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databricks_list_accounts

Read-onlyIdempotent

Lista os workspaces Databricks conectados a este install — host, label.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds that it returns host and label, which is useful but minimal. It does not disclose any additional behavioral traits like pagination or filtering, but given the annotations cover the main safety aspects, a 3 is appropriate.

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, concise sentence that front-loads the purpose and includes the key output fields. There is no wasted text, and it is appropriately sized for a simple list operation.

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?

Given the tool's simplicity (one optional param, no output schema, read-only), the description is mostly adequate. However, the lack of parameter explanation and any mention of return format or filtering leaves some gaps, but it is not severely incomplete for a basic list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has one optional parameter 'account' with no description, and schema description coverage is 0%. The description does not explain what 'account' means or how it affects the results. Since the description must compensate for the lack of schema documentation but fails to do so, this scores low.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists Databricks workspaces connected to this install, including host and label. It uses a specific verb ('Lista') and resource ('workspaces Databricks'), which distinguishes it from sibling tools like databricks_list_catalogs or databricks_list_warehouses.

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 implies usage for viewing connected workspaces but does not explicitly state when to use this tool versus alternatives. It lacks guidance on when not to use it or mention of any prerequisites, though the context of listing workspaces is clear enough for basic selection.

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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TDQS

B3.4/5.0
Disambiguation4/5

The Databricks-specific tools (e.g., databricks_run_sql, databricks_list_catalogs) are clearly distinct, each targeting a unique resource or action. However, the set also includes several platform-level tools (marketplace, authenticate, connect) that serve a different purpose, creating a mild mix of domains but without direct overlap or ambiguity.

Naming Consistency2/5

The Databricks tools follow a consistent `databricks_verb_noun` pattern, but the platform tools (authenticate, connect, marketplace, report_bug, show_version, toolkit_info) do not follow this pattern and use inconsistent naming conventions (some are single verbs, some are noun phrases, some are verb_noun). This mixing of styles makes the set feel patchwork rather than unified.

Tool Count3/5

With 17 tools, the count is slightly above the typical well-scoped range of 3-15 and leans toward being heavy, especially considering that 6 of them are not Databricks-specific but general MCP platform utilities. The number is not extreme, but it feels a bit bloated for a server focused on Databricks.

Completeness4/5

The Databricks surface covers the core operations: listing catalogs/schemas/tables/warehouses, running SQL, polling and canceling statements, and user/account info. Minor gaps exist (e.g., no table or warehouse creation/deletion), but these are not obvious dead ends for the likely use cases. The platform tools (marketplace, connect, etc.) also provide comprehensive coverage of the MCP platform lifecycle.