column_value_format

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

No annotations are provided, so the description carries the burden. It discloses useful behavioral facts: it returns exact JSON formats and requires no API key. It does not describe behavior for invalid column types, but for a simple lookup this is a minor gap.

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 three short sentences with no fluff. It front-loads the core behavior, then the auth note, then the clear usage condition. 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?

For a one-parameter reference/lookup tool, the description is nearly complete: it defines the input scope, the kind of output, and the intended use case. It lacks an explicit response structure or error behavior, but no output schema exists and the general output type is clear.

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?

The schema already documents the single parameter with examples and 100% coverage. The description adds 'any monday.com column type' but does not meaningfully add parameter semantics beyond what the input schema already provides.

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 uses a specific verb ('Returns') and names the exact resource ('read and write JSON format for any monday.com column type'). This clearly distinguishes the tool from siblings like run_query or get_schema.

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 'Use this whenever you need to know how to read or write a column value,' which gives a clear condition for choosing the tool. It does not mention when-not-to-use it or name alternatives, so it stops short of full alternative routing.

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