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dbeaver-mcp

dbeaver-mcp

by apsolut
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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describe_table

Retrieve column definitions for a table using a saved DBeaver connection. Specify connection, table, and optional schema to get structure details.

Instructions

Describe columns of a table on a DBeaver connection.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesConnection name or id
tableYesTable name
schemaNoSchema name (default public)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.6.1

TDQS

B3.3/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation but does not explicitly state non-destructiveness, permissions required, or what the response contains. This is a significant gap for a tool with no annotation support.

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, clear sentence with no wasted words. It is front-loaded with the core action and resource, making it easy to scan and understand.

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?

For a simple tool, the description is minimally adequate, but it lacks details about the return format (e.g., column names, types, constraints). Since there is no output schema, the agent might benefit from knowing what the result looks like. However, the operation is common enough that the description suffices for basic use.

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?

The schema provides 100% description coverage for all three parameters, so the description adds no additional meaning beyond what the schema already documents. The baseline of 3 is appropriate since the schema handles parameter semantics.

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 action ('Describe columns') and the resource ('a table on a DBeaver connection'). This is distinct from sibling tools like list_tables (which lists tables) and execute_query (which runs queries), so an agent can easily differentiate it.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives, nor any context about prerequisites or typical use cases. It simply states what it does without explaining when it is the appropriate choice.

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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