vera_context

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

No annotations are provided, so the description must fully disclose behavioral traits. It mentions the cost ($0.10 USDC via x402) and output categories, but lacks details on auth, rate limits, data freshness, or side effects. The 'AI-powered' claim gives minimal insight.

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, highly concise and front-loaded with the core purpose. Every sentence provides value without 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 has 3 parameters and no output schema. The description lists return fields but no structure or example. For a simple tool, it is adequate but lacks completeness for an agent to fully understand return format or optional parameter usage.

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 0% with no parameter descriptions. The description explains that sector and country are inputs for market context, but does not clarify the format or restrictions for the 'query' parameter (optional). The enum for country is in schema, but no further semantics added.

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 provides 'AI-powered LATAM market context' with specific outputs (market_size, key_players, regulations, growth signals) and requires sector and country inputs. It effectively distinguishes from sibling tools like vera_rates, vera_sanctions, and vera_entity.

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 usage for market context but does not explicitly state when to use this tool versus siblings or provide any exclusions. Sibling names are mentioned but no guidance on selection criteria.

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