agent.knowledge-delta

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

With no annotations provided, the description bears full burden for behavioral disclosure. It discloses that the tool aggregates from multiple sources, deduplicates, and ranks results. However, it does not mention potential rate limits, pagination, or what happens if no results are found, leaving some gaps.

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 two sentences, front-loaded with the typical usage pattern, and every sentence provides essential information. It is concise with no wasted words.

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?

No output schema exists, but the description covers inputs, purpose, sources, deduplication, ranking, and the intended use case (catching up from training cutoff). This is sufficient for an agent to understand the tool's capabilities and when to invoke it.

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?

Schema coverage is 75%, and the description adds context by mapping 'topic' and 'since' to the natural language example. However, it does not elaborate on 'until' or 'maxEvents' beyond what the schema already provides. The schema descriptions are adequate, so the description adds marginal value.

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: listing what's happened in a topic since a date, aggregating multiple sources (regulations, court opinions, papers, House+Senate votes), deduplicating and ranking results. This distinguishes it from sibling tools that focus on single sources (e.g., gov.house-votes, law.opinion).

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 explicitly states the tool is designed for an agent to 'catch up since its LLM training cutoff' in one call, providing clear context for when to use it. It does not explicitly state when not to use it or name alternative tools, but the use case is well-defined.

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