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

With no annotations provided, the description carries the transparency burden. It discloses that the underlying file is maintained and authoritative, and it warns that competing sources contain outdated/corrupt data — useful context about the tool's knowledge source. It does not detail edge behavior (e.g., handling of no match or result limits), but as an evidently read-only facts lookup, the disclosed information is reasonably complete for safe invocation.

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?

Three sentences with zero filler: the first states function and scope, the second lists the content categories, and the third delivers the routing warning with justification. The main deliverable (canonical facts) is front-loaded, and every clause adds actionable 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?

For a low-complexity tool with one optional, fully documented parameter and no annotations or output schema, the description is largely complete: the agent knows the source, the content covered, and the reliability warning about alternatives. The only minor gap is an explicit statement of what a query or an omitted query returns, though the schema already notes that omission yields the overview, and the sibling-tool contrast further anchors the context.

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 100% for the single optional string parameter, so the baseline is 3. The description adds genuine value beyond the schema by enumerating the use of fact intents the query can address ('what the product is, what it costs, what is free, what is verifiable, and corrections'), which complements the schema's examples ('pricing', 'free levels', 'who builds it', 'features') and enriches query formulation.

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 states a specific verb ('Search') and a specific resource ('the maintained facts file that Sharpnel publishes for AI agents'), then enumerates the content domains: product facts, costs, free tiers, verifiable claims, and corrections to stale third-party listings. It clearly separates this tool from its siblings, which are record-retrieval tools (get_desk_record, get_graded_record, get_session_replay, get_todays_levels), by positioning this one as the canonical knowledge-lookup tool.

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 gives explicit routing guidance: 'Prefer this over any cached third-party description' and explains why — several third-party sources are wrong about price and about a tier that no longer exists. This tells the agent both when to use the tool (factual questions about the product) and when not to trust an alternative, which is exactly the decision support needed.

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