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

企业微信应用号-发送文本消息

wework_app_send_text

Send text or Markdown messages through WeChat Work app to specific users or all members, with support for @all and individual IDs.

Instructions

通过企业微信应用号发送文本或Markdown消息

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes消息内容,最长不超过2048个字节
msgtypeNo内容类型,仅支持: text/markdowntext
touserNo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

Implementation Reference

  • The `wework_app_send_text` function is the handler for sending text/markdown messages via WeWork app (应用号). It POSTs to the WeChat Work API with the message content, type, and recipient.
    def wework_app_send_text(
        text: str = Field(description="消息内容,最长不超过2048个字节"),
        msgtype: str = Field("text", description="内容类型,仅支持: text/markdown"),
        touser: str = FIELD_TO_USER,
    ):
        res = requests.post(
            f"{WEWORK_BASE_URL}/cgi-bin/message/send?access_token={get_access_token()}",
            json={
                "touser": touser or WEWORK_APP_TOUSER,
                "agentid": WEWORK_APP_AGENTID,
                "msgtype": msgtype,
                msgtype: {"content": text},
                "enable_duplicate_check": 1,
                "duplicate_check_interval": 60,
            },
        )
        return res.json() or {}
  • Input schema for `wework_app_send_text`: `text` (message content), `msgtype` (text/markdown), `touser` (recipient user IDs).
    text: str = Field(description="消息内容,最长不超过2048个字节"),
    msgtype: str = Field("text", description="内容类型,仅支持: text/markdown"),
    touser: str = FIELD_TO_USER,
  • The @mcp.tool decorator registers the tool with FastMCP, providing title and description metadata.
    @mcp.tool(
        title="企业微信应用号-发送文本消息",
        description="通过企业微信应用号发送文本或Markdown消息",
    )
  • The top-level MCP server is created and `wework.add_tools(mcp)` is called to register all WeWork tools including `wework_app_send_text`.
    mcp = FastMCP(name="mcp-notify", version="0.1.11")
    wework.add_tools(mcp)
    tgbot.add_tools(mcp)
    other.add_tools(mcp)
    hass.add_tools(mcp)
    util.add_tools(mcp)
  • The `get_access_token` helper function (cached) fetches an OAuth access token required for the WeWork App API call.
    @cached(TTLCache(maxsize=1, ttl=3600))
    def get_access_token():
        res = requests.get(
            f"{WEWORK_BASE_URL}/cgi-bin/gettoken",
            params={"corpid": WEWORK_APP_CORPID, "corpsecret": WEWORK_APP_SECRET},
            timeout=60,
        )
        return res.json().get("access_token")
Behavior2/5

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

No annotations provided, so the description carries full burden. It does not disclose side effects, authentication needs, rate limits, or what happens if the text exceeds the byte limit (though schema mentions max). Missing crucial behavioral details.

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 focused sentence with no wasted words. It is front-loaded and efficient.

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?

No output schema provided, and the description does not explain return values or success/failure behavior. Given the tool's simplification (no nested objects, low param count), it is minimally complete but lacks context about expected outcomes.

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 baseline is 3. The description adds no additional meaning beyond what the schema already provides for each parameter.

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 tool sends text or Markdown messages via WeChat Work app, which is a specific verb and resource. It distinguishes from other sibling tools that send files or images, but does not explicitly differentiate from the similar 'wework_send_text' sibling.

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 like wework_send_text or other messaging tools. The description only states what it does, not the context or prerequisites.

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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