mongosh-eval
Evaluates a MongoDB expression via mongosh and returns the output.
This record as markdown: /tools/io-github-dave-london-github/mongosh-eval.md
What mongosh-eval does on Github
AI agents invoke mongosh-eval to trigger actions in Github. 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.
Why mongosh-eval is rated High
This tool executes arbitrary MongoDB expressions through the mongosh shell. Depending on the expression passed, it can read, write, delete, or drop data — including destructive operations. Since the capability is unbounded and depends on arguments, Execute is the correct category (with Destructive being a possible subset).
From the tool's definition Evaluates a MongoDB expression via mongosh
Attacks that exploit this kind of access
The rule that runs mongosh-eval safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Github, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For mongosh-eval, this is the rule to start with:
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.
The button opens the PolicyLayer dashboard: create your workspace, connect Github, apply this rule, and every mongosh-eval call is checked against it from then on.
Questions about mongosh-eval
Evaluates a MongoDB expression via mongosh and returns the output. It is categorised as a Execute tool in the Github MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Github 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 Github. 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 Github MCP server (@paretools/github). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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