xmcp-core
Click on "Install 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., "@xmcp-coresearch my bookmarks for AI papers"
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.
xmcp-core
xmcp-core is a generic X/bookmark intelligence MCP core. It lets an operator
bring their own X account, ingest their own bookmark corpus, classify and enrich
that corpus, embed it, retrieve across it, generate briefs, scan academic paper
feeds, and expose the result through MCP tools.
What this is:
Corpus-agnostic bookmark intelligence pipeline plumbing.
MCP tools for local intel search, semantic retrieval, ingestion, enrichment, briefs, paper radar, and model registry checks.
Optional generated X API MCP tools from X's OpenAPI surface when credentials and network access are available.
Public starter configuration with environment hooks for paths, topics, watchlists, and credentials.
What this is not:
It is not a private intel corpus.
It does not ship runtime
db/,intel/,logs/,drop/, orstate/.It does not ship OAuth tokens, cached X data,
.env, secrets, private wiki promotion code, or private audit documents.It does not include the deferred wiki promoter module. The wiki-coupled tools
intel_domains,intel_stale_domains, andintel_promoteare intentionally absent from this MVP.
Setup
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt -r requirements-dev.txt
cp env.example .envFill .env or ~/.config/xmcp/secrets.env with your own credentials. Keep all
secret values out of git. The tracked env.example contains empty placeholders
only.
For bookmark ingest, create an X app with OAuth 2.0 user auth and run:
.venv/bin/python scripts/x-oauth-setup.pyFor the optional hosted X MCP bridge, install/configure xurl, preferably with
a separate X app/client ID from the bookmark pipeline. See CONNECT.md.
Configuration
Useful environment hooks:
XMCP_DB_PATH: SQLite database path. Defaults todb/bookmarks.db.XMCP_INTEL_DIR: generated JSONL corpus path. Defaults tointel/.XMCP_LOG_DIR: logs path. Defaults tologs/.XMCP_TOPICS_FILE: JSON list or{ "topics": [...] }file.XMCP_WATCHLIST_PATH: watchdog keyword file. Defaults tostate/watchlist.json.XMCP_OAUTH_TOKEN_PATH: explicit OAuth2 token file path.XMCP_INCLUDE_X_API=0: build the MCP server with only local intel/model tools.
Examples live in examples/. To customize topics:
export XMCP_TOPICS_FILE="$PWD/examples/topics.default.json"To enable watchlist alerts, create an untracked runtime file:
mkdir -p state
cp examples/watchlist.example.json state/watchlist.jsonRun
Run the local MCP server:
.venv/bin/python server.pyRun the pipeline manually:
.venv/bin/python -m pipeline.run --dry-run
.venv/bin/python -m pipeline.runRun tests:
pytestLive corpus tests are opt-in with XMCP_RUN_LIVE_TESTS=1.
IRIN satellite
xmcp-core is an IRIN satellite: generic MCP plumbing that an operator can run
next to IRIN, not a private corpus and not a required IRIN dependency. When
IRIN's Sheldon validator is enabled and a local xmcp server is reachable and
exposes the generated X tool searchPostsRecent, IRIN uses only that tool for
live or recent X posts. Sheldon does not use this repo's bookmark corpus or
hybrid intel tools for validation, and IRIN does not claim Gateway transport supplies
search. If no local xmcp server is reachable, IRIN just proceeds without that
evidence path — this server is optional, not a hard dependency. It also runs
fully standalone for operators who never use IRIN. See CONNECT.md
for local vs hosted X MCP routing.
License
Apache-2.0 — same as the IRIN triad repos. See LICENSE.
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