circuit-context
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@circuit-contextSearch for return path guidelines in PCB layout"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Circuit Context
Source-backed hardware engineering guidance for engineers and AI assistants. Search recommendations, read their conditions and exceptions, and follow citations to the publisher. Covers schematics, PCB layout, interfaces, EMI/EMC and mechanical integration.
Start
Requires Python 3.10 or newer.
python -m pip install ".[mcp]"
circuit-context serveserve runs a read-only MCP server over stdio. Configure an MCP client to run
circuit-context with the argument serve. It exposes:
Interface | Purpose |
| Find bounded, cited summaries with explicit filters |
| Read one guide's inputs, verification, limitations and sources |
| Browse coverage, IDs, filter values and source editions |
For example, search {"query": "return path", "domain": "pcb"}, then read a
returned ID with get_guideline. A two-layer review can start directly with
{"id": "two-layer-emc-workflow"}.
Related MCP server: Security Context MCP Server
Other ways to use it
circuit-context search "return path" --domain pcb
circuit-context get two-layer-emc-workflow
circuit-context cataloguefrom circuit_context import get_guideline, search_guidelines
hits = search_guidelines("termination", interface="can")
guide = get_guideline("can-end-termination")The base package supports Python and CLI retrieval without FastMCP. Install the
mcp extra for the server. The AI client performs reasoning and uses separate CAD
tools for design work; this package does not place parts, route boards or generate
answers with an internal LLM.
Knowledge and retrieval
The corpus has 161 editorial guides and 77 source records, expanded from KiCadFlow's engineering guidance. JSON is the maintained source; SQLite FTS5/BM25 provides offline keyword retrieval by default. Optional local semantic search handles paraphrases; hybrid search combines both using reciprocal rank fusion. An optional cross-encoder reranks the candidates.
python -m pip install ".[mcp,search]"
circuit-context prepare-search --reranker
circuit-context search "Where does returning current go when a signal changes layers?" --method hybridModel downloads happen only during explicit preparation. Queries run locally.
Use --rerank to enable the second stage, or MCP arguments
{"query": "...", "method": "hybrid", "rerank": true}. Neither semantic
similarity nor reranking establishes that the corpus contains an answer.
See setup and measured tradeoffs.
Applicability, required inputs, verification, limitations and source editions stay attached to each guide. Missing coverage is reported. The corpus has not undergone independent hardware-engineer review and does not establish product compliance.
The generated index lives in ~/.circuit-context/cache/; override it with
CIRCUIT_CONTEXT_DIR. It contains guidance, not user queries or project files.
Development
python -m pip install -e ".[mcp,dev,ingest,search]"
python -m ruff check .
python -m mypy
python examples/scripts/pcb_knowledge.py
python examples/scripts/hybrid_contract.py
python -m buildThe example checks actual MCP retrieval, citations, bounds and cache recovery in both protocol modes. Its prompt fixtures also describe AI expectations; passing retrieval checks does not measure model reasoning or hardware outcomes.
Publisher captures, extracted Markdown, page images and generated reports stay
under ignored out/. They are not distributed with the package.
AGPL-3.0-only. See LICENSE and provenance.
This server cannot be deployed
Maintenance
Related MCP Connectors
Page-cited retrieval for embedded docs, datasheets, MISRA, CMSIS, and RTOS references.
Run, build, and validate firmware on virtual hardware from your AI agent. Hardware knowledge corpus.
Versioned documentation registry and semantic search for AI tools and coding assistants.
- KumbukaOAuthai.kumbuka
Governed, auditable knowledge your team curates for its AI assistants, self-hostable
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