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MCP Server for notify to weixin / telegram / bark / lark

企业微信群机器人-发送文本消息

wework_send_text

Send text or Markdown messages to WeChat Work group chats using bot integration. Configure bot keys to deliver notifications with character limits up to 4096 bytes.

Instructions

通过企业微信群机器人发送文本或Markdown消息

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes消息内容,长度限制: (text: 2048个字节, markdown_v2: 4096个字节)
msgtypeNo内容类型,仅支持: text/markdown_v2text
bot_keyNo企业微信群机器人key,uuid格式,默认从环境变量获取

Implementation Reference

  • Handler function for 'wework_send_text' tool. Sends text or markdown messages via WeWork group robot webhook using requests.post. Includes input schema via Pydantic Field.
        title="企业微信群机器人-发送文本消息",
        description="通过企业微信群机器人发送文本或Markdown消息",
    )
    def wework_send_text(
        text: str = Field(description="消息内容,长度限制: (text: 2048个字节, markdown_v2: 4096个字节)"),
        msgtype: str = Field("text", description="内容类型,仅支持: text/markdown_v2"),
        bot_key: str = FIELD_BOT_KEY,
    ):
        if msgtype == "markdown":
            msgtype = "markdown_v2"
        res = requests.post(
            f"{WEWORK_BASE_URL}/cgi-bin/webhook/send?key={bot_key or WEWORK_BOT_KEY}",
            json={"msgtype": msgtype, msgtype: {"content": text}},
        )
        return res.json()
  • Registers the 'wework_send_text' tool (among others from wework.py) by calling add_tools on the FastMCP instance.
    wework.add_tools(mcp)
  • Pydantic Field definitions reused in 'wework_send_text' and other tools for input validation.
    FIELD_BOT_KEY = Field("", description="企业微信群机器人key,uuid格式,默认从环境变量获取")
    FIELD_TO_USER = Field("", description="接收消息的成员ID,多个用`|`分隔,为`@all`时向该企业应用全部成员发送,默认从环境变量获取")
  • Imports the wework module containing the add_tools function that registers 'wework_send_text'.
    from . import (
        wework,
        tgbot,
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions sending messages but lacks details on authentication (e.g., how 'bot_key' is used), rate limits, error handling, or what happens on success/failure. For a tool that interacts with an external service, this is a significant gap in 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 a single, efficient sentence that front-loads the core functionality ('通过企业微信群机器人发送文本或Markdown消息'). It wastes no words and is appropriately sized for the tool's complexity, making it easy to scan and understand quickly.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't address key contextual aspects like authentication needs, response format, or error conditions. For a tool with external dependencies and multiple parameters, more information is needed to ensure reliable use by an AI agent.

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 already documents all parameters ('text', 'msgtype', 'bot_key') with details like length limits and defaults. The description adds no additional meaning beyond what's in the schema, such as examples or usage context. This meets the baseline for high schema coverage but doesn't enhance understanding.

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 ('发送文本或Markdown消息') and resource ('企业微信群机器人'), making the purpose immediately understandable. It distinguishes from siblings like 'wework_send_image' or 'wework_send_news' by specifying text/markdown content. However, it doesn't explicitly contrast with 'wework_app_send_text', which might be a similar tool for a different API endpoint, leaving some ambiguity.

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?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools for different messaging platforms (e.g., 'ding_send_text', 'lark_send_text') and WeWork variants (e.g., 'wework_app_send_text'), there is no indication of context, prerequisites, or exclusions. This lack of differentiation could lead to confusion in tool selection.

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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