Slack MCP Server

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 specifies the return format (dictionary with message details) and parameter defaults/limits (e.g., limit range 1-1000, default 20), which adds useful context. However, it lacks details on permissions, rate limits, pagination, or error handling, leaving gaps for a tool with mutation-free but potentially sensitive data access.

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 well-structured with a purpose statement followed by Args and Returns sections. Each sentence earns its place by clarifying parameters and output without redundancy. It is front-loaded with the core function and remains appropriately sized for the tool's complexity.

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 moderate complexity (4 parameters, no annotations, but with an output schema), the description is largely complete. It covers purpose, parameters, and return format, and the output schema likely details the dictionary structure, reducing the need for return value explanation. However, it lacks behavioral details like permissions or rate limits, which could be important for safe usage in a Slack context.

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 description coverage is 0%, so the description must fully compensate. It provides clear semantics for all four parameters: channel_id (ID format example), limit (range and default), oldest (Unix timestamp, optional), and latest (Unix timestamp, optional). This adds significant value beyond the bare schema, making parameter usage understandable without relying on schema descriptions.

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 verb ('fetch') and resource ('message history from a Slack channel'), making the purpose specific and unambiguous. It distinguishes itself from sibling tools like get_dm_history (for direct messages) and get_thread_replies (for threads), establishing a clear scope for channel-based message retrieval.

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 implies usage for retrieving channel message history but does not explicitly state when to use this tool versus alternatives like get_dm_history for direct messages or search_messages for broader searches. No exclusions or prerequisites are mentioned, leaving the agent to infer context from the tool name and description alone.

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