personalized_feed返回千人千面的发现 feed：登录且设过兴趣（set_my_preferences）时按兴趣语义排序，否则回落「已认领优先 + 热度」。比 random_feed 更贴合用户口味，是网页登录后的默认发现页。续拉时把 nextCursor 原样回传以延续同一副牌。
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 返回条数，默认 18 | |
| cursor | No | 分页游标 nextCursor，原样回带 |
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?
注解已覆盖 readOnlyHint、idempotentHint、destructiveHint 等安全画像，描述在此基础上补充了条件排序逻辑与「续拉时把 nextCursor 原样回传以延续同一副牌」的分页行为，价值超越注解本身。唯一小缺口是无输出 schema 时未说明返回条目结构，但这是次要信息。
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?
三句话依次完成核心行为、条件分支、sibling 对比与分页须知，信息密度高且零冗余。关键约束（nextCursor 原样回传）单独成句收尾，结构清晰、重点前置。
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?
两参数均已在 schema 中完整说明，注解覆盖安全属性，描述又补齐了算法、回落条件、默认页面定位与分页延续规则，对只读 feed 工具而言几乎无缺。唯一缺口是缺少返回条目结构的说明，但因无输出 schema 且工具语义直观，影响有限。
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 对 limit（默认 18）和 cursor（原样回带）的覆盖率达 100%，参数语义已由 schema 完整承担。描述仅复述 cursor 的分页行为并补充「延续同一副牌」的语境，属于锦上添花而非必要补偿，按基线规则给 3 分。
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?
描述以具体动词「返回」界定行为，资源明确为「千人千面的发现 feed」，并披露核心算法（按兴趣语义排序，否则回落「已认领优先 + 热度」）。同时点名「比 random_feed 更贴合用户口味」，直接与同名 sibling 区分，无同义反复。
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?
清晰给出两条分支的使用前提——登录且设过兴趣时走个性化路径，否则走回落路径，并指明「是网页登录后的默认发现页」这一具体场景。虽以 random_feed 作对比并暗示了取舍，但未显式说明何时应改选 random_feed，缺少排除性指引。
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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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.