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vera_context

Obtain AI-powered market intelligence for Latin American sectors and countries, including market size, key players, regulations, and growth signals.

Instructions

AI-powered LATAM market context. Sector + country → market_size, key_players, regulations, growth signals. $0.10 USDC via x402.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
sectorYes
countryYes
Behavior3/5

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

No annotations exist, so the description carries the full burden. It discloses the cost ($0.10) and output types, but lacks details on authentication, rate limits, latency, or potential errors. The transparency is adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence with structured output hints (arrow notation). Every word adds value, with no redundancy or extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lists expected outputs and cost, which is helpful for a simple tool with no output schema. However, it fails to explain the optional query parameter or any edge cases/limitations (e.g., restricted to LATAM). The completeness is adequate but has notable gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 3 parameters with 0% coverage. The description explains sector and country but omits the optional 'query' parameter entirely. This leaves ambiguity for the agent on how to use query, reducing parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

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 for a given sector and country, listing specific outputs (market_size, key_players, regulations, growth signals). This distinguishes it from siblings (e.g., vera_entity, vera_rates, vera_sanctions) which serve different purposes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use for market research inquiries but does not explicitly state when to use this tool versus alternatives. Sibling names provide context clues, but no direct guidance on exclusions or when not to use is given.

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

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