MCP Hub
Agent orchestration, project knowledge and reusable experience
MCP & AI agent infrastructure
An agent needs more than access to an LLM. It needs to find the right tool, receive enough context and act within explicit permissions. I build the infrastructure around that loop. MCP Hub is the deeper platform example: integrations, project knowledge, retrieval, orchestration and reusable execution experience.
How I approach the work
Discover before loading
Expose a compact discovery surface and return the relevant capabilities with typed argument hints. Retrieve bounded knowledge fragments through lexical and embedding-based search instead of inserting an entire knowledge base into every prompt.
Control actions and results
Check provider and project access on the server, keep approval state explicit and record execution results. Orchestration needs budgets, bounded parallelism and cancellation. A tool call accepted by the transport is not proof that the business action succeeded.
Reuse experience deliberately
Store useful methods and scoped outcomes from completed work, then retrieve them for a relevant task. MCP Hub treats this as reusable knowledge and execution history, not as training the model’s weights. Compact result handling limits unnecessary context growth.
A decision to make early
Reducing the tool surface can save context, but exact token savings depend on schemas, tasks and the model. The linked Unity MCP Efficient case has a specific schema comparison; it is not a universal savings claim for every MCP Hub workflow.
Relevant work
Unity MCP Efficient
A smaller tool interface with access to the full operation catalog
Meeting-to-Actions
From spoken decisions to structured work
Tell me about your task
Describe the task the agent should complete, the systems it may access and the actions that need human approval. Include the current failure mode: tool selection, missing context, cost or unreliable execution.
Discuss the task