Runs go mod tidy to add missing and remove unused module dependencies.
AI agents invoke mod-tidy to trigger actions in Python. 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 the 'go mod tidy' command, which modifies the go.mod and go.sum files by adding missing dependencies and removing unused ones. It triggers an external operation (running a shell command) that modifies project files. While it writes/modifies files, the primary risk is the execution of an external command that could alter dependency resolution in potentially undesirable ways.
From the tool's definition Runs go mod tidy to add missing and remove unused module dependencies
Documented attack patterns abuse exactly the kind of access mod-tidy gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Python, and nothing reaches the server without passing your rules. This is the rule we recommend for mod-tidy:
{
"version": "1",
"default": "deny",
"tools": {
"mod-tidy": {
"limits": [
{
"counter": "mod-tidy_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}mod-tidy 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.
Runs go mod tidy to add missing and remove unused module dependencies. It is categorised as a Execute tool in the Python MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Python MCP server in PolicyLayer and add a rule for mod-tidy: 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 Python. Nothing to install.
mod-tidy 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 mod-tidy 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 mod-tidy. 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.
mod-tidy is provided by the Python 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 Python, 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 Python tools catalogued and risk-classified — across an index of 43,000+ MCP servers.