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slack_channel_history

Fetch Slack messages from a chosen channel over a specified time window, using channel names or IDs and flexible date bounds. Returns top-level messages with optional user-name resolution.

Instructions

Read messages from one channel over a time window.

Accepts a channel name ('project-x' or '#project-x') or a channel ID. Name lookups are cached. Returns top-level messages only — use slack_thread to expand any message whose reply_count is non-zero.

Args: channel: Channel name or ID. oldest: Start bound as 'YYYY-MM-DD', 'YYYY-MM-DD HH:MM:SS', an epoch, or a relative offset like '-7d'. latest: End bound, same formats. A bare date means end-of-day. limit: Maximum messages (default 100). resolve_names: Resolve user IDs to display names (costs one call per user). include_broadcasts: When False, drop @here/@channel/@everyone messages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
latestNo
oldestNo
channelYes
resolve_namesNo
include_broadcastsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full burden and delivers: it says the operation is a read, accepts names or IDs, mentions name lookup caching, notes that only top-level messages are returned, and discloses cost implications (resolve_names costs one call per user). This is rich behavioral transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a one-line purpose, then a short behavior note, then a compact Args list. Every sentence adds value, with no fluff, making it easy to scan and parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 6 parameters and no annotations, the description covers all invocation concerns: params, defaults, behavior, limitations, and an alternative tool. Since an output schema exists, not describing the return format is acceptable. The description is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the Args section fully explains every parameter: channel accepts name or ID, oldest/latest accept multiple date formats and semantics, limit has a default, resolve_names has a cost note, and include_broadcasts filters broadcast messages. This fully compensates for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Read messages from one channel over a time window,' giving a specific verb, resource, and scope. It also distinguishes itself from sibling tools by explicitly directing users to slack_thread for expanding replies, making its purpose clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly states when to use this tool ('Read messages from one channel over a time window') and provides an explicit alternative: 'use slack_thread to expand any message whose reply_count is non-zero.' This helps the agent choose between tools.

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

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