"Semantic search, RAG, and memory systems" matching MCP servers:

Enables LLMs to search and retrieve Autotask tickets, companies, and contacts using advanced semantic search and AI-powered reranking.

Turns Google Calendar history into a searchable people CRM, enabling queries about meeting patterns, relationships, and contacts through natural language with any MCP client.

Verified, long-tail company search: describe what you want, get an LLM-verified company shortlist.

Provides interactive tools to explore AFAS customer data and employee leave information, including bubble charts, leave balance reports, and detailed leave cards.

Enables searching for volunteer opportunities via MCP protocol, REST API, and includes a custom Apex MCP client for Salesforce Agentforce integration, all powered by the free VolunteerConnector API.

Enables interaction with IntelliMatch recruiting and iSuite insurance systems through spec-driven MCP tools, allowing natural language queries for candidate search, policy management, claims, and workflows.

A unified MCP server that integrates Zoho CRM and Zoho Books, enabling AI assistants to manage CRM records, Books customers/invoices, synchronize data, and search across both systems.

Enables interaction with Odoo ERP systems for product, customer, order, invoice, and payment management using MCP tools.

Enables AI assistants to interact with Redmine project management systems through MCP tools, providing access to issues, projects, time tracking, wiki, and more.

Enables AI assistants to interact with HubSpot CRM data, providing tools for managing contacts and companies, retrieving activity and conversations, and performing semantic search with vector storage and caching for improved performance.

Enables semantic search of Salesforce case summaries using embeddings, accessible via MCP tools and REST API.

Integrates with Odoo ERP systems, enabling AI assistants to interact with Odoo data and functionality through the Model Context Protocol.

Enables AI assistants to directly access and manage HubSpot CRM data, including contacts, companies, and conversations, with semantic search and caching for improved performance.

Enables AI assistants to interact with HubSpot CRM data by creating contacts and companies, retrieving activity and recent conversations, and performing semantic search across previously retrieved records with built-in caching and vector storage.

Enables AI assistants to access and manage HubSpot CRM data, including contacts, companies, activity, conversations, and semantic search over cached interactions while improving response times via built-in vector storage and caching.

Enables AI assistants to interact with HubSpot CRM data, including contacts, companies, and engagement information, with semantic search over previously retrieved data and built-in caching/vector storage to improve response times.

Enables AI assistants to interact with HubSpot CRM data by creating and retrieving contacts, companies, and recent activity, while providing semantic search over stored conversations with vector storage and caching.

Enables AI assistants to interact with HubSpot CRM data, including creating contacts and companies, retrieving active records and recent conversations, and performing semantic search over previously retrieved HubSpot data through built-in vector storage and caching.

Enables AI assistants to interact with HubSpot CRM data through tools for contacts, companies, activity, conversations, and semantic search, with vector storage and caching to improve response times.

MCP server that provides mock APIs and deterministic seed data for ERP/OMS, WMS, and CRM systems, enabling supply chain data exploration and integration testing.

A demo MCP server with two governed systems (pipeline semantic layer and predictive model) for querying and scoring professional services opportunities with cross-system governance.

Enables AI assistants to interact with HubSpot CRM data for contacts, companies, activity, conversations, and semantic search, with built-in vector storage and caching to overcome API limitations and improve response times.

Enables AI assistants to interact with HubSpot CRM data, including contacts, companies, and engagement data. It provides built-in vector storage and caching to support semantic search and faster responses.

Enables AI assistants to interact with HubSpot CRM data, including contacts, companies, and engagement activities, with built-in vector storage and caching for semantic search and improved response times.

Enables AI assistants to interact with HubSpot CRM data, including creating and searching contacts and companies, retrieving activity and conversations, and using vector storage for semantic search across prior interactions.

Enables AI assistants to interact with HubSpot CRM data by accessing contacts, companies, activity, and conversations, while providing vector-backed semantic search and caching for faster, more reliable workflows.

Enables AI assistants to interact with HubSpot CRM data, including contacts, companies, and engagement information, with built-in vector storage and caching for semantic search and faster responses.

Enables AI assistants to interact with HubSpot CRM data by creating and retrieving contacts, companies, activity, and conversations, while using vector storage and caching to improve semantic search and response times.

Enables AI assistants to access and manage CRM data by creating contacts and companies, retrieving activities and conversations, and performing semantic search over previously retrieved information with built-in vector storage and caching.

Enables AI assistants to interact with CRM contacts, companies, activity, and conversations, and to perform semantic search across previously retrieved data.