Get column names and data types for a specific table by providing data source ID and exact table name. Use when you need table structure for SQL queries.
Get detailed column information (names and types) for a specific table. Use search_schema if you don't know the exact table name.
| Name | Required | Description | Default |
|---|---|---|---|
| table_name | Yes | Exact table name | |
| data_source_id | Yes | Data source ID |
Changes observed during successful MCP inspections.
v0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already signals a safe read operation. The description adds value by disclosing the return content—column names and types—which is beyond the annotation. It does not mention auth, errors, or rate limits, but for a simple getter this is acceptable.
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?
Two sentences with no wasted words. The purpose is front-loaded and the usage guidance is appended efficiently. Every sentence earns its place.
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?
The description covers what the tool does, what it returns, and provides one key routing hint (search_schema). It does not mention how to obtain data_source_id or contrast with get_data_source_schema, but for a simple read-only tool with full schema coverage and annotations, the remaining gaps are minor.
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 100% with both parameters documented (data_source_id and table_name). The description only repeats the 'specific table' idea already in the schema, adding no meaningful parameter-level semantics, so the baseline 3 applies.
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 states a specific verb and resource: 'Get detailed column information (names and types) for a specific table.' It also distinguishes itself from the sibling search_schema by explicitly pointing to it for unknown table names, making the tool's scope clear.
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?
The description gives an explicit alternative and when-not: 'Use search_schema if you don't know the exact table name.' However, it does not explicitly state when to use this tool over get_data_source_schema or mention prerequisites like obtaining data_source_id, so the guidance is good but not exhaustive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.