mcpField
カ
カオナビMCPサーバー

カオナビMCPサーバー

Local
github.com/otokawa-kBusiness & Marketing0Updated 70w ago
Repository

About カオナビMCPサーバー

カオナビMCPサーバー connects MCP clients to the Kaonavi API for member and sheet data. Its tools retrieve records and describe available fields.

Key features

  1. 01Retrieve registered members
  2. 02Retrieve registered sheets
  3. 03Describe member fields
  4. 04Describe sheet fields
  5. 05List configured sheet IDs

Use cases

  1. 01Retrieve member records from Kaonavi
  2. 02Inspect fields available in member and sheet APIs

MCP support

It exposes Kaonavi API operations as MCP tools for retrieving member and sheet data and describing fields.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Kaonavi MCP Server

Overview

Provides an MCP server that utilizes the Kaonavi API.

Tools

  1. DescribeMemberFields
    • Retrieves field information from the Members API.
  2. DescribeSheetFields
    • Retrieves field information from the Sheets API.
  3. GetMembers
    • Retrieves a list of members registered in Kaonavi.
  4. GetSheets
    • Retrieves sheet information registered in Kaonavi.
  5. GetSheetIds
    • Retrieves a list of sheet IDs set in sheets_config.json.

Installation

  1. Download kaonavi_api_executor-0.3.0-py3-none-any.whl and install it with the following command:
uv pip install kaonavi_api_executor-0.3.0-py3-none-any.whl

Debug

  1. Install Node.js.
  2. Run the following command to start the MCP server:
npx @modelcontextprotocol/inspector

License

This project is licensed under the MIT License.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.