get_product按 id 或 slug 获取产品完整详情（owner 主理人 / 描述 / 链接 / 媒体 / 分类 / 标签 / 发布时间）。两个参数二选一，slug 优先。
【何时用】用户点了某个产品想看详情，或 search/list 返回后要展开看某条。
【相关 resource】也可以用 resources/read URI: opcmenu://product/{slug}。
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | 产品 id（cuid） | |
| slug | No | 产品 slug（URL 上 /p/<slug>） |
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 already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond that: the two parameters are mutually exclusive, slug takes priority, and the returned detail fields are listed. No contradiction with annotations.
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 and well-structured with three labeled sections: action, when-to-use, and related resource. Every sentence earns its place, and the main behavior is front-loaded.
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 read-only getter with no output schema, the description covers input selection, parameter precedence, usage scenarios, and the content of the returned detail. An agent has everything needed to call it correctly.
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 description coverage is 100%, so the baseline is 3. The description adds the important rule '两个参数二选一，slug 优先', which the schema does not encode (both are optional). This meaningfully improves invocation correctness.
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?
Description clearly states '按 id 或 slug 获取产品完整详情' and enumerates the fields returned (owner, description, links, media, categories, tags, publish time). It is unmistakably a single-product detail fetcher, distinct from list/search/rating siblings.
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 '【何时用】' section explicitly tells an agent when to invoke: when a user clicks a product or wants to expand a search/list result. It does not explicitly name alternative tools or exclusions, so it falls just short of a 5.
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.