Slim Tools

Tool orchestration runtime for AI agents.

Agents should spend context on judgment, not tool catalogs and call loops. Slim Tools gives them one MCP URL with discover_tools and execute_code, then runs service discovery, branching, batching, filtering, and fan-out across MCP and OpenAPI services in a sandboxed runtime.

Your MCP client
Claude
Codex
Cursor
one MCP URL
slim.tools
tool runtime
https://slim.tools/mcp
What the agent sees
discover_toolscapability search
execute_codesandboxed code mode
Cached index
8 upstreams · 76 tools indexed
fan out at runtime
Your upstream tools
8 upstreams · OAuth handled once

Two tools for the model. A full runtime behind them.

discover_tools searches the authorized service catalog at runtime. execute_code runs sandboxed code against typed callables, with OAuth, whitelisting, output limits, logs, and tool-call metadata handled by Slim Tools.

Agent sessionPOST /mcp
3 calls · 1.45 s
  1. 1
    tools/calldiscover_tools
    query"create a github repository"
    detail"typescript"
    ↳ returns4 matches · github.create_repo et al.
    12 ms
  2. 2
    tools/calldiscover_tools
    query"create a railway project, link a github repo"
    detail"typescript"
    ↳ returns5 matches · railway.create_project, railway.link_github_repo et al.
    11 ms
  3. 3
    tools/callexecute_code
    codegithub.create_repo → railway.create_project → railway.link_github_repo
    ↳ returns{ repo: "acme/agent-demo", project: "prj_2k9c3" }
    1.42 s