LLM-Adviser-Mcp
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 | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get_model_infoA | Get detailed information about a specific LLM/VLM model: pricing, benchmarks, capabilities, and ready-to-use API code example. Returns structured Markdown (~300 tokens). |
| list_top_modelsA | List top-ranked LLM/VLM models for a category. Categories: coding, math, vision, general, cost-effective, open-source, speed, context-window, reasoning. Returns a compact Markdown table (~250 tokens). |
| compare_modelsA | Compare 2-5 LLM/VLM models side-by-side: pricing, benchmarks, capabilities. Returns a compact Markdown comparison table (~400 tokens). |
| recommend_modelA | Get personalized model recommendations based on use case, budget, and requirements. Returns top 3 picks with reasoning (~350 tokens). |
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 4 tools
Each tool has a clearly distinct purpose: compare_models for side-by-side comparisons, get_model_info for detailed single-model data, list_top_models for category-based rankings, and recommend_model for personalized suggestions. There is no overlap or ambiguity in their functions.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., compare_models, get_model_info). The verbs are distinct and appropriate for each action, making the naming predictable and readable throughout the set.
With 4 tools, the server is well-scoped for its purpose of advising on LLM/VLM models. Each tool earns its place by covering key aspects: comparison, detailed info, rankings, and recommendations, without being too sparse or bloated.
The tool surface provides complete coverage for the domain of LLM/VLM model advising. It includes tools for comparison, detailed retrieval, categorical listing, and personalized recommendations, covering all essential workflows without obvious gaps.