Reads nested schema, size, row count, partitioning, and clustering of a BigQuery table before writing SQL to avoid errors and optimize queries.
Read nested schema, size, row count, partitioning and clustering before writing SQL.
| Name | Required | Description | Default |
|---|---|---|---|
| account | No | ||
| table_id | Yes | ||
| dataset_id | Yes | ||
| project_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered. The description adds that multiple metadata facets are returned in one read, but since an output schema exists, that is largely duplicative; nothing on permissions or required access is disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded clause with no filler; the key information (what is read) comes first and the usage trigger last. It is terse to the point of being a fragment, but nothing is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With rich annotations and a declared output schema, the description covers the what and roughly the when. The gap is scope resolution (which project/account a dataset_id is resolved against) and sibling differentiation, both left to inference for a tool with 4 unresolved parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% across 4 parameters (dataset_id, table_id, account, project_id), so the description must compensate and does not — it mentions no parameter at all. Parameter names are mostly self-explanatory, which keeps this above a 1, but the null-defaulted account/project_id scoping is left entirely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource and the exact metadata it returns (nested schema, size, row count, partitioning, clustering), which is far more informative than the bare name get_table. It does not, however, explicitly distinguish itself from siblings like preview_table or get_dataset, so an agent must infer the boundary.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'...before writing SQL' gives a clear implied moment to use the tool. But no alternative is named and no exclusion is stated (e.g., when to use preview_table for sample rows instead), so the routing guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.