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Get a public table's schema

get_table_schema
Read-onlyIdempotent

One public table's columns (name, type, and any published profile such as null counts, distinct counts or ranges), its immutable version_id, and its capabilities: whether anonymous sampling, keyed querying and Parquet download are enabled, and, when the publisher declared text search, capabilities.search (spec_digest and keyword_columns for query_table's search). Private workspace tables are not available here: use query_run for sealed output evidence and compare it with the approved recipe document already in the conversation. If that document is unavailable, report the limitation; do not invent a schema or publish a dataset to inspect it. Example: {"table_id": "0f2f6bfa-4a63-4f75-9a0b-1a7d9c5b2e10"}. Read this before writing a query_table call: the column names it lists are the only ones the query grammar accepts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
table_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, it discloses concrete behavioral facts: private tables are excluded, version_id is immutable, and it reports the specific capability flags (anonymous sampling, keyed querying, Parquet download). It even prescribes failure behavior ('report the limitation; do not invent a schema').

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?

Front-loads the return contents, then usage, then the query_table prerequisite. Every sentence is functional, but the passage is dense and packs return values, scope exclusions, and procedural guardrails into run-on sentences, slightly straining readability.

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?

With no output schema, the description must explain returns, and it does thoroughly: columns/profiles, version_id, capability flags, and search spec details. An agent has enough to call the tool and interpret the result.

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?

With one parameter at 0% schema description coverage, the description must carry the burden, and it does provide a concrete example object ({"table_id": "0f2f6bfa-..."}) that shows the expected UUID-style identifier. This meaningfully compensates for the undocumented parameter, though it stops short of naming constraints beyond the format.

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?

States a specific verb (get) and resource (a public table's schema) and enumerates exactly what is returned: columns with names/types/profiles, an immutable version_id, capability flags, and capabilities.search details. It explicitly scopes to public tables, distinguishing it from get_table, list_tables, and query_table.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('Read this before writing a query_table call' because its column names are the only ones the grammar accepts), an exclusion ('Private workspace tables are not available here'), and a redirect to an alternative ('use query_run for sealed output evidence'). Nothing is left to inference.

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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