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

With no annotations, the description carries the burden of behavioral disclosure. It states the tool returns 'prose + citations' and uses a 'grounded LLM,' which clarifies output format and sourcing behavior. However, it does not mention any potential limitations, latency, or side effects, though for a synthesis tool the read-only nature is implied.

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 three sentences, front-loaded with the most important information (what it does, what it returns) followed by usage guidance and a distinction from a sibling. Every sentence earns its place; no fluff or redundancy.

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 tool is relatively simple (2 params, no output schema) and the description covers its core behavior and primary alternative. However, it fails to explain the 'system_prompt' parameter and does not address other related research tools (e.g., fetch_url, x_sentiment) in the sibling list, leaving some gaps in a fully self-contained description.

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 description coverage is 0%, so the description must compensate. It gives an example query ('summarize NVIDIA's last earnings call and link the transcript') which implies what 'query' means, but it says nothing about the 'system_prompt' parameter. Half the parameters are unexplained, and the description adds minimal semantic value beyond the schema's bare string types.

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 a specific verb+resource: 'Citation-rich research synthesis (grounded LLM). Returns a concise synthesized answer plus the list of source URLs that grounded it.' It also distinguishes itself from web_search, making the purpose unambiguous.

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?

Explicit when-to-use guidance is provided: 'Use when the user wants the answer GROUNDED with explicit sources' with a concrete example. It also contrasts with 'web_search — this returns prose + citations, not a list of headlines,' clarifying 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.