Maap

io.github.codecraf8由 GitHub 帳號 codecraf8 發布v0.1.1更新於 Oct 29, 2026

Calibrated numeric estimates with uncertainty from messy records

已驗證STDIO僅桌面資料與分析AI 與機器學習

概覽

AI 產生的概覽

Maap 讓助理檢索已訓練的數值目標，並給出帶不確定區間與門檻機率的校準估計。

可以做什麼
  • 依關鍵字與數值類型搜尋已訓練目標目錄，找出查詢所需的 target id
  • 針對單一案例與某個目標產生估計，回傳中位數、指定分位數、80% 區間以及超過指定門檻的機率
  • 透過 regime、calibration 與 novelty 標記說明該答案的可信程度
適用情境
當你希望得到附有明確不確定區間的數值答案，而非單純猜測時適用，例如依描述的情境預測價格或數量，或詢問某數值超過門檻的可能性。
執行需求
以 npm 套件形式透過 npx 在本機執行，需要 Node.js。需在 MAAP_API_KEY 提供 API 金鑰（從供應商主控台取得），並能連線至預設 API 端點；可用 MAAP_BASE_URL 覆寫端點。閒置後的首次呼叫可能因啟動 GPU 容器而耗時數十秒。
安裝前請注意
API 金鑰透過環境變數傳遞，屬機密資訊，應避免寫入共用設定檔。每次提問都會消耗付費額度，提交的案例資訊會傳送至供應商 API。答案可能被標記為超出領域、未校準或新穎，此時區間不保證涵蓋率。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Maap，將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only，透過 STDIO。 STDIO 服務會啟動本機處理程序，因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

maap-mcp

maap over MCP: ask for a number, get a calibrated distribution — a median, the range the true value should fall inside, and the probability of crossing a threshold you name.

Two tools:

ToolDoes
list_targetsSearch the 104 trained targets. Call this first — a target id is not guessable
estimateOne case, one target → median, quantiles, intervals, P(value > t), and how calibrated the answer is

Install

Get a key at dash.hitl.systems/keys, then add the server to your client.

Claude Desktop — claude_desktop_config.json:

json
{  "mcpServers": {    "maap": {      "command": "npx",      "args": ["-y", "maap-mcp"],      "env": { "MAAP_API_KEY": "apikey_..." }    }  }}

Claude Code:

bash
claude mcp add maap --env MAAP_API_KEY=apikey_... -- npx -y maap-mcp

Not on npm yet — until it is, install straight from this repo by swapping maap-mcp for github:hitl-systems/maap-mcp in any of the above. The built bundle is committed, so there is nothing to compile.

Cursor — ~/.cursor/mcp.json: the same mcpServers block as Claude Desktop.

VariableRequiredDefault
MAAP_API_KEYyes—
MAAP_BASE_URLnohttps://dash.hitl.systems

Using it

Which maap target predicts flight prices? Estimate the fare for a Vistara economy ticket Delhi → Mumbai, non-stop, booked 9 days out, and tell me the chance it is over ₹10,000.

The agent calls list_targets for the id, then estimate with your case as the state.

Read the median as the answer and the range as what to plan against. For a threshold question, pass thresholds and read P(value > t) — do not compare the median to the threshold yourself; the median says where the middle is, not how likely the threshold is.

An answer also carries how much to trust it: regime (in_domain, or new_target when the model only knows what your examples taught it), calibration ("none" means no correction survived its check, so the range carries no coverage promise), and novelty_flag when the case is unlike anything in training. Known limits are published: dash.hitl.systems/docs/calibration.

Notes

  • One question per call. The HTTP API takes up to 32 under names you choose; a flat tool is the one an agent fills in correctly.
  • The first call after an idle period starts a GPU container and can take tens of seconds. The server waits 120 s and tells the agent to retry rather than failing silently.
  • Each question counts against your allowance. A rejected call costs nothing.
  • Pricing, limits and the full wire format: dash.hitl.systems/docs/api.

MIT.

來源：README.md，提交 e26fd58

工具

2

list_targets

Search the maap catalogue of trained targets. A target is one specific number the model can predict, named by id. Call this FIRST to find the id to pass to `estimate`; ids cannot be guessed. Filter by keyword and by kind of number.

輸入參數limitqueryprimitive

estimate

Estimate a number for one case, with calibrated uncertainty. Returns a median (the answer), an 80% range the true value should fall inside about 80 times in 100, the quantiles you asked for, and the probability of crossing any threshold you name. Use the median as the answer, and the range when the decision depends on how wrong it could be. Needs a `target` id from `list_targets`. The first call after an idle period can take tens of seconds.

輸入參數state必填levelstarget必填examplesreturn_pmfthresholdsinterval_levels

版本歷史

1
  1. v0.1.1最新Oct 21, 2026

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