show_version
Read-onlyIdempotent
Show the current MCP platform and adapter versions.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Databricks
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description aligns with the annotations (readOnlyHint, idempotentHint, destructiveHint) but adds no additional behavioral context beyond stating 'Show'. It does not describe return format or any potential side effects, though annotations cover the safety profile. This is adequate but not enriching.
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 with no superfluous words. It is well-structured and front-loaded, immediately conveying the tool's function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema, and annotations covering safety), the description adequately explains the action. It could mention what the version output looks like, but the core purpose is clear and complete for a version check tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline score is 4 per the rubric. The description correctly avoids parameter details, and the schema already indicates no properties.
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 tool's purpose with a specific verb ('Show') and resource ('current MCP platform and adapter versions'), distinguishing it from sibling tools focused on authentication, SQL, or table operations.
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?
No explicit guidance is given on when to use this tool versus alternatives. The purpose implies use when version information is needed, but there is no mention of exclusions or alternative tools, so it meets only the 'implied usage' level.
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.