Skip to main content
Glama

get_teacher_inbox

Consolidate pending teacher tasks from submissions, live sessions, messages, and contract proposals into a prioritized inbox, with optional incremental cursor and open feedback summary.

Instructions

增量教师待办清单 (Agent Surface Hardening 第一批, P1) — 醒来先看这个而不是自己拼 live_pending + submissions + adhoc 好几刀. 输入 { pair_id?, since? } (since 缺省=全量, ISO 时间戳游标——服务端不维护游标状态, 消费方自己记住 max(occurred_at) 下次传回). 来源: 待批改 submission / 无 reflection 的已结束 live session / 学习者新 adhoc 消息 / 近 24h 同一张卡 ≥3 次 Again / proposed 未签合同. 每项 { item_id(确定性), type, priority, resource_refs, recommended_tool, occurred_at }, 按 priority (high→low) 排序, 同优先级按 occurred_at 升序. 无待办返回空数组. 另附 open_feedback 段 (现场反馈笔): status=open 的学习者反馈 count + 最近若干条摘要 (kind/原话节选/挂锚/账龄)——纯发光提醒, 软牙齿: 不阻塞任何闭环动作, 回应它用 update_feedback_status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNoISO timestamp cursor, 缺省=全量待办
pair_idNo
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 rich detail: server statelessness (consumer must track cursor), deterministic item IDs, sorting rules, empty-array return, and non-blocking open_feedback behavior. This exceeds typical 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 dense but every sentence earns its place: purpose, input semantics, sources, output fields, sorting, and special segment. Front-loaded with the primary action and cleanly structured into logical sections.

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?

Given the tool's complexity and the absence of an output schema, the description thoroughly explains return fields, sources, cursor behavior, pagination state, and the open_feedback section. It is fully actionable for an agent without external 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 coverage is only 50%; the description adds helpful semantics for 'since' (default full list, ISO cursor, stateless behavior) but provides no explanation for 'pair_id' beyond indicating it is optional. A partial compensation, leaving a notable gap.

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 this is an 'incremental teacher todo list' and explicitly differentiates from sibling tools by saying 'look here first instead of piecing together live_pending + submissions + adhoc'.

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?

The description explicitly says to use this tool 'when waking up' and names the alternatives (live_pending, submissions, adhoc). It also directs open_feedback handling to update_feedback_status, providing clear guidance on when to use it versus a related tool.

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/sf-shenfeng/learn-shell'

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