Evaluates a MongoDB expression via mongosh and returns the output.
AI agents invoke mongosh-eval to trigger actions in Lint. 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 runs arbitrary MongoDB expressions through the mongosh shell. Depending on the expression, it could read, write, delete data, or perform administrative operations. Since it executes arbitrary code/expressions, it falls under Execute, and the blast radius is high because a malicious or erroneous expression could drop collections, exfiltrate data, or corrupt the database.
From the tool's definition Evaluates a MongoDB expression via mongosh
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 Lint, 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 Lint MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Lint 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 Lint. 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 Lint MCP server (Dave-London/Pare). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Lint, 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.
202 Lint tools catalogued and risk-classified — across an index of 43,000+ MCP servers.