slack_get_reactions

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does explain the effect of the 'full' parameter (return complete reaction list vs. presumably aggregated counts) and states the return type ('Message with its reactions'). However, it omits details such as error handling, rate limits, or what happens when the message has no reactions. The description adds some behavioral context but lacks completeness.

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 concise and well-structured. The one-line purpose is front-loaded, followed by a clear parameter list and a returns line. There is no redundant or verbose content. The structure makes it easy to scan, and every sentence serves a purpose. It is appropriately sized for a simple tool.

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 the tool's simplicity and the presence of an output schema (though not shown), the description covers the main aspects: what it does, its parameters, and the return type. However, it lacks contextual differentiation from the sibling tool slack_list_reactions, which could lead to incorrect tool selection. It also does not mention any edge cases or preconditions. For a straightforward read operation, this is adequate but not fully 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?

Schema description coverage is 0%, so the description must compensate. It does explain all three parameters: channel_id (with valid prefixes), timestamp (with an example), and full (with its effect). This adds meaningful meaning beyond the bare schema, which only lists types and defaults. The explanation of 'full' is particularly useful. The parameter descriptions are concise and helpful, though not exhaustive (e.g., no mention of validation rules or default behavior when full is false).

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 action and resource: 'Get reactions for a specific Slack message.' It specifies the verb 'Get' and the resource (reactions for a message), which is distinct enough. However, it does not explicitly differentiate from the sibling tool slack_list_reactions, which could cause confusion. The purpose is clear but lacks explicit sibling differentiation.

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 provides no guidance on when to use this tool versus alternatives. It does not mention that it is for a specific message as opposed to slack_list_reactions (which might be for a user's reactions). There is no mention of prerequisites, exclusions, or conditions for choosing this tool. This leaves the agent to infer usage context, which is a significant gap.

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