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openmetadata-mcp-server

get-table-summary

Aggregate table details, lineage, sample data, and data quality test cases in a single API call to reduce round-trips and simplify metadata retrieval.

Instructions

Aggregated table view: entity + lineage + (optional) sample data + (optional) DQ test cases in a single call. Replaces 3-4 round-trips of get-table-by-name + get-lineage + get-sample-data + list-test-cases.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fqnYesFully qualified table name (e.g. 'service.database.schema.tableName')
includeLineageNoInclude upstream/downstream lineage (depth 2). Default true.
includeSampleNoInclude sample data rows. Default false (sample data can be large).
includeTestCasesNoInclude data-quality test cases for the table. Default false.
extractFieldsNoComma-separated dotted paths to project from response (e.g. 'id,name,owner.name,columns.*.name'). Use `*` as wildcard for arrays/objects. Wrap field names with dots in backticks. Reduces response tokens dramatically on large entities.
Behavior4/5

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

No annotations provided, so description carries full burden. Discloses optional parameters, defaults, and rationale (e.g., sample data can be large). Also mentions extractFields to reduce response size. No contradictions.

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?

Two efficient sentences: first defines what it does, second explains benefit. No wasted words, front-loaded.

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?

Covers main purpose, optional behaviors, and performance considerations. No output schema, but description doesn't need to explain return values per rules. Appropriate for the complexity.

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%, baseline 3. Description adds value by explaining defaults, rationale for defaults, and detailed usage of extractFields with examples.

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?

Clearly states it provides an aggregated view combining entity, lineage, sample data, and DQ test cases in one call. Distinguishes from sibling tools like get-table-by-name, get-lineage, etc.

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?

Explicitly says it replaces multiple round-trips, implying when to use. Could be more explicit about when not to use, but context is clear.

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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