MCP MeasureMCP Measure

索引已观测

Feedback Synthesis MCP

Synthesize GitHub Issues, HN and App Store reviews into ranked pain clusters. Pay-per-call x402.

我该使用它吗

质量与安全性

A
描述质量
68%
模式完整度
98%
命名质量
95%
投毒风险
100%
权限匹配度
100%
协议合规性
100%

发现（3）

  • LOWTool 'synthesize_feedback' description lacks action verb在 synthesize_feedback 中
  • LOWTool 'get_pain_points' description lacks action verb在 get_pain_points 中
  • LOWTool 'get_sentiment_trends' description lacks action verb在 get_sentiment_trends 中

基于对工具定义和协议合规性的自动分析。

上下文开销

~751token 数（工具定义）
~2.0 KB典型响应大小
对注意力有中等影响（占 128k 上下文窗口的 0.59%）

这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高，可用于其他任务的注意力就越少。

安装

一键安装

将以下内容添加到你的 `claude_desktop_config.json` 文件中：

{
  "mcpServers": {
    "feedback-synthesis-mcp": {
      "command": "uvx",
      "args": [
        "feedback-synthesis-mcp"
      ]
    }
  }
}

可运行的软件包

pypifeedback-synthesis-mcp0.1.1stdio

远程端点

https://feedback-synthesis-mcp-production.up.railway.app/mcpstreamable-http

它能做什么

工具清单

工具（4）

🟢 只读🟡 写入🔴 删除⚪ 未知
⚪synthesize_feedback(sources, focus, max_items_per_source, since)

Multi-source feedback synthesis into ranked pain clusters.

输入模式

{
  "type": "object",
  "properties": {
    "sources": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "type": "array",
      "description": "List of source configs. Each must have \"type\" (github_issues, hackernews, appstore) and \"target\" (e.g. \"owner/repo\", \"product name\", app ID)."
    },
    "focus": {
      "default": "",
      "type": "string",
      "description": "Optional focus area to prioritize (e.g. \"performance\", \"onboarding\")."
    },
    "max_items_per_source": {
      "default": 100,
      "type": "integer",
      "description": "Max feedback items per source (default 100)."
    },
    "since": {
      "default": "",
      "type": "string",
      "description": "Only include feedback after this date (ISO 8601, e.g. \"2025-01-01\")."
    }
  },
  "required": [
    "sources"
  ],
  "additionalProperties": false
}

输出模式

{
  "type": "object",
  "additionalProperties": true
}
🟢get_pain_points(source, top_n, max_items)

Quick single-source pain point extraction.

输入模式

{
  "type": "object",
  "properties": {
    "source": {
      "additionalProperties": true,
      "type": "object",
      "description": "Source config with \"type\" (github_issues, hackernews, appstore) and \"target\"."
    },
    "top_n": {
      "default": 10,
      "type": "integer",
      "description": "Number of top pain points to return (default 10)."
    },
    "max_items": {
      "default": 50,
      "type": "integer",
      "description": "Max feedback items to analyze (default 50)."
    }
  },
  "required": [
    "source"
  ],
  "additionalProperties": false
}

输出模式

{
  "type": "object",
  "additionalProperties": true
}
🟢search_feedback(query, sources, source, target, since, ...)

Full-text search across cached feedback items.

输入模式

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "Search query string."
    },
    "sources": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional list of source types to filter (e.g. [\"github_issues\"], [\"github\"]).\nAccepted values: github_issues (or \"github\"), hackernews, appstore."
    },
    "source": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Singular alias for sources — accepts a single source type string.\nIf both source and sources are provided, sources takes precedence."
    },
    "target": {
      "default": "",
      "type": "string",
      "description": "Optional target filter (e.g. \"owner/repo\" or app ID)."
    },
    "since": {
      "default": "",
      "type": "string",
      "description": "Only include items after this date (ISO 8601, e.g. \"2025-01-01\")."
    },
    "limit": {
      "default": 20,
      "type": "integer",
      "description": "Max results to return (default 20)."
    }
  },
  "required": [
    "query"
  ],
  "additionalProperties": false
}

输出模式

{
  "type": "object",
  "additionalProperties": true
}
🟢get_sentiment_trends(sources, granularity, since)

Time-series sentiment analysis across feedback sources.

输入模式

{
  "type": "object",
  "properties": {
    "sources": {
      "items": {
        "additionalProperties": true,
        "type": "object"
      },
      "type": "array",
      "description": "List of source configs. Each must have \"type\" and \"target\"."
    },
    "granularity": {
      "default": "weekly",
      "type": "string",
      "description": "Time granularity — \"weekly\" or \"monthly\" (default \"weekly\")."
    },
    "since": {
      "default": "",
      "type": "string",
      "description": "Only include feedback after this date (ISO 8601)."
    }
  },
  "required": [
    "sources"
  ],
  "additionalProperties": false
}

输出模式

{
  "type": "object",
  "additionalProperties": true
}

社区

评价此服务器

证据

最近观测

已验证未记录版本4 个工具
已验证未记录版本4 个工具
已验证未记录版本4 个工具