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build_connect_data_source

Create a data connector to an EXTERNAL source the app owner controls (their own database, a REST API, or a warehouse). Returns a connector_id. Does NOT bind schemas yet — run build_discover_source then build_bind_data_source. Credentials are sent to the platform and NEVER echoed back; prefer read-only DB creds / the Key Store for production. Requires a domain- or project-admin API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
authNoapi kind: {type: none|api_key|bearer|basic|oauth2, ...}.
hostNo
kindYesSource kind.
nameYesConnector name (unique within the project).
portNo
configNoExtra source config (e.g. warehouse account).
db_typeNodatabase/warehouse: postgresql|mysql|mssql|oracle|mongodb|snowflake|bigquery|redshift|databricks|clickhouse|fabric.
base_urlNoapi kind: API base URL.
databaseNo
endpointNoapi kind: endpoint path.
passwordNoDB password — sent to the platform, never echoed.
ssl_modeNo
usernameNo
test_firstNoProbe connectivity before creating (default true).
schema_namespaceNoNamespace for schemas from this source.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations are sparse (readOnly=false, etc.), so the description carries the burden. It discloses that the tool returns a connector_id, does not bind schemas, sends credentials to the platform (never echoed), and requires a domain- or project-admin API key. These are behavioral traits beyond the annotations, with no contradiction.

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?

Five sentences, each with a distinct purpose: purpose, return value, workflow, security, auth. Information is front-loaded and there is no filler. Every sentence earns its place.

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?

For a 15-parameter tool with no output schema, the description covers the return value, workflow, security, and authentication requirements. It does not enumerate parameters (the schema handles that), but it gives an agent enough context to call it correctly. Missing error handling is acceptable given the annotations and schema coverage.

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 67%, so the schema documents many parameters (kind, name, db_type, base_url, etc.). The description adds no parameter-specific meaning beyond a generic reference to credentials, which overlaps with the schema's password description. With moderate coverage, the description does not compensate further, so a baseline 3 is appropriate.

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?

States a specific verb and resource, enumerates source kinds (database, REST API, warehouse), and explicitly clarifies it does not bind schemas, naming the exact workflow siblings (build_discover_source, build_bind_data_source). This distinguishes it clearly from related tools without ambiguity.

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?

Explicitly directs the agent to run build_discover_source then build_bind_data_source, giving a clear sequential workflow and alternatives. Also notes the API key requirement and production security preferences, which are practical usage conditions.

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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