Evaluates a MongoDB expression via mongosh and returns the output.
AI agents invoke mongosh-eval to trigger actions in Http. 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.
This tool executes arbitrary MongoDB expressions via the mongosh shell. An AI agent could issue any MongoDB command, including destructive operations (db.dropDatabase(), collection.deleteMany(), etc.), data exfiltration queries, or schema modifications. Because it can run arbitrary expressions — not just reads — the most severe applicable category is Execute (and it borders on Destructive).
From the tool's definition "Evaluates a MongoDB expression via mongosh" — runs arbitrary code/expressions against a MongoDB instance through the mongosh shell
Documented attack patterns abuse exactly the kind of access mongosh-eval gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Http, and nothing reaches the server without passing your rules. This is the rule we recommend for mongosh-eval:
{
"version": "1",
"default": "deny",
"tools": {
"mongosh-eval": {
"limits": [
{
"counter": "mongosh-eval_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}mongosh-eval 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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Evaluates a MongoDB expression via mongosh and returns the output. It is categorised as a Execute tool in the Http MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Http MCP server in PolicyLayer and add a rule for mongosh-eval: 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 Http. Nothing to install.
mongosh-eval 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 mongosh-eval 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 mongosh-eval. 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.
mongosh-eval is provided by the Http MCP server (@paretools/http). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Http, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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202 Http tools catalogued and risk-classified — across an index of 43,000+ MCP servers.