pipeline_summary

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

No annotations are provided, so the description carries the burden. It explicitly states 'Read-only,' which is a key behavioral trait. It also discloses what data is returned (stages with candidate counts, names, days-in-stage, last activity). It doesn't mention rate limits or authorization requirements, but for a read-only aggregation tool, this is a reasonable level of transparency.

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 compact and well-organized. It starts with the core purpose, includes user-facing examples, provides a prerequisite step, outlines the return content, and ends with efficiency value. Every sentence adds meaningful information—no filler. It is front-loaded and easy to scan.

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?

Given the tool's simplicity (one parameter, read-only, output schema present), the description is fully complete. It covers what the tool does, when to use it, how to get the required ID, what it returns, and why it's beneficial. The agent has all necessary context to select and invoke it correctly.

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 only parameter, job_id, already has a description in the schema ('Greenhouse job ID — list_jobs → match by name'), and the description reinforces this with a concrete workflow. Since schema coverage is 100%, the baseline is 3, but the additional context about how to obtain the job_id adds value, justifying a 4.

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's function: 'Complete pipeline view for a job — candidates grouped by stage.' It also provides user examples ('show me the pipeline for Backend Engineer') and conveys the resource (job pipeline). While it doesn't explicitly differentiate from siblings like pipeline_metrics, the 'Complete' framing and focus on stage grouping set it apart to some degree.

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 gives clear usage context with example queries ('how many candidates are in each stage') and a prerequisite workflow ('To find the job_id: list_jobs → match by name'). It also highlights a value proposition ('One call replaces 5-10 sequential API calls'). However, it does not explicitly mention alternatives or exclusions, so it stops short of full guidelines.

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