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Llm Advisor MCP

by Daichi-Kudo

Compares current LLM and VLM pricing, benchmarks, capabilities, and fit without requiring an API key.

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Install Llm Advisor MCP

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "daichi-kudo-llm-advisor-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "llm-advisor-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

The install command started, but didn't respond the way we expected when we tried to talk to it.

npx -y llm-advisor-mcp

initialize succeeded but no response to tools/list.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

Daichi-Kudo/llm-advisor-mcp MCP server brings current LLM and VLM pricing, benchmark results, capabilities, and recommendations into MCP-compatible assistants. It fetches data from five public sources, combines it into compact Markdown responses, and refreshes sources on different cache schedules. Use it when an agent needs current model-selection information rather than relying on its training cutoff. No API key, registration, or environment file is required.

Use cases

  • Compare current models for coding, reasoning, vision, or general workloads
  • Find models that fit input, output, and context-window limits
  • Inspect pricing and benchmark data for a specific model
  • Generate a shortlist based on capabilities and release date

Key features

  • Current LLM and VLM pricing and metadata
  • Benchmark data from five public sources
  • Side-by-side comparison for 2-5 models
  • Constraint-based top-three recommendations
  • Compact Markdown responses
  • No API key or registration required

Tools4

What Llm Advisor MCP lets your agent do. Expand a tool for its parameters.

Verified live~763 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 3d ago

Recent health check succeeded.

18/18 checks healthy over the last 78d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 9d ago via OSV.dev · llm-advisor-mcp (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars2Stargazers on the source repository.
npm downloads308Package downloads in the last 30 days.
Last commit4mo agoMost recent push to the default branch.
Install checkInconclusiveStarted but did not complete the MCP handshake in our test.
Tools exposed4Callable tools this server registers over MCP.
Directory activity3 viewsConfig copies, upvotes, and views on AllMCPs.

How Llm Advisor MCP works

What Daichi-Kudo/llm-advisor-mcp MCP server does

Daichi-Kudo/llm-advisor-mcp MCP server helps an AI assistant evaluate available LLM and VLM models using current public data. It brings together pricing, context limits, modalities, release dates, benchmark scores, and capability indicators in a single MCP interface. The results are formatted as short Markdown tables or structured sections instead of large raw-data responses, which keeps tool output relatively small.

The server supports four decision-making workflows. Developers can inspect one model in detail, rank models by a selected category, compare two to five models side by side, or request three recommendations based on a use case and constraints. Supported ranking categories include coding, math, vision, general quality, cost effectiveness, open source, speed, context window, and reasoning.

How it works

The server reads data from OpenRouter, SWE-bench, LM Arena, OpenCompass VLM, and Aider Polyglot. These sources provide model pricing and metadata, coding results, human-preference ratings, vision benchmarks, and multilingual coding scores. Data is retrieved from public APIs or published leaderboards without authentication.

Source data is cached for different periods. OpenRouter data has a one-hour cache lifetime, while the benchmark sources generally use six-hour cache periods. Recommendations combine benchmark weighting, pricing, capability matches, and a freshness adjustment for recently released models.

Setup and configuration

Daichi-Kudo/llm-advisor-mcp runs locally over the MCP stdio transport. Node.js 18 or newer is required. Install and launch it with:

Terminal
npx -y llm-advisor-mcp

For Claude Desktop, Cursor, or Windsurf, add a server entry whose command is npx and whose arguments are -y and llm-advisor-mcp. Claude Code can also add the same server through its MCP command. No API keys, registration, .env file, or other credential configuration is needed.

Tools and capabilities

Daichi-Kudo/llm-advisor-mcp exposes these tools:

  • get_model_info accepts a model ID or partial name and returns pricing, benchmarks, percentile ranks, context and capability details, plus an optional API example. Examples can use the OpenAI SDK, cURL, or Python requests.
  • list_top_models ranks models for a selected category. Optional filters include result count, minimum context length, and minimum release date.
  • compare_models compares two to five model IDs across pricing, context, maximum output, benchmarks, capabilities, open-source status, and release date. Best values are highlighted in the result.
  • recommend_model returns three ranked choices for coding, math, general, vision, creative, reasoning, or cost-effective workloads. Filters can require price limits, context size, vision, tools, open-source status, or a release date.

Limitations and notes

The server reports only the models and measurements represented by its five data sources. Coverage and freshness vary by source, and benchmark scores may not predict performance for every workload. Recommendation scoring uses fixed factors described by the project, including weighted benchmarks, price, capability bonuses, and release freshness.

The API code example returned by get_model_info is a usage template for the selected provider route; using that example may require a separate provider credential even though the MCP server itself does not require one. The project is distributed under the MIT license and requires Node.js 18 or newer.

Read the full README View source on GitHub

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Frequently Asked Questions about Llm Advisor MCP

Run `npx -y llm-advisor-mcp` with Node.js 18 or newer, or add that command to an MCP client configuration.

Yes.

No. It fetches data from free public APIs and does not require authentication, registration, or an environment file.

Call `compare_models` with two to five model IDs or partial names to receive a side-by-side comparison of pricing, benchmarks, capabilities, and related metadata.

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