For AI agents & tool use · no real credential, ever

Give your agent a world it can't break.

Your agent never holds a real credential here. Any token is accepted and every host is read-only, so a leaked auth header or an over-eager tool call cannot reach anything real. MCP servers, LangChain tools, function-calling integrations and coding assistants get a believable data set across the developer tools they already call — without real-provider quotas burned on eval runs.

Set up MCP Get a free API key

The agent loop, without the riskREST · GraphQL · MCP-ready

01 / Agent-native demo

Watch the tool call land.

tool_call · list_issues

{
  "name": "list_issues",
  "arguments": {
    "owner": "olympus-labs",
    "repo": "parthenon",
    "state": "open"
  }
}

tool_result · application/json

{
  "number": 84,
  "title": "Proposal: type the event emitter",
  "state": "open",
  "user": "tiresias",
  "labels": [
    "enhancement"
  ],
  "comments": 2,
  "created_at": "2026-07-25T00:34:40Z",
  "html_url": "https://gh.sandboxapis.dev/olympus-labs/parthenon/issues/84"
}

No token · no cleanup · no surprisesSame response over HTTP: https://gh-2026-03.snap.sandboxapis.dev/repos/olympus-labs/parthenon/issues/84

02 / Built for the loop

Your agent needs a world. Give it one.

Give your tools a stable world to explore. Your credentials — and your cleanup scripts — can stay home.

MCP server development

Build and debug API tools against a stable data set before an MCP client ever touches production.

LLM eval harnesses

Replay the same repo, issue, and pull request scenarios across every model run.

Coding assistant sandboxing

Let agents inspect branches and read PRs without handing them the keys to your real codebase.

CI integration testing

Exercise tool calls in parallel. No flaky credentials, cleanup jobs, or provider throttling.

Agents that read team context

Point a conversations.history reader at a workspace it can't break — where the chatter reconciles with the pull requests, issues, and commits every other host serves.

03 / Production is a bad sandbox

Agents are fast. Production is permanent.

Development needs a reset button. Your real accounts do not come with one.

Token leaks in logs

An agent can echo a credential, a private URL, or an entire auth header while you are still instrumenting it. Here there is nothing real to echo: any token is accepted, so the agent never holds one.

Real repo mutations

One enthusiastic tool call can close an issue, push a branch, or rewrite a production-facing description.

Eval non-determinism

Live repos change underneath your harness. The same prompt gets a different world on the next run.

Rate-limit fragility

Batch evals multiply requests fast — against real providers that's quota burned and bills run up. Here a free key and a deterministic world mean a rerun costs exactly what the last run cost.

04 / How it works

Two lines of setup. Zero cleanup.

Read the MCP guide

Point your tool's base_url at us

Keep the SDK, auth shape, and tool schema. Swap one environment variable.

Or add the MCP server

One config line and the agent orients itself — no docs reading, no human setup.

Run the loop, repeatedly

Spin up parallel runs against the same believable repos, issues, users, and PRs.

Ready for the next run?

Let your agent break something fake.

Know your agent can handle the happy path and the weird path before it meets production.

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