mcp-second-opinion 0.1.1

Let your MCP-aware agent consult rival LLMs (OpenAI, Gemini, Anthropic, Grok) mid-conversation.

mcp-second-opinion

Let your MCP-aware agent consult rival LLMs mid-conversation. One simple MCP server, four providers — OpenAI, Google Gemini, Anthropic, xAI/Grok — unified behind two tools.

Install

pip install mcp-second-opinion

or with uv:

uv add mcp-second-opinion

Configure

Set at least one of these environment variables:

Variable	Provider
OPENAI_API_KEY	OpenAI
GEMINI_API_KEY	Google Gemini
ANTHROPIC_API_KEY	Anthropic
XAI_API_KEY	xAI/Grok

Providers without a key set are gracefully disabled — they appear in panel responses with a friendly error field rather than crashing the server.

Optional:

Variable	Default	Purpose
MCP_SECOND_OPINION_PROFILE	flagship	flagship | balanced | cheap — controls which model each provider uses in ask_the_panel.
MCP_SECOND_OPINION_SELF_SKIP	unset	Provider key to omit from panel (openai, gemini, anthropic, grok). Useful when the host is itself one of the panelists.
MCP_SECOND_OPINION_TIMEOUT	30	Per-provider timeout in seconds.
MCP_SECOND_OPINION_MAX_TOKENS	2048	Cap on rival output length.

Register with your MCP client

Claude Desktop / Claude Code (mcp_settings.json or equivalent):

{
  "mcpServers": {
    "second-opinion": {
      "command": "mcp-second-opinion",
      "env": {
        "OPENAI_API_KEY": "sk-...",
        "GEMINI_API_KEY": "...",
        "ANTHROPIC_API_KEY": "...",
        "XAI_API_KEY": "..."
      }
    }
  }
}

Tools

ask_other_model(question, model, context?, system_prompt?)

Ask one specific rival.

question:      "is this regex correct? /^[a-z]+$/"
model:         "gpt-5" | "gemini-2.5-pro" | "claude-opus-4-7" | "grok-4" | ...
context:       optional — code snippets, prior reasoning, files
system_prompt: optional — persona/framing

Returns:

{
  "answer": "...",
  "model": "gpt-5",
  "latency_ms": 842,
  "tokens": {"input": 47, "output": 92},
  "cost_usd": 0.0014
}

ask_the_panel(question, context?, system_prompt?)

Fan out to every enabled provider in parallel.

question:      "what's wrong with this approach?"
context:       optional
system_prompt: optional — applied uniformly to all panelists

Returns:

{
  "responses": {
    "openai":    {"answer": "...", "model": "gpt-5", "latency_ms": 800, "cost_usd": 0.001, "error": null},
    "gemini":    {"answer": "...", "model": "gemini-2.5-pro", "latency_ms": 750, "cost_usd": 0.0008, "error": null},
    "anthropic": {"answer": null, "model": "claude-opus-4-7", "error": "skipped (self)"},
    "grok":      {"answer": null, "model": "grok-4", "error": "XAI_API_KEY not set"}
  },
  "total_cost_usd": 0.0018,
  "total_latency_ms": 800
}

How it works

The server is a thin layer over LiteLLM, which provides a unified OpenAI-shaped interface to all four providers. Cost calculation comes from LiteLLM's pricing table.

License

MIT