spiderswitch for ai-lib Ecosystem

MCP (Model Context Protocol) server that enables agents to dynamically switch AI models from the ai-lib ecosystem.

Features

• Protocol-Driven: All model configurations loaded from ai-protocol manifests (ARCH-001)

• Multi-Provider Support: Switch between OpenAI, Anthropic, Google, DeepSeek, and more

• Runtime-Agnostic: Uses ai-lib-python SDK for unified model interaction

• MCP-Compliant: Implements standard MCP tools over stdio transport

• Capability Discovery: Query available models and their capabilities

• Runtime Profile Signal: Exposes runtime capability profile for upper-layer routing policy engines

• Local Readiness Hints: list_models includes API key presence and proxy readiness per provider

• Explicit Exit Path: exit_switcher resets switcher runtime/state for clean fallback

• Auto Protocol Setup: Auto-detects local ai-protocol path and sets AI_PROTOCOL_PATH for current process

• Official Dist Sync: Best-effort sync of official dist/v1/*.json snapshot into local ai-protocol/dist/v1

Related MCP server: Multi-LLM Gateway MCP

Quick Start

Installation

# Clone the repository
git clone https://github.com/ailib-official/spiderswitch.git
cd spiderswitch

# Install dependencies
pip install -e .

Environment Setup

Set up your API keys:

export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export GOOGLE_API_KEY="..."

Recommended provider key mapping:

Security note:

• Prefer environment variables over passing api_key in tool arguments.

• The server redacts sensitive fields in logs, but passing secrets in arguments still increases exposure risk in client traces.

Optional runtime env controls:

• SPIDERSWITCH_SYNC_ON_INIT=1 to enable dist sync during runtime initialization (default: disabled).

• SPIDERSWITCH_SYNC_DIST=0 to disable dist sync when it is explicitly invoked.

• AI_PROTOCOL_DIST_BASE_URL to override raw dist source (default official GitHub raw URL).

• AI_PROTOCOL_DIST_API_BASE_URL to override GitHub API listing source for models/providers dist json.

• SPIDERSWITCH_LIST_CACHE_TTL_SEC for list_models cache TTL (default: 5).

• SPIDERSWITCH_STATUS_CACHE_TTL_SEC for get_status cache TTL (default: 2).

• AI_HTTP_TRUST_ENV=1 so ai-lib-python (used after switch_model) forwards standard proxy env vars to its HTTP client; without this, HTTP_PROXY / HTTPS_PROXY may be ignored by the SDK (see ai-lib-python transport docs).

One-Click Install (Plugin-Market Style)

bash scripts/install_one_click.sh

Then generate MCP client config template:

spiderswitch init --client cursor --output ~/.cursor/mcp.spiderswitch.json --force
spiderswitch doctor --json

Offline Install (air-gapped/intranet)

Install from local wheel or local source directory:

bash scripts/install_offline.sh /path/to/spiderswitch-0.4.0-py3-none-any.whl
# or
bash scripts/install_offline.sh /path/to/spiderswitch-source

Configuration

Add to your MCP client configuration:

For OpenCode

Configuration file: ~/.config/opencode/opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "spiderswitch": {
      "type": "local",
      "command": ["python3", "-m", "spiderswitch.server"],
      "enabled": true,
      "environment": {
        "AI_PROTOCOL_PATH": "/path/to/ai-protocol",
        "OPENAI_API_KEY": "sk-your-key",
        "ANTHROPIC_API_KEY": "sk-ant-your-key",
        "DEEPSEEK_API_KEY": "sk-your-key"
      }
    }
  }
}

For Claude Desktop / Cursor

Configuration file: ~/.config/claude-desktop/config.json or ~/.cursor/mcp.json

{
  "mcpServers": {
    "spiderswitch": {
      "command": "python3",
      "args": ["-m", "spiderswitch.server"],
      "env": {
        "AI_PROTOCOL_PATH": "/path/to/ai-protocol"
      }
    }
  }
}

Verification (OpenCode)

# List loaded MCP servers
opencode mcp list

# Expected output:
# ✓ spiderswitch connected
#   python3 -m spiderswitch.server

Usage

In your agent, call MCP tools:

# List available models
models = await mcp_client.call_tool("list_models", {})

# Switch to Claude 3.5 Sonnet
await mcp_client.call_tool(
    "switch_model",
    {"model": "anthropic/claude-3-5-sonnet"}
)

# Check current status
status = await mcp_client.call_tool("get_status", {})

Available MCP Tools

1. switch_model

Switches to a different AI model/provider.

Parameters:

• model (string, required): Model identifier (e.g., openai/gpt-4o, anthropic/claude-3-5-sonnet)

• api_key (string, optional): Explicit API key (overrides environment variable; not recommended for production)

• base_url (string, optional): Custom base URL for testing/mock

• runtime_id (string, optional): Runtime target selected by upper-layer policy

Returns:

{
  "status": "success",
  "data": {
    "id": "anthropic/claude-3-5-sonnet",
    "provider": "anthropic",
    "capabilities": ["streaming", "tools", "vision"],
    "proxy_status": {
      "provider": "anthropic",
      "proxy_required_guess": false,
      "proxy_configured": false,
      "configured_proxy_env_vars": [],
      "hint": null
    },
    "warnings": []
  },
  "message": "Successfully switched to anthropic/claude-3-5-sonnet"
}

2. list_models

Lists all available models from registered providers.

Parameters:

• filter_provider (string, optional): Filter by provider ID

• filter_capability (string, optional): Filter by capability (streaming, tools, vision, embeddings, audio)

• runtime_id (string, optional): Runtime target selected by upper-layer policy

Returns:

{
  "status": "success",
  "data": {
    "count": 2,
    "runtime_profile": {
      "runtime_id": "python-runtime",
      "language": "python",
      "supports": ["model_switching", "capability_filtering", "provider_manifest_loading"]
    },
    "models": [
      {
        "id": "openai/gpt-4o",
        "provider": "openai",
        "capabilities": ["streaming", "tools", "vision"],
        "api_key_status": {
          "provider": "openai",
          "has_api_key": true,
          "expected_env_vars": ["OPENAI_API_KEY"],
          "configured_env_vars": ["OPENAI_API_KEY"]
        },
        "proxy_status": {
          "provider": "openai",
          "proxy_required_guess": true,
          "proxy_configured": false,
          "configured_proxy_env_vars": [],
          "hint": "This provider may require proxy access in your network region. Set HTTPS_PROXY/HTTP_PROXY in the MCP server process environment if needed."
        }
      },
      {
        "id": "anthropic/claude-3-5-sonnet",
        "provider": "anthropic",
        "capabilities": ["streaming", "tools", "vision"]
      }
    ],
    "filtered": {
      "require_api_key": false,
      "provider": null,
      "capability": null
    }
  }
}

3. get_status

Gets current model status and configuration.

Parameters:

• runtime_id (string, optional): Query status in a specific runtime scope

Returns:

{
  "status": "success",
  "data": {
    "provider": "anthropic",
    "model": "claude-3-5-sonnet",
    "capabilities": ["streaming", "tools", "vision"],
    "runtime_profile": {
      "runtime_id": "python-runtime",
      "language": "python",
      "supports": ["model_switching", "capability_filtering", "provider_manifest_loading"]
    },
    "is_configured": true,
    "connection_epoch": 3,
    "last_switched_at": "2026-03-02T09:00:00+00:00"
  }
}

4. exit_switcher

Explicitly reset spiderswitch state and runtime client.

Parameters:

• runtime_id (string, optional): Runtime id for scoped reset

• scope (string, optional): all (default) or runtime

Returns:

{
  "status": "success",
  "data": {
    "exited": true,
    "status": {
      "provider": null,
      "model": null,
      "is_configured": false
    }
  }
}

API Key Guidance and Troubleshooting

When switch_model fails due to missing credentials, the response includes:

• provider: which provider is missing credentials

• expected_env_vars: accepted environment variable names

• hint: actionable setup instruction

Typical setup flow:

1. Configure provider key in your MCP server process environment.

2. Restart the MCP server process if your client does not support hot env reload.

3. Call switch_model.

4. Verify with get_status.

Connection Coordination with Agent Runtime

This MCP server manages model client lifecycle internally. To avoid conflicts with an agent's own connection manager:

• Treat MCP switcher as the control plane for model selection.

• Let the agent side observe get_status.connection_epoch.

• Rebuild agent-side cached sessions only when connection_epoch increases.

This pattern prevents stale session reuse after model switches and supports deterministic synchronization.

Runtime Routing Boundary

spiderswitch only executes routing actions with explicit runtime signals:

• Runtime capability model is exposed via runtime_profile (runtime-neutral schema).

• Runtime selection policy remains in upper-layer applications.

• Built-in registry/resolver only resolves runtime_id and does not implement cost/quality/business strategy.

Architecture

spiderswitch/
├── src/
│   ├── server.py           # MCP server main entry point
│   ├── tools/              # MCP tool implementations
│   │   ├── switch.py       # switch_model tool
│   │   ├── list.py         # list_models tool
│   │   ├── status.py       # get_status tool
│   │   └── reset.py        # exit_switcher tool
│   ├── runtime/            # Runtime abstraction layer
│   │   ├── base.py         # Base runtime interface
│   │   ├── python_runtime.py  # ai-lib-python implementation
│   │   └── loader.py       # ProtocolLoader wrapper
│   └── state.py            # State management
├── tests/                  # Test suite
└── pyproject.toml          # Project configuration

Development

Running Tests

# Install test dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=src/spiderswitch

Testing with Mock Server

Use ai-protocol-mock:

# Start mock server
docker-compose up -d ai-protocol-mock

# Run with mock
MOCK_HTTP_URL=http://localhost:4010 python -m spiderswitch.server

Code Style

# Format code
ruff format src tests

# Lint
ruff check src tests

# Type check
mypy src

Protocol-Driven Design (ARCH-001)

This server follows the ai-lib design principle:

一切逻辑皆算子，一切配置皆协议

All provider configurations are loaded from ai-protocol manifests. No provider-specific logic is hardcoded. Adding a new provider requires only a manifest file in ai-protocol.

Routing boundary:

• spiderswitch exposes runtime/model capability signals only.

• Routing strategy policy (cost/latency/circuit-breaker/business rules) belongs to upper-layer applications.

Deterministic routing contract:

• runtime resolution order is fixed as request runtime_id -> active state runtime_id -> default runtime.

• reset supports scoped behavior (scope=runtime) to clear a target runtime without global teardown.

• contract tests in tests/test_runtime.py verify resolver order and scoped reset stability.

Related Projects

• ai-protocol - Protocol specification

• ai-lib-python - Python runtime SDK

• ai-lib-rust - Rust runtime SDK

• ai-lib-ts - TypeScript runtime SDK

License

This project is licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)

• MIT License (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contributing

Contributions are welcome! Please ensure:

1. Code follows PEP 8 and passes ruff check

2. Type hints pass mypy --strict

3. Tests are included for new features

4. Documentation is updated

spiderswitch - Where MCP meets ai-lib. 🤖🔀