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denue_near

Find businesses and economic units near any location in Mexico using INEGI's DENUE registry. Input coordinates to get nearby establishments with filtering options.

Instructions

Economic units (businesses) near a point (INEGI DENUE).

Requires env INEGI_TOKEN. Free registration: https://www.inegi.org.mx/servicios/api_denue.html

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYes
lngYes
radius_mNo
keywordNotodos
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses the need for an INEGI_TOKEN environment variable and a free registration link, which is useful for authentication. However, it doesn't describe behavioral traits like rate limits, response format, error handling, or whether this is a read-only operation. The mention of 'near a point' implies a query, but more details are needed for a mutation-aware agent.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, with the core purpose stated first. The two sentences are efficient: one defines the tool, and the other covers authentication. There's no wasted text, making it easy to scan, though it could be slightly more structured (e.g., bullet points for parameters).

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?

Given the complexity (5 parameters, no annotations, but an output schema exists), the description is incomplete. It covers authentication and the high-level purpose but misses parameter semantics and behavioral context. The output schema may handle return values, but without annotations, the description should do more to explain usage, constraints, and how results are structured. It's minimally viable but has clear 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?

Schema description coverage is 0%, so the description must compensate. It doesn't explain any of the 5 parameters (lat, lng, radius_m, keyword, limit). While it implies location-based searching, it provides no details on parameter meanings, units (e.g., meters for radius), default behaviors, or the 'keyword' parameter's purpose. This leaves significant gaps in understanding how to use the tool effectively.

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

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Economic units (businesses) near a point (INEGI DENUE).' It specifies the verb ('near' implying search/locate) and resource ('economic units/businesses'), and identifies the data source (INEGI DENUE). However, it doesn't differentiate from sibling tools, which include unrelated functions like air quality or crime data, so it doesn't fully distinguish itself in context.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It mentions the INEGI DENUE data source but doesn't explain if this is for specific types of businesses, geographic regions, or other contextual factors. No exclusions, prerequisites beyond the token, or comparisons to sibling tools are included.

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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