Conduit

AI agent optimization

Reduce tool definition bloat when adding multiple MCP servers to agents like Claude or Cursor.

Multi-client tool management

Point multiple AI tools at a single gateway instead of configuring each client separately.

Secrets management

Store API keys in the OS keychain and inject them at runtime, avoiding client config or cloud storage.

Destructive tool governance

Toggle off all dangerous tools fleet-wide with one switch, preventing accidental agent actions.

Observability and debugging

Monitor per-server latency, error rates, and audit every tool call directly from the app.

Local-first development

Run MCP servers as host processes without Docker or cloud infrastructure.

Meta-tool architecture

Exposes three searchable meta-tools instead of hundreds of raw tool definitions, keeping agent context flat.

Token reduction

Measured 97% less tool overhead per request and ~90% fewer tokens overall, with the same task success rate.

Multi-client gateway

Point each AI tool at Conduit once; it fans out to every server you manage with hot toggles and no restarts.

OS keychain secrets

API keys live in your OS keychain and are injected at runtime, never in client configs or the cloud.

Per-tool governance

Toggle any individual tool on or off; one switch hides every destructive tool from all clients fleet-wide.

Live observability

View per-server latency, error rates, and a full audit trail of every tool call built into the app.

Native desktop app

Runs as a local app with no Docker or cloud dependencies; supports local servers as host processes and remote server proxies.

Tool invocation testing

Invoke any tool and see the raw result before an agent ever calls it.