AI agents invoke expression_evaluator to trigger actions in Math MCP Server. 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.
An 'expression_evaluator' tool likely executes or interprets mathematical expressions, potentially including arbitrary code depending on implementation. Given the empty description, there is uncertainty, but expression evaluation commonly involves executing parsed input which could be misused (e.g., code injection). The most severe plausible category given the name and context is Execute.
From the tool's definition Tool name 'expression_evaluator' on a math server with sibling tools like 'calculus_engine' and 'mathematical_functions'; description is empty and uninformative.
Documented attack patterns abuse exactly the kind of access expression_evaluator gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Math MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for expression_evaluator:
{
"version": "1",
"default": "deny",
"tools": {
"expression_evaluator": {
"limits": [
{
"counter": "expression_evaluator_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}expression_evaluator 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.
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expression_evaluator. It is categorised as a Execute tool in the Math MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Math MCP Server MCP server in PolicyLayer and add a rule for expression_evaluator: 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 Math MCP Server. Nothing to install.
expression_evaluator 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 expression_evaluator 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 expression_evaluator. 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.
expression_evaluator is provided by the Math MCP Server MCP server (111-test-111/math-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Math MCP Server, 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.
22 Math MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.