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.

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

Quick Start

Claude Code

claude mcp add llm-advisor -- npx -y llm-advisor-mcp

Claude Code (Windows)

claude mcp add llm-advisor -- cmd /c npx -y llm-advisor-mcp

Claude Desktop / Cursor / Windsurf

Add to your MCP configuration file:

{
  "mcpServers": {
    "llm-advisor": {
      "command": "npx",
      "args": ["-y", "llm-advisor-mcp"]
    }
  }
}

That is it. No API keys, no .env files.

Compatible Clients

Tools

get_model_info

Detailed specs for a specific model: pricing, benchmarks, percentile ranks, capabilities, and a ready-to-use API code example.

Parameters

Example output

## anthropic/claude-sonnet-4

**Provider**: anthropic | **Modality**: text+image→text | **Released**: 2025-06-25

### Pricing
| Metric | Value |
|--------|-------|
| Input | $3.00 /1M tok |
| Output | $15.00 /1M tok |
| Cache Read | $0.30 /1M tok |
| Context | 200K |
| Max Output | 64K |

### Benchmarks
| Benchmark | Score |
|-----------|-------|
| SWE-bench Verified | 76.8% |
| Aider Polyglot | 72.1% |
| Arena Elo | 1467 |
| MMMU | 76.0% |

### Percentile Ranks
| Category | Percentile |
|----------|------------|
| Coding | P96 |
| General | P95 |
| Vision | P90 |

**Capabilities**: Tools, Reasoning, Vision

### API Example (openai_sdk)

from openai import OpenAI client = OpenAI( base_url="https://openrouter.ai/api/v1", api_key="<OPENROUTER_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

Example output

## Top 5: coding

| # | Model | Key Score | Input $/1M | Output $/1M | Context | Released |
|------|------|------|------|------|------|------|
| 1 | openai/o3-pro | SWE 79.5% | $20.00 | $80.00 | 200K | 2025-06-10 |
| 2 | anthropic/claude-sonnet-4 | SWE 76.8% | $3.00 | $15.00 | 200K | 2025-06-25 |
| 3 | google/gemini-2.5-pro | SWE 75.2% | $1.25 | $10.00 | 1M | 2025-03-25 |
| 4 | openai/o4-mini | SWE 73.6% | $1.10 | $4.40 | 200K | 2025-04-16 |
| 5 | anthropic/claude-opus-4 | SWE 72.5% | $15.00 | $75.00 | 200K | 2025-05-22 |

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

Example output

## Model Comparison (3 models)

| | **anthropic/claude-sonnet-4** | **openai/gpt-4.1** | **google/gemini-2.5-pro** |
|------|------|------|------|
| Input $/1M | $3.00 | **$2.00** | $1.25 |
| Output $/1M | $15.00 | $8.00 | **$5.00** |
| Context | 200K | 1M | **1M** |
| Max Output | 64K | 32K | **65K** |
| SWE-bench | **76.8%** | 55.0% | 75.2% |
| Aider Polyglot | **72.1%** | 65.3% | 71.8% |
| Arena Elo | 1467 | **1492** | 1445 |
| Vision | Yes | Yes | Yes |
| Tools | Yes | Yes | Yes |
| Reasoning | Yes | No | Yes |
| Open Source | No | No | No |
| Released | 2025-06-25 | **2025-04-14** | 2025-03-25 |

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).

Parameters

Example output

## Recommended for: coding

### 1. anthropic/claude-sonnet-4 (score: 78)
Input: $3.00/1M | Output: $15.00/1M | Context: 200K | Released: 2025-06-25
Benchmarks: SWE-bench: 76.8%, Aider: 72.1%, Arena: 1467
Strengths: reasoning, tools, vision

### 2. google/gemini-2.5-flash (score: 74)
Input: $0.15/1M | Output: $0.60/1M | Context: 1M | Released: 2025-05-20
Benchmarks: SWE-bench: 62.9%, Arena: 1445
Strengths: tools, vision, 1M+ context

### 3. openai/o4-mini (score: 71)
Input: $1.10/1M | Output: $4.40/1M | Context: 200K | Released: 2025-04-16
Benchmarks: SWE-bench: 73.6%, Arena: 1430
Strengths: reasoning, tools

Data Sources

All data is fetched in real time from free, public APIs. No authentication required.

Context Cost

MCP tool definitions and responses consume your LLM's context window. This server is designed to be lean:

For comparison, most MCP servers that return raw JSON consume 3,000-10,000 tokens per response. Every response from llm-advisor-mcp is pre-formatted Markdown, keeping context costs roughly 10x lower.

Architecture

┌──────────────────────────────────────────────┐
│              MCP Client (Claude, etc.)        │
└──────────┬───────────────────────────────────┘
           │ stdio (JSON-RPC)
┌──────────▼───────────────────────────────────┐
│            llm-advisor-mcp server             │
│                                               │
│  ┌─────────┐  ┌───────────┐  ┌────────────┐  │
│  │  Tools   │  │ Registry  │  │   Cache    │  │
│  │ (4 tools)│──│ (unified) │──│ (in-memory)│  │
│  └─────────┘  └───────────┘  └────────────┘  │
│                     │                         │
│        ┌────────────┼────────────┐            │
│        ▼            ▼            ▼            │
│  ┌──────────┐ ┌──────────┐ ┌──────────┐      │
│  │Normalizer│ │Percentile│ │ Fetchers │      │
│  │(slug map)│ │ (5 cats) │ │(5 sources│      │
│  └──────────┘ └──────────┘ └──────────┘      │
└──────────────────────────────────────────────┘
           │           │           │
     OpenRouter    SWE-bench    Arena / VLM / Aider

• TypeScript + ESM — Single entry point, tsup build
• In-memory cache — TTL-based (1h pricing, 6h benchmarks), stale-while-revalidate
• Cross-source normalization — Maps inconsistent model names (e.g. Claude 3.5 Sonnet vs anthropic/claude-3.5-sonnet) to canonical IDs
• Percentile computation — Ranks across 5 categories (coding, math, general, vision, cost efficiency)
• Freshness scoring — Recommendation algorithm gives a bonus to recently released models (+3 for <=3mo, +1 for <=6mo)
• Zero runtime deps beyond @modelcontextprotocol/sdk and zod

Roadmap

Development

git clone https://github.com/Daichi-Kudo/llm-advisor-mcp.git
cd llm-advisor-mcp
npm install
npm run build       # Build with tsup
npm run dev         # Run with tsx (hot reload)
npm test            # Run 51 unit tests (vitest)
npm run test:watch  # Watch mode

Project structure

src/
  index.ts              # Server entry point
  types.ts              # Shared type definitions
  tools/
    model-info.ts       # get_model_info tool
    list-top.ts         # list_top_models tool
    compare.ts          # compare_models tool
    recommend.ts        # recommend_model tool
    formatters.ts       # Markdown output formatters
  data/
    registry.ts         # Unified model registry
    cache.ts            # In-memory TTL cache
    normalizer.ts       # Cross-source name normalization
    percentiles.ts      # Percentile rank computation
    fetchers/
      openrouter.ts     # OpenRouter API
      swe-bench.ts      # SWE-bench leaderboard
      arena.ts          # LM Arena Elo ratings
      vlm-leaderboard.ts # OpenCompass VLM benchmarks
      aider.ts          # Aider Polyglot scores
    static/
      api-examples.ts   # API code snippet templates

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Run npm test to verify all 51 tests pass
5. Submit a pull request

License

MIT — built by Cognisant LLC