Lista os SQL warehouses do workspace (id, name, state, cluster_size, warehouse_type). Use o id em databricks_run_sql (ou deixe o run_sql escolher um RUNNING automaticamente).
| Name | Required | Description | Default |
|---|---|---|---|
| account | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the safety profile is known. The description adds that it lists warehouses with specific fields, but does not elaborate on response shape, pagination, or error conditions. It doesn't contradict annotations, so no red flag, but it adds minimal behavioral detail beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences. The first states the exact output fields, the second provides actionable usage guidance. Front-loaded, no fluff, efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (list warehouses) and the description covers output fields and integration with run_sql. It doesn't mention pagination or the output structure, but given the tool's simplicity and the presence of readOnly/IDEMPOTENT hints, it's sufficient. Minor gap is the unaddressed `account` parameter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter `account` has 0% schema coverage and the description does not mention it at all. Since the description carries the burden for parameter meaning with such low coverage, this is a clear gap. The parameter is optional, but its purpose (likely selecting a Databricks account) is not explained and could cause confusion.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists SQL warehouses with specific fields (id, name, cluster_size, warehouse_type). It distinguishes this tool from siblings like databricks_get_warehouse (which fetches a single warehouse) and databricks_run_sql (which executes queries) by explicitly linking usage to run_sql.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides concrete usage guidance: use the returned `id` in databricks_run_sql, and notes that run_sql can automatically choose a RUNNING warehouse. It doesn't explicitly say when NOT to use this tool, but the context and pointer to run_sql makes the intended usage clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.