
    _, messages = mail.search(None, search_query)
    
    # Return last 5 emails
    return process_messages(messages[-5:])

if __name__ == "__main__":
    server.run()

2. Iterate Based on Need

Add features as your AI assistant encounters limitations:

  • Need API testing? Add sandboxed API calls
  • Want deployment info? Hook into your CI/CD system
  • Need document access? Add filtered file system access

3. Keep It Focused

Resist the temptation to build a comprehensive system. Each MCP service should serve one project with laser focus.

This gives your AI assistant the perfect context to help with migration planning, troubleshooting, and progress tracking.

Benefits of Project-Based MCP

Security

Rather than exposing secrets, credentials and live system details to an AI this is abstracted behind the MCP layer. The only code with access to your sensitive information is your own code (the MCP service provider).

Enhanced Accuracy

Your AI gets precisely the context it needs without distracting information from other projects.

Improved Security

Each MCP service has minimal, project-specific permissions rather than broad organizational access.

Faster Development

No need for comprehensive documentation or extensive testing – you’re building for immediate, focused use.

Reduced Cognitive Load

Both you and your AI can focus on the project at hand without information overload.

Easy Iteration

Add features as needed, deprecate when projects end, and create new services for new projects.

Conclusion

  1. Identify your most frequent project-specific queries: What do you find yourself manually searching for?
  2. Choose your first integration: Email filters are often the easiest starting point
  3. Set up basic MCP structure: Use existing MCP libraries (Python, Node.js, etc.)
  4. Add one tool at a time: Start with read-only access and consider if write access is required (higher coding responsibility) or if a sandbox can be used for write testing.
  5. Test with your AI assistant: See how the focused context improves responses
  6. Iterate based on usage: Add features when you hit limitations

Project-Based MCP represents a shift from “connect everything” to “connect exactly what you need.” By building lightweight, focused MCP services for each project, you give your AI assistant on-demand precision while maintaining security and reducing cognitive overhead.

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