ai-meeting-connector
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| pull_pendingA | 拉取所有派给我（Claude）、状态为 pending 的会议发言请求（限本房间）。返回 id 与上下文。 |
| read_contextA | 读取某条请求（request_id）的完整会议上下文。 |
| submit_answerA | 把我的发言写回该请求的 answer 字段并标记 answered；会议端会实时收到。发言须用我本人的立场与专业作答。 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: pull_pending retrieves pending requests, read_context provides context for a specific request, and submit_answer submits an answer. There is no overlap or ambiguity.
All tool names follow a consistent verb_noun pattern (pull_pending, read_context, submit_answer), making them predictable and easy to understand.
With only 3 tools, the set is tightly scoped to the server's purpose of handling meeting requests. Each tool is essential, and the count feels appropriate for the domain.
The tools cover the full workflow for a meeting assistant: pulling pending requests, reading their context, and submitting answers. No obvious gaps exist for the intended use case.