MCP 服务器

PeakInfer MCP Server

MCP server for achieving peak inference performance by detecting drift between code and runtime behavior, comparing against benchmarks, and providing optimization templates.

README

PeakInfer MCP Server

Achieve peak inference performance in Claude Desktop and Claude Code.

PeakInfer helps you run AI inference at peak performance by correlating what no one else sees together: your code, runtime behavior, benchmarks, and evals.

The Problem

Your code says streaming: true. Runtime shows 0% actual streams. That's drift—and it's killing your latency.

Peak Inference Performance means: Improving latency, throughput, reliability, and cost without changing evaluated behavior.

Features

Drift Detection: Find mismatches between code declarations and runtime behavior
Runtime Connectors: Fetch events from Helicone and LangSmith
Benchmark Comparison: Compare your metrics to InferenceMAX benchmarks (15+ models)
Template Library: Access 43 optimization templates
Analysis History: Track and compare performance over time

Installation

Via npx (Recommended)

npx @kalmantic/peakinfer-mcp

Via npm (Global)

npm install -g @kalmantic/peakinfer-mcp
peakinfer-mcp

Claude Desktop Configuration

Add to ~/.config/claude/claude_desktop_config.json (macOS) or %APPDATA%\claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "peakinfer": {
      "command": "npx",
      "args": ["@kalmantic/peakinfer-mcp"],
      "env": {
        "HELICONE_API_KEY": "your-key-here",
        "LANGSMITH_API_KEY": "your-key-here"
      }
    }
  }
}

Build from Source

git clone https://github.com/Kalmantic/peakinfer-mcp.git
cd peakinfer-mcp
npm install
npm run build

Available Tools

Runtime Data

Tool	Description
`get_helicone_events`	Fetch LLM events from Helicone
`get_langsmith_traces`	Fetch traces from LangSmith

Benchmarks

Tool	Description
`get_inferencemax_benchmark`	Get benchmark data for a model
`compare_to_baseline`	Compare current analysis to historical baseline

Templates

Tool	Description
`list_templates`	List available optimization templates
`get_template`	Get details of a specific template

Analysis

Tool	Description
`save_analysis`	Save analysis results to history

Environment Variables

Variable	Description
`HELICONE_API_KEY`	API key for Helicone integration
`LANGSMITH_API_KEY`	API key for LangSmith integration

Example Usage

In Claude Desktop or Claude Code:

Fetch the last 7 days of events from Helicone and identify any drift between my code and runtime behavior.

Compare my current p95 latency to InferenceMAX benchmarks for gpt-4o.

Show me optimization templates for improving throughput without changing model behavior.

Resources

The server also exposes MCP resources:

peakinfer://templates - Optimization templates (43 total)
peakinfer://benchmarks - InferenceMAX benchmark data (15+ models)
peakinfer://history - Analysis run history

Prompts

Available prompt templates:

analyze-file - Analyze a file for LLM inference points
compare-benchmarks - Compare your metrics to peak benchmarks
suggest-optimizations - Get optimization recommendations that preserve behavior

The Four Dimensions

PeakInfer analyzes every inference point across 4 dimensions:

Dimension	What We Find
Latency	Missing streaming, blocking calls, p95 vs benchmark gaps
Throughput	Sequential bottlenecks, batch opportunities
Reliability	Missing retries, timeouts, fallbacks
Cost	Right-sized model selection, token optimization