Skip to main content
Glama

chat_list_channel_messages

Read-onlyIdempotent

Read recent messages from a Teams channel to catch up on discussions. Provide team and channel IDs to retrieve each message's text, author, timestamp, and importance.

Instructions

Read the most recent messages in one Teams channel, newest first, given a team id and channel id from chat_list_teams and chat_list_channels. Returns text, author, timestamp and importance. Use to catch up on a channel; chat_search_messages is the tool for finding a message by keyword across everything. Requires ChannelMessage.Read.All.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idYesThe team ID
channel_idYesThe channel ID
max_resultsNoMaximum messages to return
Behavior4/5

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

Annotations already convey safety (readOnly, idempotent, non-destructive), and the description adds behavioral details beyond that: chronological ordering, return fields (text, author, timestamp, importance), and permission requirement. This goes beyond annotation coverage, but could mention pagination behavior, though the schema covers max_results.

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 two sentences, front-loaded with the core action and input source, and includes usage guidance and permission in a compact manner. Every sentence earns its place with no redundancy or filler.

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 simple list tool with strong annotations and full schema coverage, the description provides the needed context: what it returns, ordering, usage context, and permissions. The output schema is absent, but the description covers the return fields sufficiently, making the tool actionable.

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

Parameters4/5

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

Schema coverage is 100% for all three parameters, so the baseline is 3. The description adds meaningful context by indicating where the team_id and channel_id come from (chat_list_teams and chat_list_channels), which is not in the schema. This extra guidance elevates the score.

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 tool reads the most recent messages in a Teams channel with a specific verb ('read') and resource ('messages'), and specifies ordering (newest first). It also references how to obtain the required IDs from sibling tools, distinguishing it from chat_search_messages.

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?

Explicit usage guidance is provided: 'Use to catch up on a channel' and explicitly names the alternative tool for keyword search (chat_search_messages). It also mentions the required permission, further clarifying when this tool is appropriate.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nitin27may/ms-graph-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server