cls_ingest

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

No annotations are provided, so the description carries the full burden. It describes the input data structure for each source but does not disclose side effects, error handling, idempotency, or permissions. The lack of such detail limits 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 concise with three sentences. The first states the purpose, the second lists sources and their transformation, and the third mentions installation. It is front-loaded and contains no redundant information.

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 complexity (nested payload with three variants, no output schema, no annotations), the description covers the essential aspects. However, it lacks details on return values, error conditions, and prerequisites, which would make it more complete.

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 input schema has 100% description coverage for all three parameters. The description adds significant value by explaining the expected payload structure for each source type (git_commit, ci_outcome, ide_diagnostic), which goes beyond the schema's generic descriptions.

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 purpose: ingest learning signals without explicit session_end calls. It lists three specific sources (git_commit, ci_outcome, ide_diagnostic) and explains how each contributes to learning. This distinguishes it from sibling tools like session_end or auto_learn_session.

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 explains when to use the tool (to ingest learning signals from specific sources) and mentions an alternative/related tool cls_install_hooks for automatic ingestion. It provides context but does not explicitly exclude use cases or state when not to use it.

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