get_my_dispatch【需要登录】查看安排路径、建议接洽的人/活动、等待结果反馈的安排和对方来找我的请求。仅服务端灰度已开启的账户可用。此调用会将展示的当前建议标记为已看，影响后台自动换人，故不是纯只读。fillStatus=FILLING 可稍后重查；FAILED 如实报告失败，不自动重新汇报。接受建议用 accept_dispatch_arrangement，反馈用 set_dispatch_outcome。
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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?
The description discloses the key side effect beyond annotations: calling this tool marks the current suggestion as seen and affects backend automatic replacement, explicitly stating '不是纯只读'. It also explains fillStatus semantics and that FAILED results are not automatically re-reported. This aligns with readOnlyHint=false and adds meaningful operational nuance.
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?
The description is compact but information-dense, front-loading the login requirement and scope, then covering side effects, state handling, and alternatives without wasted words. Semicolon-separated clauses keep related behaviors grouped and scannable.
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 zero-parameter, no-output-schema tool, this description is remarkably complete: it states prerequisites, scope, side effects, retry semantics, and even cross-references the relevant sibling tools. An agent has enough context to decide when to call it and what to do with the result.
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?
There are zero parameters and the input schema is empty, so the baseline is 4. The description adds no parameter-related confusion and even references fillStatus, which is a response/state field rather than an input parameter. No additional parameter documentation is needed.
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 uses the specific verb '查看' and identifies the resource as dispatch arrangements, enumerating the exact categories it covers (paths, suggested contacts/activities, items awaiting feedback, and inbound requests). It also names accept_dispatch_arrangement and set_dispatch_outcome as the follow-up tools, making the boundary between viewing and acting explicit.
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?
The description gives clear context: it requires login, is gated by a server-side gray release, and should be used for viewing rather than accepting or providing feedback—those are explicitly routed to sibling tools. It also advises on retry behavior for FILLING and how to treat FAILED results. However, it does not mention when to use decline/skip/report/respond_dispatch_inbound, so guidance is not exhaustive.
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.