AgentSpawnMCP

<p align="center"> <img src="docs/assets/agent-spawn-mcp-hero.png" alt="AgentSpawnMCP routing tasks to multiple model agents" width="100%"> </p>

Universal MCP server for any OpenAI-compatible LLM. Supports OpenAI and Anthropic API formats, cloud providers (OpenAI, Grok, Claude, Minimax, DeepSeek) and local models (Ollama, LM Studio, Jan). Built on FastMCP with pure httpx.

Quick Start — Spawn Agents

# No install needed — run directly with uvx
export MINIMAX_TOKEN=your-token
uvx agent-spawn-mcp spawn \
  --name minimax \
  --url https://api.minimax.io/anthropic/v1 \
  --token-env MINIMAX_TOKEN \
  --model MiniMax-M2.7 \
  --api-type anthropic

Or install globally:

pip install agent-spawn-mcp
agent-spawn-mcp spawn --name minimax --url https://api.minimax.io --token-env MINIMAX_TOKEN --model MiniMax-M2.7

API Types

• --api-type openai (default) — OpenAI-compatible (chat/completions)
• --api-type anthropic — Anthropic API (v1/messages)

URL versioning

Pass --url pointing directly at the API root. If your base URL already contains a version segment (e.g. …/paas/v4), the client will not re-append v1/. Examples:

Claude Code / OpenCode Integration

Add to your .mcp.json:

{
  "mcpServers": {
    "minimax-agent": {
      "command": "uvx",
      "args": ["agent-spawn-mcp", "spawn",
               "--name", "minimax",
               "--url", "https://api.minimax.io/anthropic/v1",
               "--token", "your-minimax-token",
               "--model", "MiniMax-M2.7",
               "--api-type", "anthropic"]
    },
    "claude-agent": {
      "command": "uvx",
      "args": ["agent-spawn-mcp", "spawn",
               "--name", "claude",
               "--url", "https://api.anthropic.com",
               "--token", "your-anthropic-token",
               "--model", "claude-sonnet-4-20250514",
               "--api-type", "anthropic"]
    },
    "glm-agent": {
      "command": "uvx",
      "args": ["agent-spawn-mcp", "spawn",
               "--name", "glm",
               "--url", "https://api.z.ai/api/paas/v4",
               "--token", "your-zai-token",
               "--model", "glm-5.1"]
    },
    "glm-turbo-agent": {
      "command": "uvx",
      "args": ["agent-spawn-mcp", "spawn",
               "--name", "glm-turbo",
               "--url", "https://api.z.ai/api/paas/v4",
               "--token", "your-zai-token",
               "--model", "glm-5-turbo"]
    }
  }
}

Gotchas:

• Each entry in .mcp.json must use a unique --name — it becomes the tool

name {name}_agent, and duplicates collide.

• --token on the command line is visible in ps output and some crash logs.

Prefer keeping the MCP config file read-protected (chmod 600).

Codex CLI Integration

Codex can register stdio MCP servers with codex mcp add.

For a Minimax Anthropic-compatible agent:

export MINIMAX_TOKEN=your-minimax-token

codex mcp add minimax-agent -- \
  uvx agent-spawn-mcp spawn \
    --name minimax \
    --url https://api.minimax.io/anthropic/v1 \
    --token-env MINIMAX_TOKEN \
    --model MiniMax-M2.7 \
    --api-type anthropic

For z.ai GLM through the OpenAI-compatible API:

export ZAI_TOKEN=your-zai-token

codex mcp add glm-agent -- \
  uvx agent-spawn-mcp spawn \
    --name glm \
    --url https://api.z.ai/api/paas/v4 \
    --token-env ZAI_TOKEN \
    --model glm-5.1

Verify the registration:

codex mcp list
codex mcp get minimax-agent

Restart Codex from a shell where the token env var is set. The tool exposed to Codex is named from --name, for example minimax_agent or glm_agent.

If you need Codex to store the env var with the MCP server config, add --env MINIMAX_TOKEN="$MINIMAX_TOKEN" before --. This is convenient, but it stores the secret in Codex config. Passing --token directly also works, but stores the token in the command args.

Tools Exposed

• {name}_agent(task, model?, system_prompt?, temperature?, max_tokens?, timeout?) — Spawn agent
• agent_info() — Get provider info

max_tokens behaviour

The client imposes no cap. Behaviour when max_tokens is omitted depends on --api-type:

• `openai` — the field is simply not sent; the provider falls back to its

own default (usually the model's full output budget). Reasoning models like GLM-5.x can burn huge amounts on chain-of-thought, so pass an explicit limit for short tasks.

• `anthropic` — the Anthropic API requires the field, so when you omit

it the client fills in a safe default of 16384. Override when you need more or want to cap spend:

  claude_agent(task="summarise this PR")                    # uses 16384
  claude_agent(task="exhaustive review", max_tokens=64000)  # bigger
  claude_agent(task="ping",              max_tokens=256)    # cheaper

Typical values:

Upper bound is model-specific (Claude Sonnet 4 — 64k, Opus 4 — 32k, GLM-4.5-Air — 8k, etc.). Pass max_tokens explicitly up to that limit.

Return Format

{
    "result": "...",  # Agent response text
    "metadata": {
        "provider": "minimax",
        "model_used": "MiniMax-M2.7",
        "usage": {"prompt_tokens": 100, "completion_tokens": 500},
        "latency_ms": 2340
    }
}

AgentSpawnMCP — Full Server

Full MCP server with all tools (chat, vision, files, search, agent). Requires git clone.

git clone https://github.com/sandsaber/AgentSpawnMCP
cd AgentSpawnMCP
uv sync
cp example.env .env
# Edit .env with your tokens

uv run python main.py main --provider grok

Auto-Discovery

Providers auto-detected when env var is set:

Available Tools (Full Server)

Image generation support

generate_image targets the OpenAI /images/generations request shape (model / prompt / n / image_url). Providers that expose the same shape (OpenAI dall-e-3, Grok grok-imagine-image) work out of the box.

Providers with custom image APIs are not currently supported:

• z.ai (glm-image, cogview-4-250304) expects quality / size / user_id

at /paas/v4/images/generations, not the OpenAI shape.

• Anthropic-compat endpoints don't expose image generation at all.

For these, use the provider's native HTTP API directly.

License

MIT License. Copyright (c) 2026 Michael Makarov.

See LICENSE for the full license text.