About MaxoPerf MCP
MaxoPerf MCP is a hosted performance-testing service for AI agents, covering test setup, cloud runs, results, and diagnostics in natural language. It includes read-only access and an agent skill that can inspect code and plan tests.
Key features
- 01Create and manage performance tests
- 02Launch load tests on managed cloud runners
- 03Review throughput, latency, and error metrics
- 04Inspect logs, anomalies, and root causes
- 05Use a read-only access mode
Use cases
- 01Run load or browser tests against an API
- 02Compare latency across test runs
- 03Investigate failures and runner load shortfalls
MCP support
This hosted remote MCP server exposes tools for tests, runs, results, diagnostics, and API discovery, along with prompts and resources. Clients connect using an API key; read-only mode hides write tools.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://app.maxoperf.com/mcpclaude mcp add --transport http maxoperf https://app.maxoperf.com/mcp{"mcpServers":{"maxoperf":{"type":"http","url":"https://app.maxoperf.com/mcp"}}}Open in Cursor{"servers":{"maxoperf":{"type":"http","url":"https://app.maxoperf.com/mcp"}}}Open in VS Code{"mcpServers":{"maxoperf":{"type":"http","url":"https://app.maxoperf.com/mcp"}}}{"mcpServers":{"maxoperf":{"serverUrl":"https://app.maxoperf.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://app.maxoperf.com/mcpAuth requiredTools
Details
From the project README⚡ MaxoPerf MCP Server
Drive the entire MaxoPerf platform from any AI agent — with your API key.
A remote, hosted Model Context Protocol server that turns Claude, Cursor, Codex, VS Code Copilot, and ChatGPT into a full performance-testing operator. Create tests, launch load runs on managed cloud runners, read results, and get root-cause analysis — all in natural language, all authenticated with your own key.
https://app.maxoperf.com/mcp
Configuration
It's a remote, hosted server — nothing to install or run. Add this to your MCP client config and set MAXOPERF_API_KEY (create one in the console → Settings → API keys):
{
"mcpServers": {
"maxoperf": {
"type": "http",
"url": "https://app.maxoperf.com/mcp",
"headers": {
"Authorization": "Bearer ${MAXOPERF_API_KEY}"
}
}
}
}
Your key is validated on every call by the real platform (auth, tenancy, OpenFGA, audit) — the server stores nothing and adds no new trust boundary. Revoke the key, access dies instantly. Send the header X-MaxoPerf-MCP-Mode: read-only for a look-but-don't-touch session (write tools hidden).
Install (pick your client)
Claude Code — the plugin bundles the server and the agent skill:
/plugin marketplace add MaxoPerf/mcp
/plugin install maxoperf
Or add just the connector:
claude mcp add --transport http maxoperf https://app.maxoperf.com/mcp \
--header "Authorization: Bearer ${MAXOPERF_API_KEY}"
Cursor · VS Code / Copilot · Codex · ChatGPT · Claude Desktop — one-click deeplinks and copy-paste config in packaging/. All use the same mcpServers block above.
Tools
29 curated tools. Reads default to response_format: "concise" (pass "detailed" for the full payload); write tools require a non-read-only session, and cancel_run is hidden in read-only mode.
Context & tenancy
whoami— Resolve the account + default workspace behind your API keylist_workspaces— List workspaces visible to the accountset_active_workspace— Set the active workspace for the sessionlist_projects— List projects (with edit/delete permissions)create_project— Create a project
Tests
list_tests— List tests (filter by project / workspace / type)get_test— Get one test + its validation summarycreate_test— Create a test shell (choose the engine/executor)get_test_overview— Run-history overview for a test
Test files
upload_test_file— Upload a script/data file in one call (real 3-step presigned flow)list_test_files— List a test's files + upload statedownload_test_file— Get a short-lived download URL for a file
Runs
start_run— Launch a load/browser run on managed cloud runners (idempotent)get_run_status— Poll lifecycle status (queued → running → passed/failed/cancelled)list_runs— Paginated run history with filterscancel_run— Cancel a run (destructive; hidden in read-only)rerun_run— Re-run from a snapshot or the current testadd_runners— Scale a live run up at existing locations
Results
get_run_results— KPI overview: throughput, latency percentiles, error ratequery_run_metrics— Time-series metrics (latency / throughput / errors / load / health)get_run_errors— Grouped error rows (message / count / code)
Diagnostics — root-cause & anomaly detection
get_run_summary— Executive summary + which failure criteria trippedget_run_error_bodies— Sampled error request/response bodies + status codesget_run_logs— Error-level engine/system log linesget_runner_health— Runner CPU/mem trend + targetVus vs peakAchievedVus (vuShortfallPct)detect_run_anomalies— Deterministic robust-outlier scan (median/MAD); terminal-gated, low false-positive
Escape hatch & discovery
call_platform_api— Reach any public/v1/*endpoint (secrets, environments, schedules, BYOC); admin/internal deny-listed, SSRF-safeget_openapi— The public OpenAPI documentsearch_endpoints— Keyword search over the API to find the right endpoint
Prompts
Text recipes that encode the correct tool sequence — great for chat-only clients that can't read a repo:
run-baseline-load-test— Start a baseline run and watch it to completiondiagnose-latency-regression— Compare p95 across two runssummarize-run— Plain-language summary of one runplan-and-build-test— Turn a goal into project → test → upload → runchoose-executor— Recommend an engine (k6 / JMeter / Playwright / Selenium)scan-endpoints-for-hotspots— Rank likely hotspots from an OpenAPI spec or pasted listsetup-secrets-and-envs— Wire workspace secrets + multi-env before a rundiagnose-run-failure— Ranked root cause for a failed runexplain-run-anomalies— Explain each detected outlier
Resources
maxoperf://openapi— The public OpenAPI specmaxoperf://run/{id}— A run report summary
Try it
- "Load test https://api.example.com/checkout with 500 users for 5 minutes and fail it if p95 goes over 800ms."
- "Scan my repo for the endpoints most worth load-testing, then build and run a test for the riskiest one."
- "Why did run run-0000000001 fail? Check the errors, the logs, and whether the runners actually reached the target load."
Pair it with the brain
The MCP server is the hands. The bundled MaxoPerf agent skill (npx @maxoperf/agent-skill install, included in the Claude plugin, or in agent-skill/ here) is the brain — it reads your code, finds the hotspots, builds and runs the test, and diagnoses why it broke, driving these tools automatically.
What's in this repo
| Path | What |
|---|---|
.claude-plugin/ | Claude Code plugin (bundles the MCP connector + the skill) |
agent-skill/ | A copy of the maxoperf agent skill (canonical home: MaxoPerf/agent-skill) |
packaging/ | MCP Registry server.json, .mcpb, VS Code / Cursor deeplinks, Codex / ChatGPT setup |
LAUNCHGUIDE.md | MCP directory listing metadata |
Get started → · Agent skill → · Docs →
Read-only mirror of the MaxoPerf monorepo. File issues at maxoperf.com.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.