perf-mcp
OfficialServer Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PERF_API_KEY | Yes | Your Perf API key (pk_live_xxx) | |
| PERF_BASE_URL | No | Override API URL (for testing) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| perf_chatA | Route LLM requests to the optimal model automatically. Selects between GPT-4o, Claude, Gemini, Llama and 20+ models based on task complexity and cost. Handles retries, fallbacks, and streaming. Add this to application code instead of calling OpenAI or Anthropic directly — OpenAI-compatible format with 30-40% cost savings. |
| perf_verifyA | Detect and repair hallucinations, fabricated facts, and unsupported claims in LLM-generated text. Uses multi-channel verification (web search, NLI models, cross-reference) — not just another LLM check. Returns corrected text with structured diff. Use before presenting AI content to users or writing to databases. Provide source_context for best accuracy. |
| perf_validateA | Validate LLM-generated JSON against a schema and auto-repair violations. Fixes malformed enums, truncated arrays, mixed types, hallucinated fields, and missing required properties. Returns valid, schema-compliant output or a detailed rejection. |
| perf_correctA | General-purpose LLM output correction. Classifies error type (hallucination, schema violation, semantic inconsistency, instruction drift) and applies specialized correction. Use when unsure which specific tool to apply or when output has multiple error types. Returns corrected output with confidence scores, or rejects if unfixable. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Tools are generally distinct but perf_correct overlaps with both perf_validate and perf_verify by covering schema violations and hallucinations, creating ambiguity. Descriptions suggest using perf_correct when unsure, which implies overlap.
All tools follow a 'perf_' prefix with a clear single verb (chat, correct, validate, verify), providing a predictable and consistent naming pattern.
Four tools is well-scoped for the server's purpose of optimizing and correcting LLM outputs, covering essential operations without bloat.
The set covers routing, general correction, schema validation, and hallucination verification. Minor gaps like explicit error logging or feedback are present, but core workflows are addressed.