Give AI Assistants Access to Your Data and Systems
The Model Context Protocol connects AI assistants like Claude to your databases, APIs, and internal tools. We build custom MCP servers that transform your proprietary data into conversational intelligence.
What Is MCP?
Model Context Protocol is Anthropic’s standard for connecting AI assistants to external systems. Instead of copying data into chat windows or writing one-off integrations, MCP provides a structured way for AI to query your systems directly.
With an MCP server, your team can ask Claude questions about your data in natural language:
- “What were the key topics discussed in yesterday’s committee hearings?”
- “Show me all mentions of renewable energy in the last month”
- “Find similar projects to our current proposal”
The AI assistant calls your MCP server, retrieves relevant data, and synthesises a response.
Why Build an MCP Server?
Your Data Becomes Conversational
Databases contain answers, but query languages create barriers. MCP lets non-technical users access complex data through natural conversation.
AI Gets Current Information
Language models have knowledge cutoffs. MCP servers provide real-time access to your latest data, documents, and system state.
Workflows Become Intelligent
Beyond queries, MCP servers can execute actions: create records, trigger processes, send notifications. AI assistants become capable operators, not just information retrievers.
Integration Without Lock-In
MCP is an open protocol. Servers you build work with Claude today and potentially other AI assistants tomorrow.
Our Approach
We design MCP servers around your specific data and workflows.
Discovery
We map your data landscape: what systems contain valuable information, how that information is structured, who needs access, and what questions they’re trying to answer.
Tool Design
MCP servers expose capabilities through “tools” that AI assistants can call. We design tool interfaces that balance flexibility with safety: powerful enough to be useful, constrained enough to prevent errors.
Query Optimisation
Natural language questions don’t map directly to database queries. We build translation layers that handle fuzzy matching, name variations, date parsing, and other real-world complexity.
Deployment
MCP servers can run locally (for development and sensitive data) or as cloud functions (for team access). We deploy to your preferred infrastructure with appropriate security controls.
Capabilities
Database Access
Connect AI assistants to your databases:
- Graph databases (Neo4j) for relationship-rich data
- Document databases (MongoDB) for flexible schemas
- Relational databases (PostgreSQL) for structured records
- Vector databases for semantic search
API Integration
Bring external services into the conversation:
- REST APIs with authentication handling
- Webhook endpoints for notifications
- Third-party services (Notion, Slack, email)
- Custom internal APIs
Document Processing
Make your documents searchable and queryable:
- PDF extraction and indexing
- Structured document parsing
- Full-text search with relevance ranking
- Citation tracking back to sources
Action Execution
Move beyond retrieval to automation:
- Record creation and updates
- Workflow triggering
- Notification sending
- Report generation
Technical Foundation
Our MCP servers are built with:
- FastMCP (Python): Rapid development with FastAPI-style decorators
- MCP SDK (TypeScript): Native Node.js implementation for JavaScript ecosystems
- AWS Lambda: Serverless deployment for team access
- OpenAI Embeddings: Semantic search for meaning-based queries
- Robust Error Handling: Retry logic, connection pooling, graceful degradation
Example: Parliamentary Data MCP Server
We built an MCP server that gives AI assistants structured access to Italian parliamentary data:
Tools provided:
- Assembly queries: recent sessions, speaker activity, debate analysis
- Commission queries: committee debates, hearing transcripts
- Document search: bills, amendments, parliamentary questions
- Government sources: ministry RSS feeds and announcements
- Direct Cypher queries for complex analysis
Features:
- Handles speaker name variations (prefixes, name order, case)
- Generates URLs to official parliamentary sources
- Tracks citations for transparency
- Optimised for Lambda with connection pooling
This server powers daily workflows for public affairs professionals who query parliamentary data through natural conversation rather than complex database interfaces.
Who This Is For
- Organisations with valuable data locked in databases
- Teams frustrated by query complexity
- Companies wanting AI assistants that understand their context
- Anyone building conversational interfaces to internal systems
Getting Started
MCP server development starts with understanding your data and workflows. We’ll discuss:
- What data do you want AI assistants to access?
- What questions do your teams ask most often?
- What actions should be possible through conversation?
- What security constraints apply?
From there, we scope and build a server tailored to your needs.