Skip to main content
Glama

list_conversations

Retrieve Microsoft Teams chats and channels with their ID, title, and type, optionally filtered by account, to identify available conversations for reading or searching messages.

Instructions

List chats/channels (id, title, type), optionally filtered by account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
accountNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description carries the burden for behavioral disclosure. The description indicates this is a read-only listing operation returning id, title, type. It discloses the account filter behavior (optional). However, it doesn't mention pagination (limit param), ordering, or whether results are complete across all accounts. The return shape is described by the output schema but the listed fields add some context.

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?

Single sentence, front-loaded with the core purpose and return fields. Zero wasted words. Every element is functional.

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?

The tool has a simple 2-param schema with no required params and an output schema present, so the baseline burden is moderate. The description covers the purpose and the account filter adequately for a straightforward listing tool. However, the limit param is undocumented and there's no mention of how filtering or listing behaves across accounts, leaving some gaps for an agent relying solely on this description.

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 0%, so the description must compensate for parameter meaning. The description mentions the 'account' filter explicitly and names the return fields (id, title, type). However, it doesn't explain the 'limit' parameter semantics, default behavior, or how account filtering works in practice. Minimal but some value beyond the bare schema.

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

Purpose4/5

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

The description clearly states the action (list), the resource (chats/channels), and the specific fields returned (id, title, type). It distinguishes itself from siblings by indicating this returns chat/channel metadata, not messages (search_messages, recent_messages) or account list (list_accounts). Somewhat brief but clear.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. It mentions optional account filtering but doesn't explain how this relates to list_accounts or when a user would need this vs read_conversation. It doesn't exclude scenarios or name alternative tools for specific cases.

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/KamorionLabs/msteams-local-mcp'

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