Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case names, descriptions, inputSchema), JSON-RPC 2.0 test call, and P50/P95 latency. Returns a PASS/FIX/BLOCK verdict with a 0-100 score and per-...
Risk signalsAccepts URL/endpoint input (url)
Part of the IA-QA — 130+ QA & Dev Tools for AI Agents server.
Free to start. No card required.
AI agents invoke mcp_server_evaluate to trigger processes or run actions in IA-QA — 130+ QA & Dev Tools for AI Agents. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
mcp_server_evaluate can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"mcp_server_evaluate": {
"limits": [
{
"counter": "mcp_server_evaluate_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}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 mcp_server_evaluate gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case names, descriptions, inputSchema), JSON-RPC 2.0 test call, and P50/P95 latency. Returns a PASS/FIX/BLOCK verdict with a 0-100 score and per-check details.. It is categorised as a Execute tool in the IA-QA — 130+ QA & Dev Tools for AI Agents MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the IA-QA — 130+ QA & Dev Tools for AI Agents MCP server in PolicyLayer and add a rule for mcp_server_evaluate: 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.
mcp_server_evaluate is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the mcp_server_evaluate 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 mcp_server_evaluate. 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.
mcp_server_evaluate 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.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.