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openmetadata-mcp-server

by us-all

get-table-summary

Retrieve a complete table summary including entity details, lineage, sample data, and data quality test cases in a single API call, reducing multiple round-trips.

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.
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses inclusion of lineage at depth 2, that sample data can be large (default false), and that extractFields reduces tokens. Does not mention read-only nature, authentication needs, or rate limits.

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-sentence description is concise and front-loaded with main purpose. Parameter descriptions are detailed but not verbose. Every sentence adds value.

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 no output schema, description explains aggregated components (entity, lineage, sample data, test cases). Could mention response structure or limitations, but sufficiently complete for agent to infer behavior.

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 description coverage is 100%. Description adds value beyond schema: includeLineage specifies 'depth 2', includeSample notes 'sample data can be large', extractFields explains wildcard and token reduction. Baseline 3 with addition.

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?

Description clearly states aggregated table view combining entity, lineage, sample data, and DQ test cases in a single call. Explicitly distinguishes from individual sibling tools like get-table-by-name, get-lineage, get-table-sample-data, and list-test-cases.

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 states it replaces 3-4 round-trips of get-table-by-name + get-lineage + get-sample-data + list-test-cases, guiding when to use this aggregated tool. Does not explicitly state when not to use, but optional parameters provide flexibility.

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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