databricks_get_table
Detalha uma ou mais tabelas (colunas, tipos) por nome completo catalog.schema.table. Aceita lista (full_names).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| full_names | Yes |
Detalha uma ou mais tabelas (colunas, tipos) por nome completo catalog.schema.table. Aceita lista (full_names).
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| full_names | Yes |
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 agent knows this is a safe read. The description adds that it provides columns and types)Skip"; it does not contradict annotations. It lacks extra behavioral detail like pagination or error cases, but the annotations cover the safety profile, so 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose, and no filler. Every word contributes to understanding what the tool does and its main parameter. Highly concise.
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 a simple read operation with low complexity. The description covers the operation, the key parameter format, and what it returns (columns/types). No output schema exists, but the description sufficiently implies the return content. It's complete enough for an agent to select and use correctly.
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?
Schema coverage is 0%, so the description must explain parameters. It explains that full_names is a list (array) and that the entries must be full names in `catalog.schema.table` format, which is essential. The optional 'account' parameter is not mentioned, but it's likely supplementary; overall, the description adds meaningful semantics beyond the raw schema.
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 the tool details tables (columns, types) by full name, which is a specific verb+resource. It differentiates from sibling databricks_list_tables by specifying 'full name' and the detail level (columns, types), though it doesn't explicitly name the alternative.
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?
The description implies usage: use this when you need column and type details for specific tables by full name. It does not explicitly state when not to use it or mention alternatives like databricks_list_tables, but the context from siblings and the 'full name' requirement provide implicit guidance.
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.