User Context MCP Server
Provides tools for managing user context and integrates with Hermes to bootstrap subagents with local guidelines and environment facts.
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., "@User Context MCP Serverget my full user context"
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.
User Context MCP Server
A local stdio Model Context Protocol (MCP) server that exposes a developer's local context (communication preferences, development stack, active repositories, memory models) to AI agents.
This allows agentic workflows (such as subagents or orchestrators) to bootstrap themselves with local guidelines and environment facts, minimizing context hallucination.
Features
Exposes context sections as MCP tools and resources.
Provides a subagent bootstrap helper tool.
Supports externalizing context data via the
USER_CONTEXT_ROOTenvironment variable.
Related MCP server: personal-context
Setup
1. Clone & Install Dependencies
git clone https://github.com/yourusername/user-context-mcp.git
cd user-context-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt2. Configure Your Personal Context
The repository contains generic template files under context/. Copy them to create your active, ignored personal context files:
cp context/communication.md.example context/communication.md
cp context/memory-model.md.example context/memory-model.md
cp context/repos.md.example context/repos.md
cp context/stack.md.example context/stack.mdThen edit the .md files to match your local setup. These files are listed in .gitignore so they won't be committed to your repository.
Alternatively, you can keep your active context files in a private directory completely outside the repository by setting theUSER_CONTEXT_ROOT environment variable:
export USER_CONTEXT_ROOT="$HOME/.config/user-context"3. Inspect and Run the Server
You can inspect the server tools and call them locally using fastmcp:
fastmcp inspect server.py:mcp
fastmcp call server.py get_context section=stack --jsonIntegration with Agentic Systems (e.g. Hermes)
You can register this MCP server with your local agent client or shell extension (such as Hermes).
For Hermes, register using the stdio transport:
# Register (interactive — pipe y to auto-accept):
printf 'y\n' | hermes mcp add user_context \
--command /path/to/user-context-mcp/.venv/bin/python \
--args /path/to/user-context-mcp/server.pyNote: Replace /path/to/user-context-mcp with the absolute path to your cloned repository.
Start a new session (or run /reload-mcp + /reset) so the tools load. Tools will appear with the mcp_user_context_ prefix (e.g., mcp_user_context_get_context).
Tools
Tool | Purpose |
| List available context sections and check whether files exist on disk. |
| Retrieve the markdown content of a specific section (or |
| Get bootstrap instructions suitable for pasting into subagent contexts. |
Resources
user-context://communicationuser-context://stackuser-context://reposuser-context://memory-modeluser-context://full
Optional: Syncing to Hermes Skill
If you use Hermes' local skill directory to cache delegation contexts, you can sync your edits using the included helper script:
.venv/bin/python scripts/sync_to_hermes_skill.py --dry-runBy default, this writes to ~/.hermes/skills/software-development/delegation-user-context/SKILL.md.
HTTP Transport (Optional)
To run the server as an HTTP service:
fastmcp run server.py:mcp --transport http --host 127.0.0.1 --port 8765This server cannot be deployed
Maintenance
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One shared context your team's AI tools read & write over MCP. No re-explaining. Free.
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