This record as markdown: /tools/io-github-dave-london-github/install.md
What install does on Github
AI agents invoke install 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 install is rated High
The description 'Runs' strongly implies execution of a command or process. Given the sibling tools (add-package, ansible-playbook, bazel) and GitHub operations context, 'install' most likely executes a package manager or build tool installation command. This is Execute category.
From the tool's definition Tool name is 'install' and description is 'Runs' (truncated/uninformative). In the context of a GitHub MCP server with sibling tools like add-package, ansible-playbook, bazel, the 'install' tool likely runs a package installation or build command.
Attacks that exploit this kind of access
The rule that runs install 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 install, this is the rule to start with:
install 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 install call is checked against it from then on.
Questions about install
Runs. 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 install: 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.
install 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 install 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 install. 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.
install 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.
More on Github, and thousands of servers like it.
Across the catalogue