PeakInfer MCP Server
# 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)
```bash
npx @kalmantic/peakinfer-mcp
```
### Via npm (Global)
```bash
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):
```json
{
"mcpServers": {
"peakinfer": {
"command": "npx",
"args": ["@kalmantic/peakinfer-mcp"],
"env": {
"HELICONE_API_KEY": "your-key-here",
"LANGSMITH_API_KEY": "your-key-here"
}
}
}
}
```
### Build from Source
```bash
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 |
## Troubleshooting
### Server not appearing in Claude Desktop
1. Check the path to `dist/index.js` is absolute
2. Verify `npm run build` completed successfully
3. Restart Claude Desktop after config changes
### API key errors
1. Verify API keys are set in config `env` section
2. Check keys are valid at provider's dashboard
3. Ensure no trailing whitespace in key values
## Links
- [PeakInfer CLI](https://github.com/Kalmantic/peakinfer)
- [Documentation](https://github.com/Kalmantic/peakinfer#readme)
- [Report Issues](https://github.com/Kalmantic/peakinfer-mcp/issues)
## License
Apache-2.0
TDQS
Scored across 8 tools
Most tools target distinct actions and resources. The only potential confusion is between get_helicone_events and get_langsmith_traces, but their descriptions clearly indicate different source platforms, making them distinguishable.
Tool names follow a consistent verb_noun snake_case pattern with get_*, list_*, save_*, and compare_to_*. The exception is 'analyze' which lacks an explicit object, but its purpose is still clear from the description.
Eight tools is a well-scoped set that covers code analysis, external data retrieval, benchmarking, template management, and persistence without unnecessary bloat. It fits the 3-15 tool range comfortably.
The set covers analysis, saving, comparison, and template browsing, but lacks any way to retrieve or list previously saved analyses. This creates a dead end after saving, as agents cannot access historical results directly.