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NiclasOlofsson

DBT Core MCP Server

get_resource_info

Retrieve detailed information about any dbt resource including models, sources, seeds, snapshots, and tests. Supports auto-detection or type filtering, and returns database schema and compiled SQL.

Instructions

Get detailed information about any dbt resource (model, source, seed, snapshot, test, etc.).

This unified tool works across all resource types, auto-detecting the resource or filtering by type. Designed for LLM consumption - returns complete data even when multiple matches exist.

Args: name: Resource name. For sources, use "source_name.table_name" or just "table_name" resource_type: Optional filter to narrow search: - "model": Data transformation models - "source": External data sources - "seed": CSV reference data files - "snapshot": SCD Type 2 historical tables - "test": Data quality tests - "analysis": Ad-hoc analysis queries - None: Auto-detect (searches all types) include_database_schema: If True (default), query actual database table schema for models/seeds/snapshots/sources and add as 'database_columns' field include_compiled_sql: If True (default), include compiled SQL with Jinja resolved ({{ ref() }}, {{ source() }} → actual table names). Only applicable to models. Will trigger dbt compile if not already compiled. Set to False to skip compilation. state: Shared state object injected by FastMCP

Returns: Resource information dictionary. If multiple matches found, returns: {"multiple_matches": True, "matches": [...], "message": "..."}

Raises: ValueError: If resource not found

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
resource_typeNo
include_database_schemaNo
include_compiled_sqlNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: auto-detection of resource type, return of multiple matches with a special structure, side effects of include_compiled_sql (triggers dbt compile), and error raising (ValueError). It also notes the state parameter injection.

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 well-structured with sections for main description, args, returns, and raises. It is front-loaded with the core purpose. Every sentence adds value without redundancy, making it appropriately concise for a 4-parameter tool.

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 output schema exists, the description provides sufficient context about the return value (resource info dict, multiple matches handling). All parameters are thoroughly explained. The description is complete for the tool's complexity.

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?

The input schema has 0% description coverage, so the description carries the full burden. It provides detailed explanations for each parameter: name format for sources, resource_type values with descriptions, boolean parameters with their effects (including side effects like triggering compile). This adds significant 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 purpose: 'Get detailed information about any dbt resource (model, source, seed, snapshot, test, etc.)'. It is a specific verb-resource combination and distinguishes itself from sibling tools like list_resources (listing) or get_lineage (lineage) by focusing on detailed information retrieval.

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

Usage Guidelines3/5

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

The description provides usage context (e.g., source naming convention) but does not explicitly state when to use this tool versus alternatives. It implies it's for detailed resource info, but no when-not or exclusion criteria are given relative to siblings.

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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