workbench-mcp

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

No annotations are provided, so the description carries full burden. It discloses that this is an insert operation (implying mutation/write) and mentions the RETURNING clause behavior. However, it doesn't cover important behavioral aspects like transaction handling, error conditions, permission requirements, or whether it's idempotent. For a database mutation tool with zero annotation coverage, this leaves significant gaps.

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?

The description is efficiently structured with a clear purpose statement, usage guideline, parameter explanations, and a concrete example. Every sentence serves a distinct purpose with zero redundancy. The information is front-loaded with the most important details first, making it easy to parse.

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?

Given this is a database mutation tool with no annotations but with output schema present, the description does well on purpose, parameters, and usage. However, it lacks behavioral context about transactions, errors, and permissions that would be important for safe operation. The output schema existence reduces the need to describe return values, but more behavioral disclosure would improve completeness.

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?

With 0% schema description coverage, the description fully compensates by explaining all three parameters clearly. It defines 'table_name' as 'table target, optionally schema-qualified', 'row' as 'object mapping column names to values', and 'returning_columns' as 'optional list of columns to return via RETURNING'. The example further clarifies usage. This adds substantial value beyond the bare schema.

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 clearly states the specific action ('Insert a single row'), target resource ('PostgreSQL table'), and scope ('one explicit insert with structured values'). It distinguishes itself from sibling tools like 'insert_rows' by specifying single-row insertion. The verb+resource combination is precise and unambiguous.

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 explicitly states when to use this tool: 'when you need one explicit insert with structured values.' It distinguishes from alternatives by specifying single-row insertion (vs. 'insert_rows' for multiple rows). The guidance is clear and includes context about the type of operation suitable for this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.