send_message【需要登录】在某个会话里以当前用户身份发一条文字消息。先用 list_my_conversations / start_conversation 拿 conversationId。
【注意】这会真的把消息发给对方——发送前请向用户确认收件人和内容。
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | 消息正文（纯文本） | |
| conversationId | Yes | 会话 id |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only mark it as non-read-only and non-idempotent, but the description goes further by warning that the message is actually delivered to the recipient and instructing the agent to confirm recipient and content before sending. This is exactly the kind of side-effect disclosure needed for a messaging tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two terse, front-loaded sections: prerequisites and action first, side-effect warning second. No redundant words; every sentence adds necessary operational or safety information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple two-parameter tool with no output schema, the description covers authentication, prerequisite tools, the action, and the real-world consequence. It is complete enough for an agent to invoke it correctly and safely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters have descriptions in the schema. The description only calls content a 'text message' and mentions 'recipient and content' in the confirmation warning, reinforcing but not adding new parameter semantics. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action — sending a text message in an existing conversation as the current user — and names the resource (conversation). This clearly separates it from read_messages and send_share_card, so the tool's role is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It tells the agent the prerequisites: authentication and obtaining a conversationId via list_my_conversations or start_conversation. This establishes the correct call sequence and context. It does not explicitly list when-not-to-use or alternatives, but the prerequisite examples make the intended usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly documented purpose, often with explicit 'when to use' guidance and cross-references, making the vast majority easy to tell apart. A few clusters (get_my_brief, get_my_positioning, get_my_work, get_my_dispatch) and data-overlapping get_my_card vs get_my_profile require careful reading, but descriptions are detailed enough to prevent serious misselection.
The overwhelming majority follow snake_case verb_noun conventions (create_product, update_need, list_my_signups). Minor deviations include noun-only feed names (personalized_feed, random_feed), inconsistency between 'prefs' and 'preferences' in notification tools, and a mix of update_* and set_* for mutations, but the pattern remains predictable overall.
137 tools is an extreme mismatch for any MCP server, far exceeding the 50+ threshold for a score of 1. Even with a broad multi-domain platform, this volume makes tool selection and navigation impractical and heavily burdens the agent's context window.
The surface covers full lifecycles for needs, products, activities/signups, conversations, collaboration goals/tasks, dispatch, profile/onboarding, and supporting resources like companies, parks, policies, and ratings. Deliberate omissions (no user-post creation, no organizer profile editing via agent) are explicitly documented, so core workflows have no obvious dead ends.