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Mizar-Contasti

Treeflow MCP Server

treeflow_create_intent

Create a new NLU intent by providing training patterns and defining required parameter slots. This enables the bot to recognize user phrases and extract key data such as dates, quantities, or room types.

Instructions

Crea una nueva intención NLU con frases de entrenamiento (patterns) y slots de parámetros requeridos (ej. fecha, cantidad, tipo_habitacion).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesNombre único de la intención (ej. saludo, reservar_mesa, consultar_precio)
typeNoTipo: conversational o contextual. Por defecto: conversational
tree_idYesID del bot/árbol
entitiesNoParámetros o slots a extraer en esta intención
patternsYesLista de frases de entrenamiento que activarán esta intención
Install Server

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description bears the full burden. It states it creates an intent with patterns and slots, but does not disclose important behavior such as whether the intent is immediately active, whether unique names are enforced, whether referenced entities must already exist, or whether training must be triggered separately. For a write operation, this is a notable gap.

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?

The description is a single, focused sentence that front-loads the core action ('Crea una nueva intención NLU') and then adds the key components (patterns and slots). There is no fluff or wasted wording.

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 covers the basic purpose and key concepts, and the schema covers all parameters. However, because there is no output schema and no annotations, important contextual details are missing: what happens after creation, whether training is needed to activate the intent, and how uniqueness or validation is handled. The sibling tools for training suggest this context is relevant.

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 description coverage is 100%, so the parameter descriptions already document tree_id, name, type, patterns, and entities. The tool description adds illustrative examples of slot values (fecha, cantidad, tipo_habitacion) and clarifies that patterns means training phrases, but it does not add substantially more meaning beyond the schema.

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 action: creating a new NLU intent with training phrases (patterns) and required parameter slots. It distinguishes the intent-creation tool from sibling tools like treeflow_create_batch or treeflow_create_leaf by specifying the exact resource type.

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 makes the tool's context obvious: it is for creating an intent, as opposed to listing, updating, or deleting intents. While it does not explicitly mention alternatives, the clear creation scope and resource-specific language provide enough contextual 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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