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agentienceMCP SERVER

Expert Registry MCP Server

Detects the technologies in your project, picks a matching expert context, and injects it into the prompt.

by agentience ↗Prompts, Skills & Instruction PacksCommunity

LABSCO SUMMARY

Two databases, because matching and relating are different jobs.

Vector search finds an expert that means roughly what you asked for; a graph finds the ones that work together. Running both and scoring on the combination is what lets expert_find_combinations propose a team rather than a single best match. The price is real infrastructure — Neo4j alongside the embedded vector store — which is why the Docker path exists. The contexts themselves are your markdown, editable in place and reloaded without a restart.

What it is

A registry of expert contexts — markdown knowledge files scoped to a technology — with the machinery to pick the right one automatically. It scans your project to detect what it is built with, matches that against the registry using both vector similarity and graph connectivity, and injects the chosen expert's knowledge into a prompt. Registry and context files live on disk and hot-reload when you edit them.

What you get

  • expert_detect_technologies scans paths in your project to work out what it uses
  • expert_smart_discover combines vector similarity and graph connectivity to pick the best expert for a task and its constraints
  • expert_semantic_search and expert_find_similar search by meaning rather than by keyword
  • expert_load_context loads an expert's knowledge and expert_inject_context folds it into a prompt
  • expert_explore_network and expert_find_combinations walk expert relationships and propose complementary teams
  • expert_registry_list, expert_registry_get and expert_registry_search cover the plain registry lookups
  • expert_track_usage and expert_get_analytics record which expert was used and how it performed

Requirements

An expert-system directory containing registry/expert-registry.json, an expert-contexts/ folder of markdown, and performance/metrics.json — pointed at by EXPERT_SYSTEM_PATH. ChromaDB is embedded and creates its own directory; Neo4j is separate and needs NEO4J_URI and NEO4J_PASSWORD, most easily run in Docker. The README's install line names expert-registry-mcp at version 1.0.0, which PyPI does not have, and the container image it points to is not publicly available — so you clone the repository and install it with uv.

Setup effort

Build from source — git clone https://github.com/agentience/expert-registry-mcp

See installation options