Daichi-Kudo/llm-advisor-mcp

**English** | 日本語
**Give your AI assistant real-time LLM/VLM knowledge.** Pricing, benchmarks, and recommendations — updated every hour, not every training cycle.
LLMs have knowledge cutoffs. Ask Claude "what's the best coding model right now?" and it cannot answer with current data. This MCP server fixes that by feeding live model intelligence directly into your AI assistant's context window.
- **Zero config** — No API keys, no registration. One command to install.
- **Low token** — Compact Markdown tables (~300 tokens), not raw JSON (~3,000 tokens). Your context window matters.
- **5 benchmark sources** — SWE-bench, LM Arena Elo, OpenCompass VLM, Aider Polyglot, and OpenRouter pricing merged into one unified view.
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Use Cases
- **"What's the best coding model right now?"** — list_top_models with category coding
- **"Compare Claude vs GPT vs Gemini"** — compare_models with side-by-side table
- **"Find a cheap model with 1M context"** — recommend_model with budget constraints
- **"What benchmarks does model X have?"** — get_model_info with percentile ranks
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Quick Start
Claude Code
Claude Code (Windows)
Claude Desktop / Cursor / Windsurf
Add to your MCP configuration file:
That is it. No API keys, no .env files.
Compatible Clients
| Client | Supported | Install Method |
|--------|-----------|----------------|
| Claude Code | Yes | claude mcp add |
| Claude Desktop | Yes | JSON config |
| Cursor | Yes | JSON config |
| Windsurf | Yes | JSON config |
| Any MCP client | Yes | stdio transport |
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Tools
get_model_info
Detailed specs for a specific model: pricing, benchmarks, percentile ranks, capabilities, and a ready-to-use API code example.
**Parameters**
| Name | Type | Required | Default | Description |
|------|------|----------|---------|-------------|
| model | string | Yes | — | Model ID or partial name (e.g. "claude-sonnet-4", "gpt-5") |
| include_api_example | boolean | No | true | Include a ready-to-use code snippet |
| api_format | enum | No | openai_sdk | openai_sdk, curl, or python_requests |
**Example output**
python
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=" ",
)
response = client.chat.completions.create(
model="anthropic/claude-sonnet-4",
messages=[{"role": "user", "content": "Hello"}],
)
---
list_top_models
Top-ranked models for a category. Includes release dates for freshness awareness.
**Parameters**
| Name | Type | Required | Default | Description |
|------|------|----------|---------|-------------|
| category | enum | Yes | — | coding, math, vision, general, cost-effective, open-source, speed, context-window, reasoning |
| limit | number | No | 10 | Number of results (1-20) |
| min_context | number | No | — | Minimum context window in tokens |
| min_release_date | string | No | — | YYYY-MM-DD. Excludes models released before this date |
**Example output**
---
compare_models
Side-by-side comparison for 2-5 models. Best values are **bolded** automatically. Includes a Released row so you can spot outdated models at a glance.
**Parameters**
| Name | Type | Required | Default | Description |
|------|------|----------|---------|-------------|
| models | string[] | Yes | — | 2-5 model IDs or partial names |
**Example output**
---
recommend_model
Personalized top-3 recommendations. Scores combine weighted benchmarks, pricing, capability bonuses, and a freshness bonus (+3 points for models released within 3 months, +1 within 6 months).