cemis-directorio

Oportunidades por sector

oportunidades_por_sector

Read-onlyIdempotent

Dado un sector (ver listar_sectores(), o un sector de la taxonomía de matching de reformas que todavía no tiene empresas asignadas — ver impacto_de_reforma()), devuelve las oportunidades de negocio relevantes: primero las curadas a mano (vinculadas directamente a alguna empresa de ese sector), luego las encontradas automáticamente por coincidencia de palabras clave de sector (mismo criterio que usa cemis.io). Si el sector no tiene empresas asignadas hoy, 'oportunidades_curadas' sale vacía (no puede haber curadas sin empresas) pero las automáticas por keyword sí pueden aparecer, porque esas se buscan directamente en el texto de las oportunidades/transformaciones, no a través de las empresas.

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": {
      "sector": "<sector>"
    },
    "name": "oportunidades_por_sector"
  }
}

Input Schema

Name	Required	Description	Default
`sector`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 1, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Beyond the annotations (read-only, idempotent, non-destructive), the description discloses ordering behavior and the important edge case where sectors without assigned companies yield an empty 'oportunidades_curadas' while automatic matches can still appear. This adds meaningful behavioral context.

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

Conciseness3/5

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

The description is information-dense but structured as one long run-on sentence with nested parentheticals. All content is relevant, but it would be easier to parse if split into clearer sentences or bullets.

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

Completeness4/5

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

Given a single parameter and no output schema, the description covers the input source, result ordering, and the empty-case behavior. It is sufficiently complete for an agent to invoke the tool correctly and interpret the response shape.

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

Parameters4/5

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

The schema only defines 'sector' as a string with 0% description coverage. The tool description compensates by explaining that the sector should come from listar_sectores() or from the reform-matching taxonomy, giving the agent concrete guidance on valid values.

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's purpose: given a sector, it returns relevant business opportunities. It also specifies the result structure—curated opportunities first, then automatic keyword matches—which distinguishes it from sibling tools like empresas_por_oportunidad and buscar_empresas.

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?

The description gives concrete guidance on valid inputs: use listar_sectores() or a sector from the reform-matching taxonomy via impacto_de_reforma(). It implies when to use the tool, though it does not explicitly say when not to use it or name alternatives.

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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