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Changuito — AI eCommerce Store Builder

Cargar productos

add_products

Carga hasta 25 productos por llamada, con nombre, precio, descripción, categoría y hasta 2 fotos por producto (image_urls: direcciones https públicas). Un producto sin precio mayor a 0 no se carga.

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": {
      "store_id": "<store_id>",
      "products": "<products>"
    },
    "name": "add_products"
  }
}

Input Schema

NameRequiredDescriptionDefault
productsYes
store_idYesId de la tienda.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnly=false, destructive=false, idempotent=false, openWorld=true, so safety is largely covered. The description adds genuinely useful behavior beyond them: the 25-item cap and the silent-skip rule for products without price > 0. However, it does not say whether the write is atomic or per-item (does one invalid product block the rest?), nor whether re-running duplicates products — a material gap for a non-idempotent bulk mutation.

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 dense sentences with no filler; the capacity limit and the field list come first, followed by the validation rule. Every clause carries information an agent needs.

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?

There is no output schema, and the description says nothing about what is returned (which products succeeded, error format, resulting IDs), which matters for a bulk operation with skip-on-invalid semantics. The mutation-safety profile is covered by annotations, but return behavior and partial-failure handling are left undocumented.

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 only 50% (only store_id is described), so the description has to compensate, and it does: it names the per-product fields, clarifies that image_urls must be public https addresses, and explains the price > 0 validation consequence. Some of it restates schema constraints (maxItems 2, maxItems 25, exclusiveMinimum 0), which limits it to a 4.

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 ("Carga ... productos") and adds scope (up to 25 per call, the accepted fields). It is unmistakably a bulk product-insert tool, clearly distinct from the store-level siblings (create_store, set_branding, preview_store), though it never names an alternative explicitly.

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 batching limit ("hasta 25 productos por llamada") implies this is the bulk path, but there is no explicit when-to-use vs. when-not, no mention that the store must already exist, and no guidance on what to do for more than 25 products (multiple calls?). Usage is inferable rather than stated.

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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