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Tlacuilo

by io.github.mastercobeos
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empresas_buscar

empresas_buscar
Read-only

Searches Mexican businesses in INEGI's DENUE directory (5M+ establishments) by name or keyword, optionally filtered by state. Returns name, legal name, activity, address, postal code, phone, email, website, size and coordinates. Price: $0.05 USDC per call (x402).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "q": "<q>"
    },
    "name": "empresas_buscar"
  }
}

Input Schema

NameRequiredDescriptionDefault
qYesBusiness name or keyword
finNoLast record, max 100 per call (default 20)
estadoNo2-digit state code, 00 for all (default 00)
inicioNoFirst record (default 1)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, so the description carries real added weight: it discloses the dataset scale (5M+ establishments), the full set of returned fields, and a per-call price of $0.05 USDC via x402, which is operationally crucial before invoking. It does not discuss result caps or error/empty-result behavior beyond the schema's own 'max 100 per call'.

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?

Two sentences with no waste: capability and data source first, then return shape and cost last where an agent needs them before committing. Every clause carries information.

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

Completeness5/5

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

With no output schema, the description's explicit return-field list is essential and present, and the cost model plus dataset scope round out what an agent needs to decide and call correctly. Remaining details such as pagination controls are fully covered by the input schema.

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

Parameters3/5

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

Schema coverage is 100%, so q, fin, estado and inicio are already documented with defaults. The description only reinforces that matching is by name or keyword and state filtering is optional, adding marginal meaning beyond the schema, so the baseline of 3 applies.

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 opens with a specific verb and resource ('Searches Mexican businesses in INEGI's DENUE directory') and states the matching mode (name or keyword) plus optional state filtering. That implicitly distinguishes it from geo-proximity sibling 'empresas_cerca', though no sibling is named explicitly, which keeps it just short of a 5.

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

Usage Guidelines4/5

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

It gives clear context for when to use it: keyword/name lookup with an optional state narrowing, over a 5M+ record source. It stops short of naming alternatives (e.g., geographic search) or stating exclusions, so there is no explicit when-not guidance.

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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