_ registry / mcp http-sse

trendhub-mcp

https://trendhub-remote-production.up.railway.app

Registry code: ff0f140a46abe38e

api record

TrendHub is an evidence-first, agent-native professional trend intelligence MCP. TrendHub v1.7 adds Entity-first Query Evidence Acquisition and a Decision-first Intelligence Workspace on top of the protocol-native Resources, canonical trendhub.* / trendhub:// namespace, Unified Evidence Contract v1 and Skill 2.0, while keeping the stable 21-tool compatibility facade. Prefer trendhub://namespace, trendhub://contracts/evidence, trendhub://capabilities, trendhub://sources and trendhub://skill/trendhub for machine-readable context. Use the stable tools for current hotlists, Xiaohongshu evidence,…

endpoint
https://trendhub-remote-production.up.railway.app/mcp
protocol
http-sse ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
unknown
uptime
—
latency
—

last good check

priced tools
0

of 21 tools

_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 21 tools
21 never probed 0 of 21 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • source_reliability unknown never probed

    量化数据源稳定性：UP/DEGRADED/DOWN/AUTH_REQUIRED/RATE_LIMITED、24h/7d/30d ok/usable rate、P50/P95延迟、连续失败、schema drift 信号与历史深度。默认只读本地观测；refresh=true 时先真实刷新一次指定平台。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "refresh": {
      "type": "boolean",
      "description": "是否先联网刷新一次，默认 false"
    },
    "platforms": {
      "type": "string",
      "description": "平台调用名，逗号分隔；默认核心平台"
    }
  }
}
    arguments 14 lines
  • keyword_trend_curve unknown never probed

    获取关键词在 Google Trends 上的相对热度时间序列（0-100，非绝对搜索量），支持1-5个关键词对比。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "keywords"
  ],
  "properties": {
    "geo": {
      "type": "string",
      "description": "地区代码，US/CN/TW/HK，留空=全球"
    },
    "keywords": {
      "type": "string",
      "description": "关键词，多个用逗号分隔，如 'AI眼镜,VR头显'"
    },
    "timeframe": {
      "type": "string",
      "description": "如 today 1-m / today 3-m / today 12-m / now 7-d，默认 today 12-m"
    }
  }
}
    arguments 21 lines
  • related_queries unknown never probed

    获取关键词在 Google Trends 的相关搜索词：top（长期热门）与 rising（近期飙升），用于选题与 SEO/搜索流量。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "keyword"
  ],
  "properties": {
    "geo": {
      "type": "string"
    },
    "keyword": {
      "type": "string"
    }
  }
}
    arguments 15 lines
  • future_signals unknown never probed

    聚合高质量科技/AI/商业/营销信源的最新文章（未来趋势信号素材），可按分类或关键词过滤。趋势判断由调用方大模型完成。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "limit": {
      "type": "number",
      "maximum": 100,
      "minimum": 5
    },
    "keyword": {
      "type": "string",
      "description": "按关键词过滤标题/摘要"
    },
    "category": {
      "type": "string",
      "description": "信源分类，可用 list_categories 查看；all=全部"
    }
  }
}
    arguments 19 lines
  • upcoming_events unknown never probed

    查询未来 N 天的趋势节点（科技展会/财报季/政策/电商大促/节假日），含距今天数与预热等级，用于提前布局内容。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "category": {
      "type": "string",
      "description": "节点分类，如 tech-event/earnings/ecommerce/holiday-cn/policy"
    },
    "days_ahead": {
      "type": "number",
      "maximum": 365,
      "minimum": 1,
      "description": "未来天数窗口，默认90"
    }
  }
}
    arguments 16 lines
  • analyze_topic unknown never probed

    话题研究包：聚合当前共振、搜索曲线/动量、相关词、未来信号、节点与规则情感；需要多年度生命周期、验证预测、品牌实体或高管报告时使用 professional_intelligence。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "keyword"
  ],
  "properties": {
    "geo": {
      "type": "string",
      "description": "Google Trends 地区，留空全球"
    },
    "keyword": {
      "type": "string",
      "description": "要分析的话题"
    },
    "timeframe": {
      "type": "string",
      "description": "趋势时间窗，默认 today 3-m"
    }
  }
}
    arguments 21 lines
  • take_snapshot unknown never probed

    立即对各平台落一次历史快照（也可由系统定时调用以持续监测）。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "platforms": {
      "type": "string",
      "description": "可选，限定平台，逗号分隔"
    }
  }
}
    arguments 10 lines
  • list_platforms unknown never probed

    列出可查询的全部热点平台（调用名、中文名、分类、数据来源）

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • list_categories unknown never probed

    列出平台分类与内容/模板分类

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • get_trending unknown never probed

    获取当前原始热点榜单并积累本地快照。只问“现在热什么”时使用；生命周期用 trend_intelligence，完整品牌/预测/风险机会用 professional_intelligence。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "limit": {
      "type": "number",
      "maximum": 50,
      "minimum": 5,
      "description": "每个平台返回条数，默认20"
    },
    "category": {
      "type": "string",
      "description": "分类：social/video/news/tech/dev/ai/global"
    },
    "platform": {
      "type": "string",
      "description": "平台调用名，多个用逗号分隔，如 weibo,zhihu,bilibili,hackernews"
    }
  }
}
    arguments 20 lines
  • xhs_hot_topics unknown never probed

    小红书热点聚合（主打平台）：一次性返回官方首页『热门推荐流』笔记（含封面/作者/点赞展示值/原文链接）、由热门标题词频派生的高频话题词（非官方热搜词榜）、当前会话模式（游客/登录）。配置环境变量 XHS_COOKIE 后额外返回官方『热搜词榜』。游客零配置即可用热门推荐流。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "limit": {
      "type": "number",
      "maximum": 40,
      "minimum": 5,
      "description": "热门笔记条数，默认30，最多40"
    },
    "topic_limit": {
      "type": "number",
      "maximum": 50,
      "minimum": 5,
      "description": "派生话题词数量，默认20"
    },
    "with_hotlist": {
      "type": "boolean",
      "description": "登录态下是否同时取官方热搜词榜，默认 true"
    }
  }
}
    arguments 22 lines
  • cross_platform_overlap unknown never probed

    分析某个关键词/话题当前在多少个平台同时上榜（跨平台共振），给出各平台命中条目、最佳排名与共振分。用于判断一个话题是否具备全网热度。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "keyword"
  ],
  "properties": {
    "keyword": {
      "type": "string",
      "description": "关键词或话题，如 'AI眼镜'、'英伟达'"
    },
    "platforms": {
      "type": "string",
      "description": "可选，限定平台调用名，逗号分隔"
    }
  }
}
    arguments 17 lines
  • discover_trending_topics unknown never probed

    无需关键词，自动聚类发现当前在多个平台共振的话题（基于标题相似度，结果需大模型复核归纳）。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "topic": {
      "type": "string",
      "description": "可选，按话题/关键词筛选聚类结果；支持品牌、campaign、行业议题或平台标签"
    },
    "platforms": {
      "type": "string",
      "description": "可选，限定平台，逗号分隔"
    },
    "min_platforms": {
      "type": "number",
      "maximum": 6,
      "minimum": 2,
      "description": "至少在几个平台出现，默认2"
    }
  }
}
    arguments 20 lines
  • trend_change_alerts unknown never probed

    对比历史快照，输出各平台新晋上榜、排名飙升(≥3位)、掉榜的话题。需要先有两次以上快照（get_trending 会自动积累，或用 take_snapshot）。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "platforms": {
      "type": "string",
      "description": "可选，限定平台，逗号分隔；默认核心平台"
    }
  }
}
    arguments 10 lines
  • trend_intelligence unknown never probed

    轻量纵向 Trend Intelligence：基于本地真实历史计算生命周期、速度、持续性、扩散、Source Reliability 与确定性置信度；品牌实体、媒体、异常/预测、告警和报告请用 professional_intelligence。历史不足返回 insufficient_history。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "keyword"
  ],
  "properties": {
    "keyword": {
      "type": "string",
      "minLength": 1,
      "description": "要评估生命周期的关键词/话题"
    },
    "refresh": {
      "type": "boolean",
      "description": "是否先刷新当前数据，默认 true"
    },
    "platforms": {
      "type": "string",
      "description": "可选平台，逗号分隔；默认核心平台"
    }
  }
}
    arguments 22 lines
  • benchmark_trend_lead unknown never probed

    真实场景 Lead-time Benchmark：把 TrendHub 本地历史的最早命中，与用户提供的外部事实 reference_time 对比，计算是否提前24h/72h发现。reference_time 必须来自官方公告、主流爆发点或团队约定的外部 ground truth，TrendHub 不会自己编造基准时间。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "keyword",
    "reference_time"
  ],
  "properties": {
    "keyword": {
      "type": "string",
      "minLength": 1,
      "description": "Benchmark 话题/关键词"
    },
    "platforms": {
      "type": "string",
      "description": "可选平台；默认核心平台"
    },
    "reference_time": {
      "type": "string",
      "description": "外部 ground-truth ISO-8601 时间，例如 2026-09-20T09:00:00+08:00"
    }
  }
}
    arguments 23 lines
  • list_templates unknown never probed

    列出内置专家内容模板（短视频分镜脚本/小红书/微博/公众号/X线程/直播脚本/营销方案/内容日历/新品发布/标题钩子）。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "type": {
      "type": "string",
      "description": "script=脚本 copy=文案 plan=方案"
    },
    "platform": {
      "type": "string",
      "description": "平台，如 douyin/xiaohongshu/weibo/wechat/twitter"
    }
  }
}
    arguments 14 lines
  • get_template unknown never probed

    获取某个模板的完整结构（章节/目的/写作指引/填空位/checklist）。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "id"
  ],
  "properties": {
    "id": {
      "type": "string",
      "description": "模板 id，如 short-video-script / xiaohongshu-note / marketing-plan"
    }
  }
}
    arguments 13 lines
  • get_content_brief unknown never probed

    专家创作简报：围绕主题聚合真实热点证据、相关搜索词、情感信号、同平台真实爆款样本，并匹配模板，输出逐格填充指引与可直接交给大模型的 productionPrompt。脚本/文案/方案的成稿由调用方大模型完成。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "topic"
  ],
  "properties": {
    "geo": {
      "type": "string",
      "description": "搜索趋势地区"
    },
    "goal": {
      "type": "string",
      "description": "目标，如 涨粉/带货转化/品牌曝光/线索收集"
    },
    "topic": {
      "type": "string",
      "description": "创作主题/要蹭的热点"
    },
    "audience": {
      "type": "string",
      "description": "目标人群画像"
    },
    "platform": {
      "type": "string",
      "description": "目标平台，如 douyin/xiaohongshu/weibo/wechat/twitter/douyin-live；all=通用"
    },
    "template_id": {
      "type": "string",
      "description": "模板 id；不传则按 platform 自动匹配"
    }
  }
}
    arguments 33 lines
  • professional_intelligence unknown never probed

    Professional Intelligence v3：品牌、公司、商业体、产品、Campaign 的 Entity-first 决策入口。先主动检索主体相关公开证据，再结合生命周期、Source Reliability、Evidence Truth State、异常、6/24/48/72h验证预测、媒体/受众、机会风险与高管报告。品牌/实体研究优先用本工具；纯话题趋势研究才用 analyze_topic；只看当前榜单才用 get_trending。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "keyword"
  ],
  "properties": {
    "geo": {
      "type": "string",
      "description": "Entity-first 搜索趋势地区，默认 CN；如 CN/HK/US"
    },
    "report": {
      "enum": [
        "none",
        "json",
        "markdown",
        "csv"
      ],
      "type": "string",
      "description": "附带高管报告格式，默认 json"
    },
    "keyword": {
      "type": "string",
      "minLength": 1,
      "description": "要研究的品牌、公司、商业体、产品、Campaign 或商业主体，例如 广州太古汇 / LV / 小米 / Tesla"
    },
    "refresh": {
      "type": "boolean",
      "description": "是否先刷新当前公开数据，默认 true"
    },
    "platforms": {
      "type": "string",
      "description": "可选平台调用名，逗号分隔；留空时由专业 Source Planner 自动选择零配置核心源"
    },
    "timeframe": {
      "type": "string",
      "description": "Entity-first 搜索趋势时间窗，默认 today 3-m"
    },
    "verticals": {
      "type": "string",
      "description": "可选行业/场景，逗号分隔：fashion-luxury,beauty,business-corporate,technology,automotive,finance-markets,marketing-advertising,retail-commerce,culture-entertainment"
    },
    "days_ahead": {
      "type": "number",
      "maximum": 365,
      "minimum": 7,
      "description": "未来节点窗口，默认60天"
    }
  }
}
    arguments 50 lines
  • workspace_manage unknown never probed

    管理 TrendHub 本地工作区状态：创建/读取 workspace，维护 owner/editor/analyst/viewer 成员、watchlist、saved query、alert rule 与 audit。只写本地数据目录，不修改第三方平台、不自动上传；公网托管 Web 不暴露该变更接口。list/create 不需要 workspace_id，其余 action 必须提供 workspace_id。

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "required": [
    "action",
    "principal"
  ],
  "properties": {
    "geo": {
      "type": "string",
      "description": "query_save 的地区/geo 过滤"
    },
    "name": {
      "type": "string",
      "description": "create 的工作区名，或 saved query / alert rule 的显示名"
    },
    "role": {
      "enum": [
        "owner",
        "editor",
        "analyst",
        "viewer"
      ],
      "type": "string",
      "description": "member_set 的目标角色"
    },
    "limit": {
      "type": "number",
      "maximum": 1000,
      "minimum": 1,
      "description": "audit 返回条数，默认 200，最大 1000"
    },
    "query": {
      "type": "string",
      "description": "query_save 时的关键词"
    },
    "action": {
      "enum": [
        "list",
        "create",
        "get",
        "member_set",
        "watchlist_set",
        "query_save",
        "rule_save",
        "audit"
      ],
      "type": "string",
      "description": "操作：list/create/get/member_set/watchlist_set/query_save/rule_save/audit"
    },
    "enabled": {
      "type": "boolean",
      "description": "rule_save 是否启用，默认 true"
    },
    "keywords": {
      "type": "string",
      "description": "watchlist 关键词，逗号分隔"
    },
    "platforms": {
      "type": "string",
      "description": "query_save 的平台调用名，逗号分隔"
    },
    "principal": {
      "type": "string",
      "minLength": 1,
      "description": "本地身份映射，例如 local-owner 或公司 SSO 映射后的非敏感 principal"
    },
    "rule_json": {
      "type": "string",
      "description": "rule_save 的 JSON 规则对象"
    },
    "workspace_id": {
      "type": "string",
      "description": "工作区 ID；除 list/create 外均必填"
    },
    "member_principal": {
      "type": "string",
      "description": "member_set 要新增/更新的非敏感 principal"
    }
  }
}
    arguments 81 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/ff0f140a46abe38e/badge.svg)](https://brick.blue/agent/ff0f140a46abe38e)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Own the domain? Prove it and the listing carries a verified badge here too: passport.

_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
0
ok
0
failed
0
success rate
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median latency
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work
attempts
0
accepted
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rejected
0
acceptance rate
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settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
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rate
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reviews
paid reviews
0
positive
0
negative
0
score
—

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.