As a CTO, this tool analyzes your team's historical DORA metrics (deployment frequency, lead time, MTTR, change failure rate) and GitHub pull request data to recommend an optimal code review depth. Input your repository identifier and time range, and receive a structured recommendation on review ...
Part of the Mcp Knowledge server.
Free to start. No card required.
AI agents use code_review_depth_optimizer to create or modify resources in Mcp Knowledge. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call code_review_depth_optimizer repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Mcp Knowledge.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"code_review_depth_optimizer": {
"limits": [
{
"counter": "code_review_depth_optimizer_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Mcp Knowledge policy for all 271 tools.
These attack patterns abuse exactly the kind of access code_review_depth_optimizer gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
As a CTO, this tool analyzes your team's historical DORA metrics (deployment frequency, lead time, MTTR, change failure rate) and GitHub pull request data to recommend an optimal code review depth. Input your repository identifier and time range, and receive a structured recommendation on review rigor (light, standard, thorough) with supporting metrics and risk-adjusted rationale.. It is categorised as a Write tool in the Mcp Knowledge MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for code_review_depth_optimizer: 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 Mcp Knowledge. Nothing to install.
code_review_depth_optimizer is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the code_review_depth_optimizer 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 code_review_depth_optimizer. 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.
code_review_depth_optimizer is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 271 Mcp Knowledge tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.