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databricks_cancel_statement

Cancela um ou mais statements em execução por id. Aceita lista (statement_ids).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accountNo
statement_idsYes

Schema Changelog

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

  1. First observed

TDQS

B3.1/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false, destructiveHint=false, and idempotentHint=false, which suggest this is a mutating operation but not destructive. The description adds that it cancels statements, which implies a state change, but it does not disclose details like whether cancellation is irreversible, what happens to the statement's results, or any side effects. With annotations present, the description adds minimal extra context beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, concise and front-loaded with the action. It includes the key parameter and its type. However, it could be slightly more structured by separating the parameter explanation, but it is efficient and free of fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 2 parameters, no output schema, and minimal annotations. The description is too brief to be complete: it doesn't explain the 'account' parameter, doesn't mention any prerequisites (like authentication), doesn't describe the expected behavior when statements are not found or already completed, and doesn't indicate any error conditions. For a mutation tool with no output schema, this is insufficient.

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?

Schema description coverage is 0%, so the description must compensate for parameter meaning. The description mentions 'statement_ids' but does not explain the 'account' parameter at all. It also doesn't clarify the format of statement IDs or whether the list can be empty. The description adds some value for statement_ids but leaves 'account' undocumented, which is a significant gap given the low schema coverage.

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 cancels one or more running statements by ID, using the verb 'Cancela' (cancels) and specifying the resource (statements) and the key parameter (statement_ids). It distinguishes from siblings like databricks_get_statement and databricks_run_sql, though it doesn't explicitly name them.

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: cancel running statements by providing their IDs. It does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites (e.g., needing an active connection or authentication). The context is clear but lacks explicit guidance on when not to use it.

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.