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create_connection

Create a new database connection for a tenant, returning a connection ID to use when creating schemas. Supports PostgreSQL and Supabase, with sensitive fields masked.

Instructions

Cria uma nova conexão de banco de dados para o tenant. O ID retornado é usado como connection_id no create_schema(db_config).

Providers suportados: postgresql, supabase. Campos sensíveis (password, keys) são mascarados no retorno.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesNome da conexão (ex: "Protheus Produção", "Supabase Dev")
providerYesTipo do banco de dados
settingsNoConfigurações adicionais (JSON livre)
is_activeNoCriar como ativa
is_primaryNoMarcar como conexão principal do tenant (remove primary das outras)
descriptionNoDescrição da conexão
connection_configYesConfiguração de conexão. Estrutura por provider: POSTGRESQL: { host: string, port?: number (default 5432), database: string, username: string, password: string, ssl?: boolean (default true para hosts não-localhost) } SUPABASE: { url: string (URL do projeto Supabase), supabase_anon_key: string }
Install Server

TDQS

A4.2/5.0
Behavior4/5

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

Annotations are all false, so the description carries most of the behavioral burden. It clearly states a create action and adds non-obvious behavior: sensitive fields such as password and keys are masked in the return value, and an ID is returned for downstream use. No contradiction with annotations.

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?

Four short, front-loaded sentences each add value: the core purpose, downstream workflow, allowed providers, and output masking. There is no filler or unnecessary repetition.

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

Completeness4/5

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

With no output schema, the description usefully explains that the call returns an ID used as connection_id and that sensitive fields are masked. Combined with a fully described input schema, an agent has enough to select and invoke the tool correctly; a fuller return-shape description would be the only meaningful gap.

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 baseline is 3 and the schema already documents every parameter, including provider-specific connection_config structures. The description does not add parameter-level semantics beyond restating supported providers and the masking behavior, so no upward adjustment is warranted.

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 opens with a specific action and resource ('Cria uma nova conexão de banco de dados para o tenant'), which clearly distinguishes this create operation from sibling list/get/update/delete/test_connection tools. It further contextualizes the object by noting the returned ID feeds create_schema.

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 gives clear workflow context (the returned ID is used as connection_id in create_schema) and constrains use to supported providers (postgresql, supabase). It does not explicitly name alternatives or exclusion conditions, but these signals are sufficient for selecting the tool in most 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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