Mochow MCP Server Python

Provides direct access to Mochow vector database capabilities for managing databases, tables, and performing vector similarity and full-text searches with filtering options.

Skills

Explore the skills and capabilities of this skillset.

list_tables

List all tables in the current database. Returns: str: A string containing the names of all tables.

stats_table

Get the table statistics in the Mochow instance. Args: table_name (str): Name of the table to get statistics. Returns: str: A string containing the table statistics.

use_database

Switch to a different database. Args: database_name (str): Name of the database to use. Returns: str: A message indicating the success of the database switch.

vector_search

Perform vector similarity search combining vector similarity and scalar attribute filtering in the Mochow instance. Args: table_name (str): Name of the table to search. vector (list[float]): Search vector. vector_field (str): Target field containing vectors to search. Defaults to "vector". limit (int): Maximum number of results. Defaults to 10. output_fields (Optional[list[str]]): Fields to return in the results. Defaults to None. filter_expr (Optional[str]): Filter expression for scalar attributes. Defaults to None. params: Additional vector search parameters Returns: str: A string containing the vector search results.

describe_index

Describe index details in the Mochow instance. Args: table_name (str): Name of the table. index_name (str): Name of the index to describe. Returns: str: A string containing the details of the index.

describe_table

Describe table details in the Mochow instance. Args: table_name (str): Name of the table to describe. Returns: str: A string containing the details of the table.

list_databases

List all databases in the Mochow instance. Returns: str: A string containing the names of all databases.

create_database

Create a database in the Mochow instance. Args: database_name (str): Name of the database. Returns: str: A message indicating the success of database creation.

fulltext_search

Perform full text search combining BM25 similarity and scalar attribute filtering in the Mochow instance. Args: table_name (str): Name of the table to search. index_name (str): Name of the inverted index to perform full text search. search_text (str): Text to search. limit (int): Maximum number of results. Defaults to 10. output_fields (Optional[list[str]]): Fields to return in the results. Defaults to None. Returns: str: A string containing the full text search results.

delete_table_rows

Delete rows with a filter expression in the Mochow instance. Args: table_name (str): Name of the table. filter_expr (str): Filter expression to select data to delete. Returns: str: A message indicating the success of data deletion.

drop_vector_index

Drop the vector index in the Mochow instance. Args: table_name (str): Name of the table. index_name (str): Name of the vector index to drop. Returns: str: A message indicating the success of index drop.

select_table_rows

Select rows with a filter expression in the Mochow instance. Args: table_name (str): Name of the table. filter_expr (str): Filter expression to select data. Defaults to None. limit (int): Maximum number of results. Defaults to 10. output_fields (Optional[list[str]]): Fields to return in the results. Defaults to None. Returns: str: A string containing the selected rows.

create_vector_index

Create a vector index on a vector type field in the Mochow instance. Args: table_name (str): Name of the table. index_name (str): Name of the index. field_name (str): Name of the vector field. index_type (str): Type of vector index. Supported values are "HNSW", "HNSWPQ", "HNSWSQ". metric_type (str): Distance metric. Supported values are "L2", "COSINE", "IP". params (Optional[dict[str, Any]]): Additional vector index parameters. Returns: str: A message indicating the success of index creation.

rebuild_vector_index

Rebuild the vector index in the Mochow instance. Args: table_name (str): Name of the table. index_name (str): Name of the vector index to rebuild. Returns: str: A message indicating the success of index rebuild initiation.

Configuration

Customize the skillset to fit your needs.
MCP Server

Connect to MCP Server

Mochow MCP Server Python

Google 分析師
逐步指南,教您如何將 Google Analytics 4 (GA4) 屬性連接到 Google 分析師代理。涵蓋創建 Google Cloud 服務帳戶、啟用 Analytics Data API、授予 GA4 查看者訪問權限,以及配置代理以支持會話、用戶、跳出率、轉換等指標。非常適合快速在 Bika.ai 中設置 GA4 數據報告。
Github issues 助手
Github Issues 助手是一個 AI 智能體,用於簡化 GitHub issues的管理。它可以直接在存儲庫中簡化創建、跟踪和優先處理錯誤、任務或功能請求的過程。非常適合團隊使用,確保一致的格式,自動化重複步驟,並與開發管道集成。
AI 寫作助手
告訴我有關 AI 產品或品牌的信息 - 我將撰寫吸引人的營銷文案、文章和社交媒體帖子,根據您的品牌聲音和產品細節量身定制,並附上相關鏈接和插圖。
需求文檔撰寫助手
告訴我您的產品或功能想法 - 我將幫助您創建全面且詳細的需求文檔,涵蓋用戶故事、驗收標準、技術規範等內容。
工單管理員
收集、分析和管理來自表單和數據庫的支持工單,幫助您高效地跟踪、優先處理和回應。
Email 营销助手
自動尋找潛在客戶並發送為期3天的跟進郵件序列。
社區活動分析員
分析社區活動截圖,報告參與趨勢和討論亮點。上傳社區互動的截圖,該 Agent 會生成一份清晰的markdown報告,總結參與水平、關鍵討論主題和顯著亮點 — 非常適合社區經理、行銷人員和產品團隊。
Discourse 社區管理員
Discourse 社區管理員助手幫助您快速生成清晰、友好且結構良好的用戶回覆,使社區管理變得更輕鬆和專業。
AI 網頁工程師
AI Programmer 是一個 AI 頁面,可以將您的原始發布說明轉換為時尚、可發布的 HTML 頁面。

Frequently Asked Questions

一句話快速介紹:什麼是Bika.ai?
是什麽让 Bika.ai 如此独特?
"BIKA" 這個縮寫單詞代表什麼意思?
Bika.ai是怎麼做到AI自動化做事的?
Bika.ai是免費使用的嗎?
Bika.ai與ChatGPT、Gemini等AI助手有什麼區別?
Bika.ai與多維表格有什麼區別?
Bika.ai 在單表數據量、關聯引用變多後,如幾萬行、幾十萬行,會卡住嗎?
Bika.ai中的"空間站"是什麼?
付款後我擁有多少個付費空間?
什麼是"資源"?
Bika.ai 的團隊是如何「吃自己的狗糧」的?
Bika.ai如何幫助提高工作效率?
Bika.ai 的AI自動化功能有哪些特點?
Bika.ai 中的自動化模板是什麼?
Bika.ai 是否支持團隊協作及權限功能?

Embark on Your AI Automation

Mochow MCP Server Python | Bika.ai