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by nic01asFr

filter_sql_query

Filter records in a Grist table using conditions, sorting, and pagination without writing SQL. Returns filtered records and query metadata.

Instructions

Exécute une requête SQL de filtrage sur une table Grist.

Version simplifiée pour requêtes SQL courantes sans écrire de SQL. Pour requêtes complexes, utiliser execute_sql_query.

Prérequis recommandés: - list_tables(doc_id) : Vérifier l'existence de la table - list_columns(doc_id, table_id) : Connaître les colonnes disponibles

Alternative à: - list_records : Quand vous avez besoin de filtrer/trier - execute_sql_query : Version simplifiée pour cas courants

Flux de travail typique: 1. list_columns(doc_id, table_id) → identifier les colonnes 2. filter_sql_query(doc_id, table_id, where_conditions={"status": "actif"}, order_by="date_creation DESC", limit=10) 3. Traiter les enregistrements retournés

Cas d'usage: - Filtrage simple: where_conditions={"status": "actif"} - Filtrage multiple: where_conditions={"status": "actif", "type": "A"} - Tri: order_by="nom" ou order_by="valeur DESC" - Pagination: limit=20 - Colonnes spécifiques: columns=["nom", "valeur", "date"]

Args: doc_id: ID du document table_id: ID de la table à requêter columns: Liste des colonnes à retourner (None = toutes) where_conditions: Dict de conditions (AND implicite entre conditions) order_by: Colonne de tri avec direction optionnelle (ex: "nom DESC") limit: Nombre max de résultats

Returns: Dict avec les enregistrements filtrés et métadonnées de requête

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
doc_idYes
columnsNo
order_byNo
table_idYes
where_conditionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses the simplified interface, return type (dict with records and metadata), and usage patterns. However, it does not explicitly state if the operation is read-only, but the context implies it is non-destructive.

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 well-structured with sections (prerequisites, alternatives, workflow, use cases, args) and front-loaded with the main purpose. However, it is somewhat lengthy; a few sentences could be trimmed without losing value.

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

Completeness5/5

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

Given the presence of an output schema (as indicated by context signals), the description adequately covers return information. It provides comprehensive context: prerequisites, workflow, use cases, and detailed parameter explanations, making it complete for a filtering query tool.

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

Parameters5/5

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

Schema coverage is 0%, so description fully compensates by explaining each parameter in detail with concrete examples (e.g., where_conditions dict, order_by syntax, limit for pagination). This adds significant meaning beyond the input 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 executes a SQL filter query on a Grist table, emphasizes it's a simplified version for common queries without writing SQL, and distinguishes from siblings like list_records and execute_sql_query.

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

Usage Guidelines5/5

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

Provides explicit when-to-use guidance by contrasting with list_records and execute_sql_query, recommends prerequisites (list_tables, list_columns), and includes a typical workflow and specific use cases.

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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