AI
MCP Deep Dive, Part 5: Designing Custom MCP Tools Your Agents Actually Use Right
Most MCP tools mirror your REST API and confuse the model. Here's how to design task-shaped tools an agent picks right, calls cleanly, and trusts.
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LLMs, RAG, vector search, Azure OpenAI, and intelligent agents for .NET teams.
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Most MCP tools mirror your REST API and confuse the model. Here's how to design task-shaped tools an agent picks right, calls cleanly, and trusts.
The server exposes tools; the client drives them. Here's a production MCP client — discovery, the agent loop, multi-server routing, and reconnects.
A demo MCP server is a weekend; a production one needs typed tools, real error handling, pagination, and health checks. The Mattrx build, end to end.
MCP has a host, clients, servers, three primitives, and a capability handshake. Here is the full architecture, mapped onto Mattrx's real Azure setup.
Every AI agent you build needs every backend you own — that is N×M glue code. MCP turns it into N+M. Here is why, with real Mattrx production numbers.
Naive AI review drowns devs in false positives until they ignore it. Here's the context-aware, adversarially-verified pipeline we run on every Mattrx PR.