get_agent_productivity_hourly_heatmap
Hour-of-day × Agent completion heatmap. Buckets done assignments by the hour (0-23) of completed_at and agent_id, returning a matrix plus per-agent totals and the fleet peak hour. Reveals when each Agent is most productive.
This record as markdown: /tools/todo-for-ai-todo-for-ai-mcp/get-agent-productivity-hourly-heatmap.md
What get_agent_productivity_hourly_heatmap does on Todo for AI MCP Server
AI agents call get_agent_productivity_hourly_heatmap to retrieve information from Todo for AI MCP Server without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why get_agent_productivity_hourly_heatmap is rated Low
Even though get_agent_productivity_hourly_heatmap only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs get_agent_productivity_hourly_heatmap safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Todo for AI MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For get_agent_productivity_hourly_heatmap, this is the rule to start with:
get_agent_productivity_hourly_heatmap is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Todo for AI MCP Server, apply this rule, and every get_agent_productivity_hourly_heatmap call is checked against it from then on.
Questions about get_agent_productivity_hourly_heatmap
Hour-of-day × Agent completion heatmap. Buckets done assignments by the hour (0-23) of completed_at and agent_id, returning a matrix plus per-agent totals and the fleet peak hour. Reveals when each Agent is most productive. It is categorised as a Read tool in the Todo for AI MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Todo for AI MCP Server MCP server in PolicyLayer and add a rule for get_agent_productivity_hourly_heatmap: 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 Todo for AI MCP Server. Nothing to install.
get_agent_productivity_hourly_heatmap is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_agent_productivity_hourly_heatmap 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 get_agent_productivity_hourly_heatmap. 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.
get_agent_productivity_hourly_heatmap is provided by the Todo for AI MCP Server MCP server (todo-for-ai/todo-for-ai-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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