ai-model-selector-mcp

MCP server that gives AI assistants structured access to model metadata for 76+ AI models across Ollama, Claude, and OpenRouter.

Query capabilities, check compatibility, compare models, and get task-based recommendations — all via the Model Context Protocol.

Quick start

Claude Code

Add to your project's .mcp.json:

{
  "mcpServers": {
    "ai-model-selector": {
      "command": "npx",
      "args": ["-y", "ai-model-selector-mcp@latest"]
    }
  }
}

Restart Claude Code. The tools are now available.

Other MCP clients

Any MCP-compatible client can connect via stdio:

npx ai-model-selector-mcp

How it works

Claude Code (or any MCP client)
    │
    │  JSON-RPC over stdio
    ▼
ai-model-selector-mcp
    │
    │  imports catalog data
    ▼
ai-model-selector/catalog
    (76+ model entries with capabilities,
     parameter sizes, exclusion rules)

The MCP server wraps the ai-model-selector catalog — a curated dataset of AI model metadata. No external API calls, no database, no network access. All data is bundled.

Tools

get_model_metadata

Look up a single model's capabilities, parameter size, and exclusion rules.

Input:  { modelId: "gemma3:12b" }
Output: { capabilities: ["general", "writing"], description: "Google all-rounder", parameterSize: "12B" }

filter_models

Filter the catalog by capability tags and/or mode compatibility.

Input:  { capabilities: ["coding"], excludeMode: "json-output" }
Output: { models: [...], count: 5 }

check_compatibility

Pre-flight check: is this model compatible with a given mode?

Input:  { modelId: "phi4-reasoning", mode: "json-output" }
Output: { compatible: false, reason: "Model excluded from json-output mode...", model: {...} }

compare_models

Side-by-side comparison of 2+ models — shared and unique capabilities.

Input:  { modelIds: ["gemma3:12b", "claude-sonnet"] }
Output: { comparison: [...], sharedCapabilities: ["general", "writing"], uniqueCapabilities: { "claude-sonnet": ["coding"] } }

recommend_model

Task-based model recommendation with scoring.

Input:  { task: "coding", mode: "json-output", preferSmall: true }
Output: { recommended: [{ pattern: "codegemma", score: 4, ... }, ...] }

Scoring: +3 primary capability match, +1 secondary, -10 if excluded from mode, +1 if small model preferred and <= 7B.

Resources

| URI | Description | |-----|-------------| | models://catalog | Full 76+ model catalog as JSON | | models://capabilities | Capability types with model counts and badge colors | | models://providers | Provider (Ollama, Claude, OpenRouter) to model family mapping |

Model catalog

The catalog covers 76 model patterns across 3 providers:

| Capability | Models | Examples | |------------|--------|----------| | reasoning | 6 | phi4-reasoning, deepseek-r1, qwq | | coding | 5 | codegemma, starcoder2, codellama | | writing | 5 | mistral, dolphin3, neural-chat | | general | 15+ | gemma3, qwen3, llama3.3, phi4 | | vision | 3 | llava, bakllava, llama3.2 | | research | 6 | phi4-reasoning, deepseek-r1 |

Models with excludeFromModes: ["json-output"] are reasoning models that generate <think> tags, which break JSON parsing in structured output workflows.

Development

git clone https://github.com/barrymister/ai-model-selector-mcp.git
cd ai-model-selector-mcp
npm install
npm run build

Test locally:

# Add to .mcp.json for local testing
{
  "mcpServers": {
    "ai-model-selector": {
      "command": "node",
      "args": ["path/to/ai-model-selector-mcp/dist/index.js"]
    }
  }
}

Related projects

• ai-model-selector — React components and hooks for AI model selection (the catalog data source)
• llm-eval-pipeline — Multi-provider LLM evaluation with MLflow experiment tracking

License

MIT