Agent Cascade

agent-cascade (MCP server)

Overview

agent-cascade bridges Model Context Protocol (MCP) clients to a local language model endpoint (e.g., LM Studio, Ollama-compatible servers). It exposes a single chat-completion tool so you can route requests from Windsurf/Cascade directly to your locally hosted model without relying on hosted APIs.

Supported capabilities

• local_chat MCP tool - forwards prompts to an LM Studio-compatible /chat/completions endpoint and returns the response to the client.
• local.chat request handler - optional direct method (for clients that support invoking custom MCP methods) with the same semantics as the tool.

Not included

Autonomous "continue working" loops are not part of this package. The earlier documentation referenced cascade.auto, but that behaviour lives in a different tool and is not shipped here.

Environment configuration

| Variable | Default value | Purpose | |-----------------|----------------------------|---------------------------------------------------------| | LM_BASE_URL | http://10.5.0.2:11434/v1 | Base URL for the local LM Studio/Ollama-compatible API. | | DEFAULT_MODEL | qwen2.5-coder | Model used when the caller does not supply one. |

Note: By default, no token limit is imposed on responses. If you need to limit response length, specify max_tokens when calling the tool.

Installation & build

cd tools/agent-cascade
npm install
npm run build

This compiles src/server.ts into dist/server.js, which is the entry point you reference from your MCP client configuration.

Windsurf/Cascade configuration

1. Open Cascade panel > Plugins > Manage > View raw config.
2. Insert (or update) the server block below and save.
3. Refresh the Cascade window so the new server loads.

{
  "mcpServers": {
    "agent-cascade": {
      "command": "node",
      "args": ["./dist/server.js"],
      "env": {
        "LM_BASE_URL": "http://10.5.0.2:11434/v1",
        "DEFAULT_MODEL": "qwen2.5-coder"
      },
      "disabled": false,
      "disabledTools": []
    }
  }
}

> Tip: For a portable setup, build first and then point to the absolute path of your locally built dist/server.js. Some environments do not expand ${workspaceFolder}.

Self‑Ask / Reflection (Same‑Model Sub‑Calls)

Yes — you can point this tool at the very same local model the client is using and have it "ask itself." Nothing special is required on the server side: agent-cascade is a thin proxy to your /chat/completions endpoint. To keep this safe and predictable, make the reflection a separate, budgeted sub‑call with depth caps and short outputs that you enforce in your orchestrator/agent logic.

• Use small budgets: set max_tokens to a low value (e.g., 64–256) and a short timeout_ms.
• Keep it concise: pass a system prompt that requires terse outputs.
• Control recursion in the caller: enforce a max “reflection depth” in your agent; the server does not loop on its own.

Example sub‑call via MCP tools/call:

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "local_chat",
    "arguments": {
      "system": "You are a terse reviewer. Reply in <=5 short bullets.",
      "prompt": "Reflect on the draft plan; list obvious risks only.",
      "max_tokens": 128,
      "temperature": 0.1,
      "timeout_ms": 10000
    }
  }
}

This snippet is drop‑in and safe: it’s just another call to your local model with a tight budget and constraints. If you’re performing multi‑step workflows, track your own depth/budget in the caller and stop when limits are hit.

Demo

The screenshot below shows agent-cascade in action, successfully routing a chat completion request through Windsurf/Cascade to a local language model:

Example: The local_chat tool responding with "Hello! I'm here and ready to help you with any coding questions or tasks you might have."

Troubleshooting

• Timeouts or empty responses - increase timeout_ms when invoking the tool or ensure the model is loaded in LM Studio.
• HTTP errors - the server surfaces the upstream status code and the first 500 characters of the body to highlight configuration issues.
• Model not found - either pass the model field in the tool call or update DEFAULT_MODEL to match a model served by your local endpoint.

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: BlinkZer0
• Source: BlinkZer0/Agent-Cascade
• License: MIT

Install and usage instructions live in the source repository linked above.