Resolve a Keploy app and return a playbook the AI should walk to inspect the dev's repo for V1 ("user maintains the flow") test generation. This MCP server has no filesystem access — the AI is the inspector. The tool returns: the resolved app metadata (id, name, base_url, storage_mode) plus a det...
Part of the Keploy server.
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AI agents call devloop_detect_app to retrieve information from Keploy without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though devloop_detect_app only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"devloop_detect_app": {}
}
}See the full Keploy policy for all 103 tools.
These attack patterns abuse exactly the kind of access devloop_detect_app gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Resolve a Keploy app and return a playbook the AI should walk to inspect the dev's repo for V1 ("user maintains the flow") test generation. This MCP server has no filesystem access — the AI is the inspector. The tool returns: the resolved app metadata (id, name, base_url, storage_mode) plus a detection_playbook field that lists exactly what the AI should look for in app_dir (framework signatures, top-N candidate resources by handler count, port hints, existing keploy/api-tests/ directory). The AI uses its native read/grep tools to answer those questions, then calls devloop_generate_resource_flow with the findings. Resolution model — pass EITHER: * app_id (UUID) — exact match, fast path. * app_name_hint (case-insensitive substring) — matches against existing apps in your tenant; resolves to one app or errors with the candidate list when ambiguous. If storage_mode is unset, the DEVLOOP decision-gate defaults require devloop_resolve_storage to run first. This tool does not re-ask the storage question — it just surfaces whatever devloop_resolve_storage already persisted (or "" if neither has run yet).. It is categorised as a Read tool in the Keploy MCP Server, which means it retrieves data without modifying state.
Register the Keploy MCP server in PolicyLayer and add a rule for devloop_detect_app: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Keploy. Nothing to install.
devloop_detect_app is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the devloop_detect_app rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for devloop_detect_app. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
devloop_detect_app is provided by the Keploy MCP server (https://api.keploy.io/client/v1/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 103 Keploy tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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