graphify_consulta_subgrafo

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

With no annotations, the description carries the full burden. It does convey that this is a consult-style operation producing synthetic hyper-dense subgraphs and quantifies token efficiency, which adds behavioral context. However, it does not disclose the output shape, size limits, or any side-effect/reliability considerations beyond the extraction claim.

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

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

The description is a single dense sentence and remains readable, but it mixes the core function with a specific token-savings statistic and a reproducibility pointer. That metric is not needed for tool selection or invocation and distracts from more actionable routing information.

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

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

For a graph query tool with no output schema and no annotations, the description is incomplete. It does not explain what a returned subgraph looks like, how max_hops affects results, or when to prefer this over closely related graphify_* siblings, leaving the agent without enough operational context to call it confidently.

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

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

Schema description coverage is 100%, and query, max_hops, and incluir_mermaid already have clear descriptions in the schema. The description adds no parameter-level detail beyond confirming the normative/doctrinal sources, so the baseline 3 is appropriate.

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

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

The description identifies a specific action ('Consulta' / extracting subgraphs) and a specific resource ('Knowledge Graph Jurídico de Doctrina Chilena'), going beyond the tool name alone. It does not explicitly contrast with sibling graphify_* tools, but the subgraph-extraction focus provides enough differentiation for basic selection.

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

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

No when-to-use or when-not-to-use guidance is provided. It does not name alternatives like graphify_explicar_institucion, graphify_analizar_impacto, or doctrina_search, nor explain what kind of question should route to this tool rather than those siblings. Usage context is only implied by the term 'knowledge graph' and the schema examples.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.