get_input_schema
Get neutral column roles and validation defaults for a vertical.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| vertical | Yes |
Get neutral column roles and validation defaults for a vertical.
| Name | Required | Description | Default |
|---|---|---|---|
| vertical | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds that the payload is column roles and validation defaults, but says nothing about the shape, size, or whether results are stable across versions.
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 sentence with no filler. It is efficient, though its brevity borders on under-specification rather than well-engineered concision.
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?
There is no output schema, so the description is the only place the return value could be characterized, and 'neutral column roles and validation defaults' is too thin for a schema-introspection tool in an audit pipeline. Nothing links it to the audit workflow or explains what an agent should do with the result.
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%, so the description must carry the meaning of 'vertical', and it does not — the reader must infer it from the regex pattern listing general_ml, demand_forecasting and bioinformatics_research. The description adds no semantics beyond the schema's own pattern constraint.
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 verb 'Get' and the resource 'neutral column roles and validation defaults' are stated, and 'for a vertical' scopes it. But 'neutral column roles' is internal jargon that an agent may not decode, and there is no differentiation from the similarly named sibling get_repair_schema or from list_verticals.
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 never says when to call this tool, what prerequisite state it assumes, or which sibling to use instead. With get_repair_schema and list_verticals in the same family, the agent is left to guess which schema-retrieval tool applies.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.