AI Model Advisor MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| select_model_for_projectA | Pick the best overall, cheapest acceptable, and best value AI model for a project. Use this when an agent has project context and needs one decision instead of manually chaining recommend, compare, pricing, and cost tools. Returns concise markdown plus structured fields with candidates, scores, reasons, pricing, and tradeoffs. |
| recommend_modelA | Recommend the best AI model for a task. Searches across 1000+ models spanning LLMs, image gen, video gen, TTS, STT, 3D, and more from 5 providers (OpenRouter, fal.ai, Together AI, Replicate, Fireworks). Returns ranked results based on task match, capabilities, quality tier, price, speed (TTFT/throughput), and intelligence benchmarks (MMLU/coding). |
| compare_modelsA | Compare AI models side-by-side in a table. Works across providers and categories — compare LLMs against each other, image generators, or even mix categories. Auto-adapts columns based on model types. Includes speed metrics (TTFT, tokens/sec) and benchmark scores (MMLU) when available. |
| list_modelsB | List and filter available AI models from all 5 providers. Filter by category (llm, text-to-image, text-to-video, text-to-speech, speech-to-text, image-to-3d, etc.), provider (openrouter, fal, together, replicate, fireworks), capability, or max price. |
| get_model_infoA | Get a comprehensive model card for any AI model. Includes description, pricing breakdown, capabilities, quality tier, context length (LLMs), speed metrics (TTFT, throughput), benchmark scores (MMLU, coding), tags, license, and more. |
| estimate_costA | Estimate the cost of using an AI model. For LLMs, provide token counts. For media models, provide unit counts. Example: estimate_cost({model_id: "fal-ai/flux-pro", usage: {images: 100}}) |
| whats_newA | Discover recently added AI models. Answers "what new models dropped this week?" Filter by time window and category. |
| find_cheapest_providerA | Find the cheapest provider for a given model. Searches across all 5 providers to find where a model (e.g. "llama 3.3 70b", "flux pro", "deepseek r1") is available and compares pricing. Uses fuzzy matching to find the same model across different providers. |
| batch_get_pricingA | Get pricing for multiple models in a single call. Returns a compact table with pricing for all requested models. Much faster than calling get_model_info one at a time. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 9 tools
Most tools have clearly distinct purposes: listing, getting details, comparing, recommending, estimating cost, etc. However, recommend_model and select_model_for_project overlap in that both provide recommendations; select_model_for_project is explicitly a higher-level aggregation tool, but agents could be uncertain which to use.
The majority of tools follow a consistent verb_noun snake_case pattern (e.g., list_models, get_model_info, estimate_cost). The exception is whats_new, which deviates from the verb_noun structure but remains clear and fits the domain.
With 9 tools, the set is well-scoped for an AI model advisor. Each tool addresses a distinct need, and the count is within the ideal range for a focused server.
The tool surface covers the full lifecycle of model advisory: discovery (list_models, whats_new), detailed info (get_model_info), recommendation (recommend_model, select_model_for_project), comparison (compare_models), pricing (batch_get_pricing, find_cheapest_provider), and cost estimation (estimate_cost). No significant gaps are apparent.