mcp
MaxoPerf for AI agents — MCP server connector + the maxoperf agent skill. Mirror of the private monorepo; do not edit directly.
Documentation
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 maxoperfOr 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 key
- `list_workspaces` — List workspaces visible to the account
- `set_active_workspace` — Set the active workspace for the session
- `list_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 summary
- `create_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 state
- `download_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 filters
- `cancel_run` — Cancel a run (destructive; hidden in read-only)
- `rerun_run` — Re-run from a snapshot or the current test
- `add_runners` — Scale a live run up at existing locations
Results
- `get_run_results` — KPI overview: throughput, latency percentiles, error rate
- `query_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 tripped
- `get_run_error_bodies` — Sampled error request/response bodies + status codes
- `get_run_logs` — Error-level engine/system log lines
- `get_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-safe
- `get_openapi` — The public OpenAPI document
- `search_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 completion
- `diagnose-latency-regression` — Compare p95 across two runs
- `summarize-run` — Plain-language summary of one run
- `plan-and-build-test` — Turn a goal into project → test → upload → run
- `choose-executor` — Recommend an engine (k6 / JMeter / Playwright / Selenium)
- `scan-endpoints-for-hotspots` — Rank likely hotspots from an OpenAPI spec or pasted list
- `setup-secrets-and-envs` — Wire workspace secrets + multi-env before a run
- `diagnose-run-failure` — Ranked root cause for a failed run
- `explain-run-anomalies` — Explain each detected outlier
Resources
- `maxoperf://openapi` — The public OpenAPI spec
- `maxoperf://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 |
Frequently asked questions
What is mcp?
mcp is MaxoPerf for AI agents — MCP server connector + the maxoperf agent skill. Mirror of the private monorepo; do not edit directly.
How do I install mcp?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is mcp open source?
Yes — it is hosted on GitHub at https://github.com/MaxoPerf/mcp and has 1 stars.
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