Run declarative assertions on an agent trace (OpenAI tool-call messages, LangChain run trees, or plain text logs). No LLM call — deterministic. Assertion types: order (tool A before B), must_call, must_not_call, max_calls, min_calls, no_error, recovery (agent continues after error). Returns per...
Risk signalsHigh parameter count (10 properties)
Part of the IA-QA — 130+ QA & Dev Tools for AI Agents server.
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AI agents call validate_agent_trajectory to retrieve information from IA-QA — 130+ QA & Dev Tools for AI Agents 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 validate_agent_trajectory 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": {
"validate_agent_trajectory": {}
}
}See the full IA-QA — 130+ QA & Dev Tools for AI Agents policy for all 146 tools.
These attack patterns abuse exactly the kind of access validate_agent_trajectory 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.
Run declarative assertions on an agent trace (OpenAI tool-call messages, LangChain run trees, or plain text logs). No LLM call — deterministic. Assertion types: order (tool A before B), must_call, must_not_call, max_calls, min_calls, no_error, recovery (agent continues after error). Returns per-assertion PASS/FAIL, parsed steps, and an overall verdict. Use this to gate CI/CD on agent behavior correctness.. It is categorised as a Read tool in the IA-QA — 130+ QA & Dev Tools for AI Agents MCP Server, which means it retrieves data without modifying state.
Register the IA-QA — 130+ QA & Dev Tools for AI Agents MCP server in PolicyLayer and add a rule for validate_agent_trajectory: 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 IA-QA — 130+ QA & Dev Tools for AI Agents. Nothing to install.
validate_agent_trajectory 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 validate_agent_trajectory 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 validate_agent_trajectory. 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.
validate_agent_trajectory is provided by the IA-QA — 130+ QA & Dev Tools for AI Agents MCP server (https://www.ia-qa.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 146 IA-QA — 130+ QA & Dev Tools for AI Agents tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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