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Get Chat Messages

discourse_get_chat_messages

Retrieve chat channel messages with flexible pagination, date filtering, or from last-read positions to access conversation history efficiently.

Instructions

Get messages from a chat channel with flexible pagination and date-based filtering. Supports: (1) paginating with direction='past'/'future' from a target_message_id, (2) querying messages around a specific target_date, (3) getting messages around a target_message_id, or (4) fetching from last read position.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_idYesThe chat channel ID
page_sizeNoNumber of messages to return (default: 50, max: 500)
target_message_idNoMessage ID to query around or paginate from
directionNoPagination direction: 'past' for older messages (DESC), 'future' for newer messages (ASC)
target_dateNoISO 8601 date string (e.g., '2024-01-15' or '2024-01-15T10:30:00Z') to query messages around that date
fetch_from_last_readNoIf true, start from the user's last read message
include_target_message_idNoWhether to include the target message in results (default: true)
Behavior3/5

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 describes functional capabilities (pagination, filtering) but lacks details on permissions, rate limits, error handling, or response format. It does not contradict annotations, but for a read operation with 7 parameters, more behavioral context would be helpful.

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 front-loaded with the core purpose and efficiently lists four usage scenarios in a single sentence, with no redundant or wasted words. It is appropriately sized for a tool with multiple parameters and complex functionality.

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

Completeness3/5

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

Given the complexity (7 parameters, no annotations, no output schema), the description covers the 'what' and 'when' well but lacks details on behavioral aspects like permissions, rate limits, or return format. It is adequate for basic use but incomplete for full agent understanding without additional context.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds minimal value by hinting at parameter interactions (e.g., direction with target_message_id) but does not provide syntax or format details beyond what the schema already specifies. Baseline 3 is appropriate given high schema coverage.

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 clearly states the verb ('Get') and resource ('messages from a chat channel'), distinguishing it from siblings like discourse_list_chat_channels (which lists channels) or discourse_search (which searches content). It specifies the domain (chat messages) and key capabilities (pagination, date filtering).

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

Usage Guidelines4/5

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

The description explicitly lists four usage scenarios (pagination with direction, date-based querying, message-ID-based querying, and fetching from last read), providing clear context for when to use this tool. However, it does not mention when NOT to use it or name specific alternatives among siblings, such as discourse_search for broader content searches.

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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