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MCP Server Development
Anthropic's Model Context Protocol is an open standard that lets AI assistants and agents connect to external data sources and tools through a consistent interface. Instead of building a separate tool integration for every agent framework you use, you build one MCP server and any compliant agent can use it.
We build production MCP servers for companies whose internal systems need to be accessible to AI agents, databases, internal APIs, SaaS platforms, and proprietary data stores. The output is a server your agents talk to, a Docker image you deploy, and documentation your team can work from.
Tell us what you're building.
An MCP server can expose tools, resources, and prompt templates. Most projects use all three to give agents a complete picture of the system they're working with.
Purpose-built servers that expose your internal APIs and databases to AI agents through MCP's standard interface: tools, resources, and prompts.
Make your documents, records, or knowledge base readable by any compliant agent. Resources are read-only data the agent can pull into context on demand.
Give agents the ability to take actions: create records, trigger workflows, run queries, send notifications. Each tool includes input validation and structured error handling.
Pre-built prompts tuned to your domain and data model. The agent receives context-appropriate instructions without requiring prompt engineering on the client side.
SSO integration and per-user context scoping so agents only access the data the logged-in user is permitted to see. OAuth, API keys, and JWT patterns all supported.
Docker image, serverless function, or managed container, whichever fits your stack. We build, test, and document it. You operate it.
Build once. Every MCP-compliant host can use it. You don't write a new integration when you add a new agent framework.
Claude Desktop
Anthropic's desktop client supports MCP natively as of late 2024.
Cursor
MCP tool calls work inside the Cursor editor for code and data tasks.
Claude.ai (Projects)
Claude.ai with Projects can connect to MCP servers for persistent context.
LangChain / LangGraph
Both frameworks have MCP adapters that translate tool calls to MCP wire format.
CrewAI
CrewAI agents can connect to MCP servers through its tool interface.
AutoGen
Microsoft's AutoGen supports MCP tool servers for multi-agent workflows.
Custom tool calls work fine when you have one agent framework and one internal system. They get expensive when you have three agent frameworks and four internal systems, that's twelve integration surfaces to maintain, each with its own schema, error handling, and versioning.
With an MCP server, you maintain one interface. The server defines what tools, resources, and prompts exist. Any MCP-compatible host reads that definition and knows how to use them. When you add a new tool to the server, every connected agent gets it. When a provider updates its model, the server doesn't change.
The trade-off is real: MCP is more infrastructure to set up than a direct tool call, and it adds latency. For teams running multiple agents against multiple internal systems, that trade-off is worth it. For a single agent against a single system, probably not.
MCP is the right tool for specific situations. Here's when it's not the right call.
Teams using a single framework with existing integrations
If you only use LangChain and it already has a direct integration with your internal system, the MCP abstraction layer adds overhead without much benefit. MCP earns its keep when you need one server to serve multiple agent frameworks.
Projects requiring sub-50ms tool call latency
MCP has a round-trip overhead of 50–150ms per tool call. For most use cases that's fine. For real-time applications where speed is the primary constraint, a direct integration will be faster.
Quick proof-of-concept work
MCP is production infrastructure: authentication layers, transport security, versioned interfaces. It's not the right choice for a two-day prototype. Build the prototype first, then we can wrap it in MCP when it's ready to scale.
Describe the systems you want to make accessible to AI agents. We'll reply within one business day with a rough scope and price range, no commitment required.