Powerful runtime expression evaluator: Test hypotheses, check computed values, call methods, or inspect object properties in the live debug context. Goes beyond simple variable inspection - evaluate any valid expression in the target language.
AI agents invoke evaluate_expression to trigger actions in DebugMCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
Although framed as a debugging aid, the ability to evaluate arbitrary expressions and call methods in a live runtime context constitutes code execution. An AI agent could leverage this to manipulate program state, exfiltrate data, or trigger unintended side effects.
From the tool's definition Tool description states it can 'evaluate any valid expression in the target language' and 'call methods' in a live debug context. This enables arbitrary code execution within the debugged process.
Documented attack patterns abuse exactly the kind of access evaluate_expression gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and DebugMCP, and nothing reaches the server without passing your rules. This is the rule we recommend for evaluate_expression:
{
"version": "1",
"default": "deny",
"tools": {
"evaluate_expression": {
"limits": [
{
"counter": "evaluate_expression_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}evaluate_expression stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Powerful runtime expression evaluator: Test hypotheses, check computed values, call methods, or inspect object properties in the live debug context. Goes beyond simple variable inspection - evaluate any valid expression in the target language. It is categorised as a Execute tool in the DebugMCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Debug MCP server in PolicyLayer and add a rule for evaluate_expression: 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 DebugMCP. Nothing to install.
evaluate_expression 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 evaluate_expression 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 evaluate_expression. 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.
evaluate_expression is provided by the Debug MCP server (microsoft/debugmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from DebugMCP, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
13 DebugMCP tools catalogued and risk-classified — across an index of 43,000+ MCP servers.