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ssolis-ti

Compra Ágil MCP Server

auditar_compras_desiertas

Audit a desierta Compra Ágil procurement to uncover why no offers were received, using comparison with similar successful tenders to pinpoint problems like tight deadlines or low budget.

Instructions

Audita un proceso de Compra Ágil que haya quedado "desierto" (sin ofertas) para identificar los motivos (plazo ajustado, presupuesto bajo, requisitos restrictivos) comparándolo con procesos exitosos similares en el mercado. Admite ingresar el código de la compra o un término de búsqueda para encontrar un proceso desierto reciente.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codigo_compraNoCódigo de la Compra Ágil desierta para auditar (ej: "1057539-228-COT26"). Opcional si se especifica "q".
qNoTérmino de búsqueda de producto/servicio para encontrar y auditar un proceso desierto reciente (ej: "resmas papel"). Opcional.
limite_analisisNoCantidad de procesos históricos exitosos con los que comparar (1-8, default 5) para no agotar la cuota de la API.
Behavior3/5

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

No annotations are provided, so the description must fully convey behavioral traits. It implies a read-only analysis by mentioning comparisons and API quota limits, but it does not explicitly state that no data is modified or require authentication. The parameter 'limite_analisis' hints at API usage, but more direct transparency would improve safety.

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 concise with two sentences, but the second sentence repeats input options already in the schema. It is well-structured and front-loaded with the primary purpose.

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 explains the action and inputs but lacks details on the output format (e.g., what kind of report is generated). Without an output schema, the AI agent may not know what to expect, reducing completeness for a tool that likely returns structured analysis.

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?

Schema coverage is 100%, so the schema already documents all parameters. The description adds value by explaining that 'codigo_compra' and 'q' are alternative inputs, and that 'limite_analisis' controls API quota usage. This contextual information goes beyond the schema's basic descriptions.

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 audits a 'Compra Ágil' that ended with no offers, identifying reasons like tight deadlines, low budget, or restrictive requirements, and compares with successful similar processes. It distinguishes itself from siblings like 'buscar_compras_agiles' and 'obtener_detalle_compra' by focusing on post-hoc analysis.

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 indicates when to use the tool (for deserted processes) and how to input data (code or search term). However, it does not explicitly mention when not to use it or provide comparisons with alternative tools, but the context is clear enough for selection.

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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