Get Analytics

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only and non-destructive behavior. The description aligns with this (compute) and adds no extra context about auth, rate limits, or side effects, so it is neutral rather than additive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise, front-loaded with the core purpose, and uses a clear list of actions with parenthetical explanations. It conveys substantial detail without excessive verbosity, though it is slightly longer than necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives a good idea of what each action returns (e.g., overview, chart, metrics) but does not specify the exact output structure or format. Since there is no output schema, the description adequately informs the agent of expected results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers all parameters with descriptions, but the tool description adds value by mapping specific parameters to actions (e.g., days for burndown, window_days for velocity, milestone_id for critical_path). This enhances understanding beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool computes various analytics metrics and lists supported actions like summary, velocity, burndown, etc. It is specific and distinguishable from sibling tools like query_graph or get_events by focusing on derived metrics rather than raw data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists supported actions but does not explicitly state when to prefer this tool over alternatives. It implies usage for analytics but lacks direct comparison or conditional guidance (e.g., 'use this for high-level metrics; use query_graph for raw data').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.