apply_cluster_summaries

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

With no annotations, the description must fully disclose behavioral traits. It only states that data is persisted, without mentioning side effects (e.g., overwriting or appending), required permissions, or failure modes. Minimal disclosure.

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 a single, well-structured sentence that conveys the core purpose and payload constraints without extraneous 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?

The description lacks details on error handling, prerequisites (e.g., cluster existence, generated summaries), and integration with other tools. Given the write operation and absence of annotations, completeness is insufficient.

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 description adds meaning to the `summaries_json` parameter by specifying its expected shape (`{summaries: [...]}`), compensating for the 0% schema coverage. The `cluster_slug` parameter is not explained but is self-evident from its name.

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 persists per-paper summaries to Obsidian and Zotero, and specifies the payload format. However, it does not differentiate from sibling tools like `summarize_cluster` or `apply_crystals`, which could cause confusion.

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 mentions the tool is for when LLM was invoked out-of-band, implying a specific usage context. It does not provide explicit guidance on when not to use it or suggest alternatives, leaving the agent to infer usage.

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