Adding Rememberizer AI to Community Servers

[ea-open-source]

Description

A Model Context Protocol server for interacting with Rememberizer's document and knowledge management API. This server enables Large Language Models to search, retrieve, and manage documents and integrations through Rememberizer.

Server Details

• Server: mcp-server-rememberizer
• Tools:

1. rememberizer_search

   • Search for documents by semantic similarity
   • Input:
     • q (string): Up to a 400-word sentence to find semantically similar chunks of knowledge
     • n (integer, optional): Number of similar documents to return (default: 5)
     • from (string, optional): Start date in ISO 8601 format with timezone (e.g., 2023-01-01T00:00:00Z). Use this to filter results from a specific date (default: None)
     • to (string, optional): End date in ISO 8601 format with timezone (e.g., 2024-01-01T00:00:00Z). Use this to filter results until a specific date (default: None)
   • Returns: Search results as text output

2. rememberizer_agentic_search

   • Search for documents by semantic similarity with LLM Agents augmentation
   • Input:
     • query (string): Up to a 400-word sentence to find semantically similar chunks of knowledge. This query can be augmented by our LLM Agents for better results.
     • n_chunks (integer, optional): Number of similar documents to return (default: 5)
     • user_context (string, optional): The additional context for the query. You might need to summarize the conversation up to this point for better context-awared results (default: None)
     • from (string, optional): Start date in ISO 8601 format with timezone (e.g., 2023-01-01T00:00:00Z). Use this to filter results from a specific date (default: None)
     • to (string, optional): End date in ISO 8601 format with timezone (e.g., 2024-01-01T00:00:00Z). Use this to filter results until a specific date (default: None)
   • Returns: Search results as text output

3. rememberizer_list_integrations

   • List available data source integrations
   • Input: None required
   • Returns: List of available integrations

4. rememberizer_account_information

   • Get account information
   • Input: None required
   • Returns: Account information details

5. rememberizer_list_documents

   • Retrieves a paginated list of all documents
   • Input:
     • page (integer, optional): Page number for pagination, starts at 1 (default: 1)
     • page_size (integer, optional): Number of documents per page, range 1-1000 (default: 100)
   • Returns: List of documents

Motivation and Context

The integration of Rememberizer with the MCP server is designed to enhance document and knowledge management for LLMs. By connecting Rememberizer's robust semantic search and document retrieval capabilities with MCP, we aim to provide LLMs with seamless access to relevant information and context-aware interactions. This integration supports the development of intelligent applications that require real-time data access and dynamic user context incorporation, thus expanding the potential use cases and effectiveness of LLMs within the MCP ecosystem.

[devin-ai-integration]

Package review completed:

Package formatting: Properly formatted in package-list.json
Publication status: Successfully published to PyPI (version 0.1.1)
Environment variables: REMEMBERIZER_API_TOKEN properly documented in helpers/index.ts
Required metadata fields: All present (name, description, vendor, sourceUrl, homepage, license, runtime)

The package meets all requirements and is ready for integration.