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openmetadata-mcp-server

quality-rollup

Aggregate data quality status across a table, test suite, or entire organization. Returns pass rate percentage, top failing test cases with details, and most recent run timestamp.

Instructions

Aggregated DQ status across a scope (table / test suite / org-wide). Lists test cases under the scope, buckets them by current status (Success/Failed/Aborted/Queued), surfaces the top failing cases (with fqn/result/timestamp), reports passRatePct + most-recent-run timestamp. Replaces the recursive list-test-cases + list-test-case-results walk LLMs do for 'what's broken in ?'. Provide ONE of entityLink / tableFqn / testSuiteId / testSuiteFqn (or none for org-wide).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityLinkNoOM entityLink, e.g. '<#E::table::svc.db.schema.orders>'
tableFqnNoConvenience: build entityLink from a table fully-qualified name
testSuiteIdNoTest Suite UUID — restricts to cases belonging to this suite
testSuiteFqnNoTest Suite FQN — resolved to UUID before listing
limitNoMaximum test cases to inspect (default 100, max 500)
topFailingLimitNoNumber of top failing cases to surface in the response (default 5)
Behavior3/5

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

No annotations provided, so description bears full burden. It discloses the outputs (buckets, top failing cases, passRatePct, timestamp) but does not mention details like pagination, error behavior, or performance implications. It is adequate but not fully transparent.

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 efficient, using 3-4 sentences to cover purpose, usage, and output. It is front-loaded with the core purpose and well-organized. No wasted words.

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

Completeness5/5

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

Given no output schema, the description thoroughly explains what the tool returns: list of test cases, status buckets, top failing cases with details, pass rate, and timestamp. It also covers scoping options. For a tool with 6 optional parameters and no nested objects, this is comprehensive.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by providing guidance to provide exactly one of the scope parameters or none for org-wide, which is not evident from the schema alone. This extra meaning justifies a 4.

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 explicitly states it provides aggregated DQ status across a scope, lists test cases bucketed by status, surfaces top failing cases, and reports pass rate and timestamp. It clearly distinguishes from sibling tools by stating it replaces the recursive walk of list-test-cases and list-test-case-results.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description tells when to use this tool (for aggregated DQ status, as a replacement for recursive walks) and how to scope by providing one of entityLink/tableFqn/testSuiteId/testSuiteFqn or none for org-wide. It names sibling alternatives but lacks explicit when-not-to-use instructions.

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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