lookup_existing_factchecks

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

With no annotations, the description carries the behavioral disclosure burden. It usefully states the return result (a list) and the non-obvious behavior that errors or a missing key yield an empty list rather than an exception. It does not disclose matching semantics, pagination, or data-source behavior, so the disclosure is only partial.

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 front-loaded with the purpose sentence, followed by tersely useful Args and Returns sections. There is no filler, and each line contributes operational 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 tool is simple and an output schema exists, so the description covers the core args and return behavior. However, it leaves gaps: an agent is not told what matching means (fuzzy/exact), why the result could be empty on 'missing key', or how this step fits into a fact-checking workflow.

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 explain the parameters, and it does: query is described as the claim text and language_code as a BCP-47 code with a default. This adds real meaning beyond the bare string types in the schema.

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 opening sentence names a specific verb ('Search'), a resource ('existing fact-checks'), and a match condition ('matching a claim'), so an agent immediately knows what is returned. The domain is clearly distinct from statistical-data siblings like eurostat_search_datasets and worldbank_search_indicators.

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 implies when to use it — when a claim needs to be checked against previously fact-checked content — but it does not explicitly state when not to use it or how it relates to related siblings such as score_check_worthiness or analyze_cherry_picking. No alternative tool is named.

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