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제작자가 쓴 설명(영어 원문)
Simulation, evaluation and monitoring for voice agents.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://tools.okareo.com/mcp터미널에서 이 명령어를 실행하세요
claude mcp add --transport http okareo https://tools.okareo.com/mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"okareo": {
"url": "https://tools.okareo.com/mcp"
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"okareo": {
"type": "http",
"url": "https://tools.okareo.com/mcp"
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:
https://tools.okareo.com/mcp메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
아직 이 서버의 도구 목록을 읽지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 도메인 okareo.com 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트는 차례대로 돌아가며 점검해요
아직 점검하지 않았어요. 엔드포인트를 점검할 때 도구 목록을 읽어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
제작자의 클라우드에서 실행돼요. 설치 없이 인터넷으로 연결하지만, 보내는 내용은 제작자의 서버를 거쳐요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.0.57)은 22일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
같은 카테고리에는: GitHub, Desktop Commander, Cloudflare MCP
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
The Okareo MCP server exposes Okareo's evaluation capabilities as MCP tools, allowing AI coding assistants to create and manage scenarios, register models, run evaluations, and execute multi-turn simulations directly from your editor.
For detailed documentation, see the Okareo MCP docs.
The fastest way to get started. No local install, no Python, no API key in .mcp.json. Browser sign-in handles auth on first connect.
Add to your copilot's MCP config (typically .mcp.json):
{
"mcpServers": { "okareo": { "type": "http", "url": "https://tools.okareo.com/mcp" } } }
Reload the copilot. It will open a browser to Okareo sign-in once; thereafter the copilot stores the token itself. Your .mcp.json contains no secrets.
For clients that haven't shipped the MCP OAuth flow yet, paste your API key as a bearer header. Prefer the env-var form over an inline literal:
{
"mcpServers": { "okareo": { "type": "http", "url": "https://tools.okareo.com/mcp", "headers": { "Authorization": "Bearer ${env:OKAREO_API_KEY}" } } } }
GitHub Copilot (in VS Code and the Copilot CLI), Slackbot and every other client are set up at docs.okareo.com/mcp/configuration.
If you have access to more than one Okareo account, simply Clear Authentication and then re-authenticate with the account you want to work in. You can always ask which account your are in and have acccess to.
Run the MCP server on your own machine. Useful for offline / airgapped environments and for development.
Prefer the [Remote MCP](#remote-mcp-hosted--recommended) section above unless you have a specific reason to install locally (airgapped, custom build, development on this repo). The remote endpoint requires no install and stays current automatically.
For multi-org users, the remote endpoint also exposes tenant management tools (list_tenants, switch_tenant).
export OKAREO_API_KEY="your-api-key"
Add this to your ~/.zshrc or ~/.bash_profile for persistence.
Add to .mcp.json:
{
"mcpServers": { "okareo": { "command": "uvx", "args": ["okareo-mcp"], "env": { "OKAREO_API_KEY": "${OKAREO_API_KEY}" } } } }
No pre-install needed — uvx handles it automatically.
Add to .cursor/mcp.json:
{
"mcpServers": { "okareo": { "command": "uvx", "args": ["okareo-mcp"] } } }
Cursor inherits OKAREO_API_KEY from your shell environment.
If you don't have uv installed:
pip install okareo-mcp
Then use "command": "okareo-mcp" instead of "command": "uvx" with "args": ["okareo-mcp"].
| Variable | Default | Description |
|---|---|---|
OKAREO_API_KEY | *(required)* | Your Okareo API key |
OKAREO_BASE_URL | https://api.okareo.com | Override for on-prem Okareo backend |
TRANSPORT | stdio | Transport: stdio (local) or sse (Docker) |
PORT | 8000 | Port for SSE transport |
| Tool | Description |
|---|---|
save_scenario | Save a named scenario from rows of input/result data (idempotent) |
list_scenarios | List all scenarios in the project with names, IDs, and row counts |
get_scenario | Retrieve a scenario's metadata and all data rows by name or ID |
create_scenario_version | Create a new version of an existing scenario with updated data |
preview_delete_scenario | Preview what will be deleted before removing a scenario |
delete_scenario | Permanently delete a scenario and all related test data |
| Tool | Description |
|---|---|
list_available_llms | Browse available LLMs from the Okareo registry |
register_generation_model | Register a generation model for testing by selecting an LLM from the registry |
list_generation_models | List all registered generation models in the project |
get_generation_model | Read detailed information about a registered generation model |
update_generation_model | Change the LLM a registered generation model points to |
delete_generation_model | Remove a registered generation model and all its related test data |
| Tool | Description |
|---|---|
list_checks | List available quality checks (built-in and custom) for evaluating model outputs |
run_test | Run a quality test that evaluates a model against a scenario using specified checks |
list_test_runs | Find past test runs, most recent first. detail_level="summary" (default) or "detailed" for aggregate metrics |
get_test_run_results | Load one page of a run's results by ID or name. detail_level="summary" (default) / "detailed" (per-check outcomes + explanations) / "full" (transcripts). Defaults to 20 conversations. Carries a rerun block — the call and config to re-run this simulation |
get_conversation_transcript | Retrieve the full conversation transcript for a single data point |
reevaluate_test_run | Re-score a completed test run against a (possibly different) set of checks |
create_or_update_check | Create or update a quality check by name — model-based, code-based, or audio (upsert) |
generate_check | Generate a check from a natural-language description, then save it |
get_check | Retrieve a check's full configuration, including its prompt template or code |
delete_check | Permanently delete a check by name |
calibrate_check | Dry-run a draft check against a finished test run — per-row verdicts and the arguments the check received, saving nothing |
| Tool | Description |
|---|---|
create_or_update_target | Create or update a Target — generation model, custom endpoint, or voice (OpenAI, Deepgram, Twilio) |
get_target | Retrieve a Target's configuration by name (all types) |
list_targets | Find simulation targets (voice and custom_endpoint). Bounded to 20 by default; name_contains filters |
delete_target | Remove a simulation target and all its related test data |
create_or_update_driver | Define a simulated user persona that will interact with your target |
get_driver | Retrieve a Driver's full configuration including the persona prompt |
list_drivers | Find Driver personas. Bounded to 20 by default; name_contains filters |
list_driver_voices | Discover the voices, voice profiles, and languages available for voice drivers |
run_simulation | Run a multi-turn conversation evaluation. With based_on_run_id it inherits the original run's full configuration; any argument you pass overrides that one field |
list_simulations | List past simulation runs with optional filters (target, scenario, limit) |
| Tool | Description |
|---|---|
ingest_conversations | Submit completed voice conversations to Okareo for monitoring |
connect_voice_integration | Connect a voice provider so its traffic flows into Okareo monitoring |
list_voice_integrations | List the voice provider integrations in your project |
get_voice_integration | Retrieve a voice provider integration by ID, including its status |
update_voice_integration | Update a voice provider integration's metadata |
rotate_voice_integration_secret | Rotate a voice provider integration's secrets |
delete_voice_integration | Delete a voice provider integration by ID |
get_voice_webhook_url | Get the inbound webhook endpoint for a voice provider |
| Tool | Description |
|---|---|
list_projects | List every project you can work in — id, name, tags, archive state — with the active one marked |
select_project | Choose the project to work in for this conversation |
create_project | Create a new project |
update_project | Rename a project, or replace its tags |
archive_project | Hide a project from the project picker. Reversible; nothing is deleted |
unarchive_project | Restore an archived project to the picker |
clone_project | Copy an existing project's scenarios into a new project (dry-run first) |
Okareo has no project delete — in the app, the API, or here. Archiving is
the removal, and it only hides the project from the picker: its scenarios,
runs, and dashboards stay intact and every tool still works against it.
| Tool | Description |
|---|---|
query_analytics | Query Okareo's product analytics to understand evaluation trends |
list_dashboards | List the analytics dashboards in your project |
get_dashboard | Retrieve a dashboard's full configuration by name |
save_dashboard | Create or update an analytics dashboard by name (upsert) |
reorder_dashboards | Set the display order of dashboards |
delete_dashboard | Delete a dashboard by name |
| Tool | Description |
|---|---|
list_tenants | List every Okareo organization you have access to in this MCP session |
switch_tenant | Doesn't change the active organization; returns how to reconnect and pick another one at sign-in |
| Tool | Description |
|---|---|
get_docs | Query the Okareo documentation system for conceptual or user-legible explanations |
get_templates | Retrieve prompt templates for common Okareo patterns (works offline) |
get_reps_baseline | Serve REPS agent-evaluation baseline material (scenario banks, drivers, checks, eval configs) from the latest tagged okareo-tools release |
| Symptom | Cause | Fix |
|---|---|---|
okareo-mcp: command not found | Not installed or not in PATH | Run pip install -e . (dev) or use uvx okareo-mcp (user) |
| Server exits with API key error | OKAREO_API_KEY not set | Export it: export OKAREO_API_KEY="..." |
pip install fails on Python 3.13+ | Okareo SDK requires Python <3.13 | Use Python 3.10–3.12 |
| Copilot can't connect (Docker) | Wrong URL | Ensure URL ends with /sse and port matches |
| Cursor doesn't pick up API key | Cursor launched from Dock, not terminal | Launch Cursor from terminal: cursor . |
This repository is a curated public mirror; the canonical source is maintained by Okareo. We welcome issues and consider community pull requests — see CONTRIBUTING.md for how proposed changes are reviewed and ported.
The Okareo MCP server source code is licensed under the Apache License 2.0 (see also NOTICE).
"Okareo", the Okareo logo, and Okareo product names are trademarks of Okareo, Inc. and are not covered by the Apache 2.0 license. See TRADEMARK.md for permitted use.