Fetch comprehensive context about an app's API plus the canonical test-suite authoring schema. Returns: * app — config, auth shape, appLevelCustomVariables (READ THIS for R32 — your extract keys must not collide with these names) * coverage — API coverage report (which endpoints have tests, which...
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AI agents call get_app_testing_context 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 get_app_testing_context 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": {
"get_app_testing_context": {}
}
}See the full Keploy policy for all 103 tools.
These attack patterns abuse exactly the kind of access get_app_testing_context 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.
Fetch comprehensive context about an app's API plus the canonical test-suite authoring schema. Returns: * app — config, auth shape, appLevelCustomVariables (READ THIS for R32 — your extract keys must not collide with these names) * coverage — API coverage report (which endpoints have tests, which don't) * recordings — summaries of captured traffic sessions * test_suites — existing suites (check before authoring to avoid duplicates) * generated_schema — AI-extracted OpenAPI for the app * step_schema — THE CANONICAL TEST SUITE STEP SCHEMA. Same content as keploy test-suite-format, shipped inline so you don't need a separate tool-call hop. Read this BEFORE authoring or curling endpoints — it contains the MANDATORY rule block (R10 / R9 / R2 / R15 / R32) the validator enforces on iter 1, plus the canonical two-step prelude+POST skeleton. * recommended_substitutions — actionable mapping of existing app-level vars to the field types they cover. Structure: {dynamic_generators: [{key, reference, type, suggested_for}], static_fixtures: [...], usage_guidance: "..."}. PREFER referencing one of these over declaring a prelude step. Dynamic generators satisfy R9/G24 for POST/PUT/PATCH bodies; Static fixtures do NOT (use them only in headers/query/asserts). When this list is empty for the field you need, call update_app_custom_variables to add a gen* var instead of building a prelude step. * authoring_templates — concrete, ready-to-fill suite skeletons indexed by endpoint shape. Structure: {overview: "...", templates: [{endpoint_shape, when_to_use, steps, notes}, ...]}. PRIMARY AUTHORING PATH: match your intent to the closest template's endpoint_shape, copy its steps, fill the <FILL: ...> blanks, replace {{genXxx}} placeholders with the matching keys from recommended_substitutions (or call update_app_custom_variables to add a missing one), capture real responses via curl (R10), then validate via the create_test_suite playbook. Templates encode the opinionated default for common patterns — author from scratch only for genuine edge cases. * authoring_directive — one-line reminder pointing at step_schema and authoring_templates. Call this FIRST when authoring suites. The step_schema field eliminates the most common iter-1 failure (AI authors based on training-data priors before reading the validator's rules).. 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 get_app_testing_context: 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.
get_app_testing_context 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 get_app_testing_context 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 get_app_testing_context. 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.
get_app_testing_context 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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