Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@PeakInfer MCP Server detect drift between my code and runtime behavior"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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.
Related MCP server: LUMINO MCP Server
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
npx @kalmantic/peakinfer-mcpnpm install -g @kalmantic/peakinfer-mcp
peakinfer-mcpAdd 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"
}
}
}
}git clone https://github.com/Kalmantic/peakinfer-mcp.git
cd peakinfer-mcp
npm install
npm run buildTool | Description |
| Fetch LLM events from Helicone |
| Fetch traces from LangSmith |
Tool | Description |
| Get benchmark data for a model |
| Compare current analysis to historical baseline |
Tool | Description |
| List available optimization templates |
| Get details of a specific template |
Tool | Description |
| Save analysis results to history |
Variable | Description |
| API key for Helicone integration |
| API key for LangSmith integration |
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.The server also exposes MCP resources:
peakinfer://templates - Optimization templates (43 total)
peakinfer://benchmarks - InferenceMAX benchmark data (15+ models)
peakinfer://history - Analysis run history
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
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 |
Check the path to dist/index.js is absolute
Verify npm run build completed successfully
Restart Claude Desktop after config changes
Verify API keys are set in config env section
Check keys are valid at provider's dashboard
Ensure no trailing whitespace in key values
Apache-2.0
Analyze code for LLM inference issues using PeakInfer engine. Returns detailed report on latency, cost, throughput, and reliability with actionable fixes.
Inputs: path, files, fixes, benchmark.
Compare current analysis results to a historical baseline
Inputs: current_analysis (required), baseline_path.
Fetch LLM runtime events from Helicone for drift detection analysis
Inputs: days, limit, api_key.
Get InferenceMAX benchmark data for a specific model to compare performance
Inputs: model (required), hardware, framework.
Fetch LLM traces from LangSmith for runtime analysis
Inputs: days, limit, api_key.
Get details of a specific optimization template
Inputs: name (required).
List available PeakInfer optimization templates
Inputs: category.
Save analysis results to PeakInfer history
Inputs: analysis (required), path.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
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.
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