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by io.github.Ranork
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get_database_schema

Read-only

Get the column schema and saved views of a database, without fetching any rows. Use this to inspect column names/types/options before querying, or view ids/configs before calling create_database_view / update_database_view / delete_database_view.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
databaseIdYesDatabase ID (from list_workspace or search)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
viewsYesSaved views — always at least one
schemaYesColumn definitions (id, name, type, options)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedOutput schema / properties / name / description
      Removed value: -"Database name"
    • changedOutput schema / properties / views / description
      Previous value: -"Saved views (id, name, config — table/kanban/calendar), always at least one"New value: +"Saved views — always at least one"
  2. Changed2 schema fields changed
    • addedOutput schema / properties / views
      Added value: +{
+  "description": "Saved views (id, name, config — table/kanban/calendar), always at least one",
+  "items": {},
+  "type": "array"
+}
    • changedOutput schema / required
      Previous value: -[
-  "name",
-  "schema"
-]New value: +[
+  "name",
+  "schema",
+  "views"
+]
  3. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Description adds specific behavioral context beyond annotations (readOnlyHint=true): 'without fetching any rows' and details on what is inspected. No contradictions. Minor room for further details on constraints.

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?

Two efficient sentences: first states function, second gives use cases. Front-loaded, no redundancy.

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 low complexity, single parameter, and output schema exists, the description fully covers purpose, usage, and behavioral aspects.

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?

Single parameter 'databaseId' is fully described in schema (100% coverage). Description does not add further semantics beyond the tool's purpose, which is acceptable.

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?

Clearly states it retrieves column schema and saved views, explicitly noting it does not fetch rows. Distinguishes from querying databases and from view manipulation tools.

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 states when to use: before querying to inspect columns, and before calling view-related tools to get ids/configs. Mentions sibling tools contextually.

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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