MCP Analytics

ai.mcpanalytics由 mcpanalytics.ai 發布官方發布者v1.0.8更新於 Oct 24, 2026

A statistical analyst in your AI chat: bring data and a question, get a citable, re-runnable report.

已驗證Streamable HTTP可網頁執行資料庫資料與分析

概覽

AI 產生的概覽

遠端 MCP 伺服器，扮演統計分析助手：上傳資料、提出業務問題，取得可引用、可重跑的分析報告。

可以做什麼
  • 上傳、列出與搜尋資料集，並可原地重新整理既有資料集
  • 把問題導向合適的處理路徑（重用、重跑、快速回答、函式庫工具執行、估算或客製建置），本身不執行也不計費
  • 執行資料快速讀取、經過驗證的函式庫分析，或依已確認的估算下單客製分析
  • 在執行前檢視並調整報告估算、檢查工具適用性、查找先例
  • 瀏覽已交付報告及其卡片與可分享連結，並跨所有已交付分析提問
  • 管理每週或每月的定期重跑、查看建置狀態，以及查詢組織資料倉儲
適用情境
適合在對話中對自己的資料集做統計或業務分析，取得標明方法、含圖表、可在新資料上重跑的報告。它針對資料問題，而非通用助理。
執行需求
遠端 Streamable HTTP 端點，不需本機套件。需要 MCP Analytics 帳號，並透過 X-API-Key 標頭提供 API 金鑰；沒有金鑰時僅能以探索模式列出工具。倉儲查詢需在商業方案中啟用。
安裝前請注意
API 金鑰是機密憑證，透過 X-API-Key 標頭傳送。上傳的資料會送往第三方服務；README 表示資料僅用於回答你的問題，保留期限請見隱私政策。部分路徑會產生付費工作：客製建置需要使用者明確確認與冪等鍵，應先審閱估算。定期排程會建立重複執行並以電子郵件寄送報告。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 MCP Analytics，將其新增到工作區。
  2. 連接 X-API-Key 憑證。它們會被私密儲存，絕不會在公開頁面上展示。
  3. 為需要使用其工具的對話啟用該服務。

Web executable，透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式，或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南，先向你展示發布者、端點和登入方式，經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/ai.mcpanalytics%2Fanalytics/install.md，並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前，先告訴我它的發布者、端點和登入方式，等我確認。不要把 API 金鑰寫進命令或我會提交的檔案；問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "analytics": {
      "type": "http",
      "url": "https://api.mcpanalytics.ai/mcp/api-key",
      "headers": {
        "X-API-Key": "<YOUR_API_KEY>"
      }
    }
  }
}

README

MCP Analytics

A statistical analyst in your AI chat. Bring a dataset and a question, get back a citable, re-runnable report with its method named.

MCP Analytics is a Model Context Protocol server that answers business and statistical questions about your data, as deep as each question needs:

  • Answer: a quick question about the data, read from all of it, shown with how we got it, and saved.
  • Analytics: a tested statistical method runs on the data, and we interpret it with charts and a written report.
  • Custom Analytics: a question only your business asks, built for your data from proven models, yours to re-run on next month's file.

Intelligent routing picks the depth: you describe the question, and the server says which path answers it and why. Analytics is repeatable: same data, same numbers. Every report names its method and the code that ran it, and runs again on new data.

It is for business and statistical questions about data, not a general-purpose assistant.

Website: mcpanalytics.ai · Free tools you can try in the browser: mcpanalytics.ai/free

Connect

Endpoint: https://api.mcpanalytics.ai/mcp/api-key (Streamable HTTP, JSON-RPC)

Authentication: an API key in the X-API-Key header. Create an account at mcpanalytics.ai and copy your key from your account settings.

json
{  "mcpServers": {    "mcpanalytics": {      "url": "https://api.mcpanalytics.ai/mcp/api-key",      "headers": { "X-API-Key": "mcp_your_key_here" }    }  }}

Claude Code:

bash
claude mcp add --transport http mcpanalytics https://api.mcpanalytics.ai/mcp/api-key --header "X-API-Key: mcp_your_key_here"

Browse before you sign up. Without a key the server answers in discovery mode: you can connect and list the tools, but not run anything.

bash
curl -X POST https://api.mcpanalytics.ai/mcp/api-key \  -H 'Content-Type: application/json' \  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Start

Ask your AI assistant a question about your data, for example:

  • "Upload sales.csv. What changed in revenue from May to June, and which channel drove it?"
  • "Which customers are most likely to churn?"
  • "Forecast next quarter's sales from this file."

The first call is the resolver: it reads your question and your data, executes nothing and charges nothing, and names the path that answers it (an answer, a library analysis, or a custom build) with the reason. Then it runs that path.

What you get

  • A report with charts, tables and a written interpretation, with its method named and the code that ran it.
  • Re-runs on new data: run the same analysis on next month's file.
  • A library of your reports you can search and ask questions across.

Data and privacy

Your uploaded data is used only to answer your questions. See the privacy policy for what we keep and for how long. Security issues: see SECURITY.md.

Support

License

See LICENSE.

來源：README.md，提交 33a3580

工具

27

about

Platform documentation and info: how it works (Answer, Analytics, Custom Analytics), usage.

輸入參數topic必填

agent_advisor

AI help desk: which analysis fits your question, interpreting results, fixing errors. Multi-turn.

輸入參數message必填

datasets_upload

Get your data in. Pass `data` as an array of row objects to create the dataset immediately and get a dataset_ref ready for path_resolver; omit it to get an upload link for a file only the user can reach. Add replace_ref (uuid://ID:KEY) with data to REFRESH an existing dataset in place; schedules and tools holding that reference read the new data on their next run.

輸入參數dataexpires_inreplace_ref

datasets_list

List and search your uploaded datasets, with fuzzy matching on name, description, and tags. Returns each dataset's uuid:// reference for use in path_resolver and path_tool_run.

輸入參數limitsearch

discover_tools

Browse the analyses you can run: the ones you commissioned plus the platform Standard Library (prebuilt tools; each result tagged source:'own' or 'standard_library'). Plain-language match; no query lists everything, your own first. Nothing fits? Commission it with path_resolver.

輸入參數query

tools_schema

Get an analysis's parameter schema. ALWAYS call before path_tool_run.

輸入參數tool_name必填

review_estimate

The estimate as you review it WITH the user: the question as understood, the estimated answer (sample, marked), every place and its question, the page link, a review checklist. Before path_bespoke.

輸入參數estimate_id必填

adjust_estimate

Apply the user's layout wishes to the estimate's page through the layout agent; a new named arrangement, nothing overwritten, no number changes.

輸入參數estimate_id必填instructions必填

find_precedent

Before estimating: how did we answer this objective before, on this data or any data? Prior packages and library runs with their tools, mappings, bespoke module names, method and verdicts. Platform-wide, read-only.

輸入參數kobjective必填dataset_ref

check_tool_fit

Before naming a library tool: does it fit THIS dataset for THIS question? Column mapping, missing required inputs, method-fit verdict, the places it delivers. Read-only.

輸入參數objectivetool_name必填dataset_ref必填

package_status

Read an analytics package back: status, every run under it, the report link once delivered.

輸入參數package_id必填

my_objects

List and search the objects you own across every question: the curated charts, tables and figures of each delivered package, grouped by objective.

輸入參數limitqueryinclude_dropped

build_status

Check a commissioned build in-chat: stage progress, queue position, rejection reason if the data didn't match the objective, honest ETA, report link when delivered.

輸入參數pipeline_idtrack_token

ask_library

Ask a question across all your delivered analyses: a synthesized answer with citations back to specific reports.

輸入參數question必填

reports_list

Your report library: every analysis delivered, with status and links. Pass semantic_query to search report content in plain language.

輸入參數limitsemantic_query

warehouse

Query your org's data warehouse free: browse the catalog (tables with column roles + computed metrics), semantically find data, plain-language ask, or named templates. Requires warehouse enablement (business plans).

輸入參數queryaction必填paramsquestion

schedules

Standing re-runs of analyses you own: action='create' (weekly/monthly against a re-runnable data reference, connector:// or an https:// link; report emailed after each run), 'list', or 'cancel'.

輸入參數action必填cadencetool_namedataset_refschedule_idcolumn_mapping

reports_view

Get a shareable browser link for a report, viewable without authentication.

輸入參數processing_id必填

report_cards

Browse a delivered report's individual cards (charts, tables, insights) inline in chat.

輸入參數processing_id必填

path_resolver

Which path a question about data should take (reuse, rerun, answer, tool_run, estimate, bespoke, ask or none), with the reason, every path's score and time, and the ready input for the path it picks. Executes nothing and charges nothing.

輸入參數doormetaobjective必填dataset_ref

path_reuse

Hand back the report already delivered for this question on this data. Time: as path_resolver quotes.

輸入參數objective必填dataset_ref必填resolution_id

path_rerun

Run a delivered analysis again, on its own dataset or a new one: the same tools and mappings, no rebuild. Time: as path_resolver quotes.

輸入參數package_id必填dataset_refresolution_id

path_answer

A read of the data (a total, an average, a count, the top group), computed on every row, in this response. When the question is not a read it says so and names the next step. Time: as path_resolver quotes.

輸入參數objective必填dataset_ref必填resolution_id

path_tool_run

Run one or more tested library tools on this dataset, with charts and a written report. No custom build. Time: as path_resolver quotes.

輸入參數objective必填tool_names必填dataset_ref必填estimate_idresolution_idcolumn_mappingidempotency_keymodule_parameters

path_estimate

A rough answer over a sample and the layout of the full report, for the person to confirm before anything is ordered. Time: as path_resolver quotes.

輸入參數objective必填tool_namesdataset_ref必填completenessresolution_idlayout_objective

path_bespoke

Order a custom analysis built for this data from a confirmed estimate, optionally with library tools alongside. Needs the person's yes and an idempotency key; a retry returns the first order. Time: as path_resolver quotes.

輸入參數tool_namesestimate_id必填resolution_ididempotency_key必填layout_objective

版本歷史

2
  1. v1.0.8最新Oct 15, 2026
  2. v1.0.7Sep 16, 2026

評分

已滿足適用檢查項的 100%

該評分衡量此收錄條目的完整程度，依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價，也不表示伺服器安全或目前可用：可連通性是帶日期的檢查結果，評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 圖示10 分
  • 文件15 分
  • 授權條款10 分