whichmodel-mcp

A model routing advisor for autonomous agents — get cost-optimised LLM recommendations via MCP.

whichmodel.dev tracks pricing and capabilities across 100+ LLM models, updated every 4 hours. This MCP server exposes that data so AI agents can pick the right model at the best price for every task.

MCP Endpoint

https://whichmodel.dev/mcp

Transport: Streamable HTTP (MCP spec 2025-03-26)

Quick Start

Add to your MCP client config:

{
  "mcpServers": {
    "whichmodel": {
      "url": "https://whichmodel.dev/mcp"
    }
  }
}

No API key required. No installation needed.

Stdio (local clients)

For MCP clients that use stdio transport (Claude Desktop, Cursor, etc.):

{
  "mcpServers": {
    "whichmodel": {
      "command": "npx",
      "args": ["-y", "whichmodel-mcp"]
    }
  }
}

This runs a thin local proxy that forwards requests to the remote server.

Tools

recommend_model

Get a cost-optimised model recommendation for a specific task type, complexity, and budget.

Parameter	Type	Description
task_type	enum (required)	chat, code_generation, code_review, summarisation, translation, data_extraction, tool_calling, creative_writing, research, classification, embedding, vision, reasoning
complexity	low | medium | high	Task complexity (default: medium)
estimated_input_tokens	number	Expected input size in tokens
estimated_output_tokens	number	Expected output size in tokens
budget_per_call	number	Maximum spend in USD per call
requirements	object	Capability requirements: tool_calling, json_output, streaming, context_window_min, providers_include, providers_exclude

Returns: recommended model, alternative, budget option, cost estimate, and reasoning.

---

compare_models

Head-to-head comparison of 2–5 models with optional volume cost projections.

Parameter	Type	Description
models	string[] (required)	Model IDs, e.g. [anthropic/claude-sonnet-4, openai/gpt-4.1]
task_type	enum	Context for comparison
volume	object	calls_per_day, avg_input_tokens, avg_output_tokens for daily/monthly cost projections

Returns: pricing, capabilities, quality tiers, and projected costs per model.

---

get_pricing

Raw pricing data lookup with filters by model, provider, price ceiling, and capabilities.

Parameter	Type	Description
model_id	string	Specific model ID
provider	string	Filter by provider, e.g. anthropic
max_input_price	number	Max input price per million tokens (USD)
capabilities	string[]	Required capabilities: tool_calling, json_output, streaming, vision
min_context_window	number	Minimum context window in tokens
limit	number	Max results (1–100, default 20)

---

check_price_changes

See what model pricing has changed since a given date.

Parameter	Type	Description
since	string (required)	ISO date, e.g. 2026-04-01
model_id	string	Filter to a specific model
provider	string	Filter to a specific provider

Returns: price increases, decreases, new models, and deprecations.

Agent Integration

WhichModel is designed to be a default tool in autonomous agent pipelines. Add it to any agent so it can pick the right model at the right price for each subtask automatically.

System prompt (copy-paste)

Add this block to your agent's system prompt:

You have access to the WhichModel MCP server for cost-aware model selection.

Before calling any LLM for a subtask, use recommend_model to pick the right model:

  recommend_model({
    task_type: "code_generation",   // chat | code_generation | summarisation | reasoning | ...
    complexity: "medium",           // low | medium | high
    estimated_input_tokens: 2000,   // optional
    estimated_output_tokens: 500,   // optional
    budget_per_call: 0.01,          // optional hard cap in USD
    requirements: {
      tool_calling: true,           // if the subtask needs tool use
    }
  })

Use the returned recommendation.model_id. The response includes cost_estimate and
reasoning so you can log why each model was chosen.

Prompt templates via MCP

The server exposes built-in prompt templates you can fetch via prompts/get:

Prompt name	Use case
cost-aware-agent	Full system prompt block for cost-aware model selection
task-router-snippet	Minimal snippet to add to an existing system prompt
budget-constrained-agent	Hard cost cap per call (pass budget_usd argument)

Retrieve them programmatically:

{ "method": "prompts/get", "params": { "name": "cost-aware-agent" } }

Framework integrations

• LangChain: langchain-whichmodel — WhichModelRouter chain
• Haystack: whichmodel-haystack — WhichModelRouter component

Data Freshness

Pricing data is refreshed every 4 hours from OpenRouter. Each response includes a data_freshness timestamp so you know how current the data is.

Links

• Website: whichmodel.dev
• MCP endpoint: https://whichmodel.dev/mcp
• Discovery: https://whichmodel.dev/.well-known/mcp.json