List Columns
list_columnsUse this when the user asks about workflow stages or you need column IDs for another call. Returns columns in board order.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
list_columnsUse this when the user asks about workflow stages or you need column IDs for another call. Returns columns in board order.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, so the safety profile is clear. The description adds that results are 'in board order' and that column IDs are provided, which is useful for downstream calls. It does not describe error cases or empty-board behavior, but for a simple read operation with no parameters, this is adequate.
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, front-loaded with the usage guidance. Every word earns its place—no filler, no redundancy. The structure is ideal: usage first, then what it returns.
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?
For a tool with no parameters and no output schema, the description covers the essential context: when to use it, what it returns, and the ordering. It could mention the exact structure of the returned columns (e.g., fields beyond IDs), but the primary use case (column IDs) is stated. Given the simplicity, it is nearly complete.
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?
There are zero parameters, so the schema provides 100% coverage by default. Per the rubric, a baseline of 4 applies when there are no params. The description does not need to explain parameters, and it doesn't; it adds value by clarifying the return type (columns) and ordering, which is not parameter-related but is helpful.
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 tool's function: it returns columns in board order, and it ties this to workflow stages and column IDs. This is specific (verb 'list' + resource 'columns') and easily distinguishes it from sibling list tools (list_tasks, list_sprints, etc.).
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?
Explicit usage triggers are given: 'Use this when the user asks about workflow stages or you need column IDs for another call.' This tells an agent exactly when to invoke this tool, and implicitly when not to. No ambiguity about the context.
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.