perf-mcp
OfficialClick on "Install 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., "@perf-mcpCheck this fact: The Eiffel Tower was built in 1887."
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.
perf-mcp
Fact-check and fix AI outputs. Catches hallucinations, repairs broken JSON, corrects errors — before they reach users.
Works with Claude Code, Cursor, Windsurf, Cline, and any MCP-compatible client.
Quick Start
Add to your MCP client config:
{
"mcpServers": {
"perf": {
"command": "npx",
"args": ["-y", "perf-mcp"],
"env": {
"PERF_API_KEY": "pk_live_xxx"
}
}
}
}Get your API key at dashboard.withperf.pro — 200 free verifications, no credit card.
Related MCP server: Arkheia Hallucination Detection MCP
Tools
perf_verify
Detect and repair hallucinations in LLM-generated text. Uses multi-channel verification (web search, NLI models, cross-reference) — not just another LLM check.
perf_verify({ content: "The Eiffel Tower was built in 1887." })
→ Corrected: "built in 1887" → "inaugurated in 1889" (89% confidence)perf_validate
Validate LLM-generated JSON against a schema and auto-repair violations. Fixes malformed enums, wrong types, missing fields, hallucinated properties.
perf_validate({
content: '{"name": "John", "age": "twenty"}',
target_schema: { type: "object", properties: { name: { type: "string" }, age: { type: "number" } } }
})
→ Rejected: /age must be numberperf_correct
General-purpose output correction. Classifies the error type and applies the right fix — hallucination, schema violation, semantic inconsistency, or instruction drift.
perf_correct({ content: "The Great Wall was built in 1950.", correction_budget: "fast" })
→ Corrected: temporal_error + factual_error detected (87% confidence)perf_chat
Route LLM requests to the optimal model automatically. Selects between GPT-4o, Claude, Gemini, and 20+ models based on task complexity. OpenAI-compatible format.
Setup by Client
Claude Code
Add to your project's .mcp.json:
{
"mcpServers": {
"perf": {
"command": "npx",
"args": ["-y", "perf-mcp"],
"env": {
"PERF_API_KEY": "pk_live_xxx"
}
}
}
}Cursor
Settings → MCP → Add Server:
{
"mcpServers": {
"perf": {
"command": "npx",
"args": ["-y", "perf-mcp"],
"env": {
"PERF_API_KEY": "pk_live_xxx"
}
}
}
}Windsurf
Add to ~/.codeium/windsurf/mcp_config.json:
{
"mcpServers": {
"perf": {
"command": "npx",
"args": ["-y", "perf-mcp"],
"env": {
"PERF_API_KEY": "pk_live_xxx"
}
}
}
}Environment Variables
Variable | Required | Description |
| Yes | Your Perf API key ( |
| No | Override API URL (for testing) |
Pricing
Plan | Credits | Price |
Free | 200 verifications | $0 (never expires) |
Pro | 1,000/mo | $19/mo |
Pay-as-you-go | Unlimited | $0.02/verification |
1 tool call = 1 credit. Get started at dashboard.withperf.pro.
License
MIT
Maintenance
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