AI Model Advisor MCP Server
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- AlicenseNot gradedqualityDmaintenanceEnables AI agents to compare LLM benchmarks, get pricing, and receive model recommendations for tasks and budgets.32 PyPI1MIT
- FlicenseNot gradedqualityDmaintenanceEnables real-time access to LLM pricing, benchmarks, deprecation alerts, and cost optimization for over 30 models across 8 providers, allowing AI agents to make cost-effective model selections.-
- AlicenseNot gradedqualityDmaintenanceDiscovers LLM models in real time from cloud providers and local Ollama instances, returning compatibility profiles and live pricing so AI agents can route tasks to the cheapest viable model without breaking tool calls or context clipping.10MIT
- AlicenseAqualityFmaintenanceProvides AI assistants with real-time access to 1000+ AI models including their latest pricing, context windows, capabilities, and specifications. Supports model search, comparison, recommendations, and live testing.361 npm1MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI assistants to compare costs, performance, and quality across 150+ models from 26 LLM providers, estimate or predict workload spend, route tasks to cost-effective models, and track actual recorded usage. Prices are cross-checked against a live external registry so drifted figures are flagged rather than silently served.MIT
- AlicenseNot gradedqualityBmaintenanceEnables agents to rank model variants and hunt down API offers for a specific task, estimate real billed cost, audit n8n/Make automations for overpay, missing fallbacks and deprecated models, and plan or run evaluation and canary suites on the user's own examples. It also supports price comparison, custom reseller channels with measured per-call cost, and non-LLM capabilities such as web search and speech-to-text.AGPL 3.0
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.