Anansi Data

xyz.anansidata由 anansidata.xyz 發布官方發布者v1.1.0更新於 Oct 24, 2026

Time-series nobody else archives: LLM price history, prediction markets, gas, perp funding.

已驗證Streamable HTTP可網頁執行金融資料與分析AI 與機器學習

概覽

AI 產生的概覽

一個遠端 MCP 伺服器,提供歸檔的時間序列資料——LLM 代幣價格、預測市場、gas、永續合約資金費率——支援搜尋、快照、歷史、異動與異常偵測。

可以做什麼
將模糊的模型或資料集名稱解析為精確識別碼,並對所有資料集目前值做全文搜尋以找出數值所在資料集。讀取目前 LLM 代幣價格、上下文視窗和近期價格變動。取得整個資料集的最新快照、單一鍵的完整時間序列,或整個資料集的欄位級差異。依變化速度排序鍵,並以 z 分數標記統計異常值。一次呼叫給出單一鍵的綜合結論,包含最新值、變化速度、同類排名和異常分數。查詢完整的付費 LLM 價格變動檔案,或在價格、上下文和模態限制下取得最便宜模型建議。
適用情境
當你需要供應商會覆寫或不歸檔的歷史或目前市場與定價資料時適用,例如稽核 LLM 價格變動、為單一序列繪圖或回測,或在成本限制下決定路由到哪個模型。
執行需求
遠端 streamable HTTP 端點;未宣告本機執行環境、套件、帳號或 API 金鑰。付費工具按次以美元透過 x402 計費,通常需要相容的加密錢包或付款設定。
安裝前請注意
大多數工具按次付費(每次 0.005 至 0.05 美元,透過 x402),重複使用會產生費用。付費呼叫可能需要錢包或付款憑證。資料是供應商上次輪詢的快照,新鮮度和完整性無法保證;異常結果在年輕資料集上被標示為不可靠。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Anansi Data,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。

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/xyz.anansidata%2Fanansi-data/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": {
    "anansi-data": {
      "type": "http",
      "url": "https://anansidata.xyz/mcp"
    }
  }
}

工具

12

anansi_price_current

FREE. Current per-token prices for LLMs as recorded at our last poll: usd_per_prompt_token and usd_per_completion_token (USD per single token, not per million), context window in tokens, and the observation timestamp (unix seconds). Use this to check what a model costs right now, or to compare a handful of models before routing. Do NOT use it to see how a price MOVED — it returns only the latest observation; use anansi_price_changes_recent (free) or anansi_price_history (paid). Input: model_id is a substring match, so 'gpt-5' matches every gpt-5 variant; resolve exact IDs with anansi_search.

輸入參數limitmodel_id

anansi_price_changes_recent

FREE. LLM price changes we observed in the last 7 days: per model, which field moved (prompt, completion, cache_read), the from/to values in USD per token, the percentage move, and the unix timestamp we detected it. Providers publish only their current catalogue and overwrite it silently, so this record exists nowhere upstream. Use it to detect that a model you depend on got cheaper, dearer, or vanished. Do NOT use it for windows longer than 7 days or more than 25 rows — it is capped; use anansi_price_changes (paid) for the full unwindowed history. Input: days is clamped to 7.

輸入參數dayslimit

anansi_price_changes

PAID ($0.03 via x402). The COMPLETE observed price-change history for every tracked LLM, with no time window and no row cap: model, field, from/to in USD per token, percent move, and detection timestamp. Use it to audit a provider's pricing behaviour over the whole archive or to feed a cost model. Do NOT use it for a quick 'did anything change lately' check — anansi_price_changes_recent answers that free. Input: since is a unix timestamp; omit it for everything we hold.

輸入參數sincepayment_header

anansi_price_advisor

PAID ($0.05 via x402). Answers 'which model should I route to' in one call: returns the cheapest models satisfying hard constraints on price, context window and modality, ranked by prompt price ascending, PLUS every price change and deprecation affecting those candidates in the window. Use it when choosing or re-validating a routing target. Do NOT use it merely to look up one model's price (anansi_price_current, free). Inputs: max_prompt_price in USD per token (e.g. 0.000001); min_context in tokens; modality is a substring of the modality string such as 'text' or 'image'.

輸入參數limitmodalitywindow_smin_contextpayment_headermax_prompt_price

anansi_data_latest

PAID ($0.005 via x402). Current state of every tracked key in one dataset, as recorded at our last poll, with provenance naming the source and licence. Use it for the newest snapshot of a whole dataset — prediction-market probabilities, gas by chain, perp funding, stablecoin pegs. Do NOT use it for LLM prices (anansi_price_current is free) and do NOT use it for change-over-time (anansi_data_changes). Input: dataset must be an exact name from anansi_search; key optionally narrows to one row.

輸入參數keydataset必填payment_header

anansi_data_history

PAID ($0.02 via x402). The full recorded time-series for ONE key in one dataset, oldest to newest, each row carrying its observation timestamp. Use it to chart or backtest a single series. Do NOT use it to compare many keys at once (anansi_data_latest returns them all for one moment) and do NOT expect history predating our first observation — the archive is days old and depth varies by dataset. Inputs: dataset and key exactly as returned by anansi_search; since is a unix timestamp.

輸入參數key必填sincedataset必填payment_header

anansi_data_changes

PAID ($0.02 via x402). Field-level diffs across a whole dataset: for each key that changed, which field moved, its from and to values, and when. Use it to detect movement across many keys without pulling every series. Do NOT use it for LLM prices specifically — anansi_price_changes_recent is free and anansi_price_changes is the full version. Input: since is a unix timestamp; omit for the whole archive.

輸入參數sincedataset必填payment_header

anansi_movers

PAID ($0.03 via x402). Velocity ranking: which keys in a dataset moved fastest on one numeric field over a window, returned as rate of change with the underlying from/to values. Use it to find what is moving without reading every series. Do NOT use it to judge whether a move is unusual for that key — that is anansi_anomalies. Inputs: field must be a numeric field present in the dataset; window_s is seconds.

輸入參數field必填limitdataset必填window_spayment_header

anansi_anomalies

PAID ($0.05 via x402). Statistical outliers: keys whose latest move breaks their OWN historical baseline, returned with a z-score. Use it to separate a genuinely abnormal move from ordinary volatility. Do NOT use it to rank raw speed (anansi_movers) and treat results cautiously on a young dataset, where a short baseline inflates z-scores. Input: z_threshold defaults to 3.0.

輸入參數field必填dataset必填z_thresholdpayment_header

anansi_verdict

PAID ($0.04 via x402). One-call composite for a single key: its latest value, its velocity, its rank against peers in the same dataset, and its anomaly z-score — the answer four separate calls would give. Use it when you have one specific key and want a verdict rather than raw series. Do NOT use it to survey many keys (anansi_movers) or to fetch a full series (anansi_data_history). Inputs: dataset, key and a numeric field, all exactly as named in the dataset.

輸入參數key必填field必填dataset必填payment_header

版本歷史

1
  1. v1.1.0最新Sep 16, 2026

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