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Connects AI agents to Conduit data streams for ingesting events, querying data, and managing schemas. It enables seamless integration between services and agent
Connects AI agents to Conduit data streams for ingesting events, querying data, and managing schemas. It enables seamless integration between services and agents with real-time forwarding and automated schema detection.
MCP server for Conduit — connect any AI agent to your data streams.
📖 API Docs · 🌐 Website · 📦 npm · 💻 GitHub
Get an API key from platform.usecondu.it/tokens, then add to your AI editor config:
Add to ~/.claude/settings.json:
{
"mcpServers": {
"conduit": {
"command": "npx",
"args": ["-y", "@useconduit/mcp"],
"env": {
"CONDUIT_API_KEY": "conduit_sk_..."
}
}
}
}
Add to .cursor/mcp.json in your project:
{
"mcpServers": {
"conduit": {
"command": "npx",
"args": ["-y", "@useconduit/mcp"],
"env": {
"CONDUIT_API_KEY": "conduit_sk_..."
}
}
}
}
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"conduit": {
"command": "npx",
"args": ["-y", "@useconduit/mcp"],
"env": {
"CONDUIT_API_KEY": "conduit_sk_..."
}
}
}
}
| Variable | Required | Default | Description |
|---|---|---|---|
CONDUIT_API_KEY |
✅ | — | Your Conduit API key |
CONDUIT_API_URL |
— | https://api.usecondu.it |
Custom API endpoint |
| Tool | Description |
|---|---|
conduit_list_streams |
List all data streams |
conduit_get_schema |
Get schema for a stream (columns, types, codecs) |
conduit_create_stream |
Create a new stream |
conduit_ingest |
Send events to a stream |
conduit_list_events |
Query events with pagination & time range filters |
conduit_add_forward |
Add a forwarding destination (HTTP, MQTT, or WebSocket) with auth options |
conduit_stream_stats |
Get ingestion statistics (event count, rate, latency) |
conduit_analyze_schema |
Analyze a JSON payload for optimal schema |
conduit_feedback |
Submit feedback to the Conduit team |
conduit_backfill |
Replay historical events to forwarding destinations |
conduit_backfill_status |
Check backfill job progress or list all jobs |
conduit_time_range |
Get earliest/latest event timestamps for a stream |
| URI | Description |
|---|---|
conduit://streams |
All streams |
conduit://streams/{name} |
Stream details + schema |
conduit://stats |
Platform-wide statistics |
Full interactive API docs are available at api.usecondu.it/docs (powered by Scalar).
Key endpoints:
| Method | Endpoint | Description |
|---|---|---|
POST |
/v1/{tenant}/{stream} |
Ingest events (any protocol: HTTP, WebSocket, MQTT) |
GET |
/api/v1/streams |
List streams |
GET |
/api/v1/streams/{name} |
Stream details + schema |
GET |
/api/v1/streams/{name}/events?from=&to=&limit= |
Query events with time range |
GET |
/api/v1/streams/{name}/stats |
Ingestion statistics |
GET |
/api/v1/streams/{name}/schema-history |
Schema evolution history |
POST |
/api/v1/streams/{name}/forwards |
Add forwarding destination |
POST |
/api/v1/streams/{name}/backfill |
Start backfill job |
GET |
/api/v1/streams/{name}/backfill |
List backfill jobs |
GET |
/api/v1/streams/{name}/time-range |
Event time range |
POST |
/api/v1/tokens |
Create API token |
GET |
/api/v1/account |
Account info + usage |
Machine-readable API specs:
Conduit is the lightweight data layer between your services. Send any JSON — schemas aren't defined, they emerge. And they evolve as your sources do.
Agent: "I see live sensor data flowing in. I'll pipe it to our datawarehouse and add hot storage."
▸ conduit_list_events(stream: "sensors", limit: 1000)
↳ 1,000 events · 18.4 MB raw → 1.1 MB stored (94% compressed)
▸ conduit_add_forward(stream: "sensors", type: "http", url: "https://dwh.acme.io/ingest")
↳ Destination added · forwarding to datawarehouse
▸ conduit_add_forward(stream: "sensors", type: "mqtt", broker: "mqtt://redis.acme.io", topic: "hot/sensors")
↳ Destination added · 2 destinations active
▸ conduit_backfill(stream: "sensors", from: "2026-02-24", to: "2026-03-02")
↳ Backfilling 89,929 events from last 7 days
Learn more at usecondu.it
MIT
Выполни в терминале:
claude mcp add conduit-mcp -- npx Web content fetching and conversion for efficient LLM usage.
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