"Vector Database" matching MCP servers:

LinkedIn prospect intelligence inside Claude. It automates the research behind outreach, not the sending: scrapes posts, profiles and comment threads into a local SQLite database, classifies what a person or company actually posts about, mines comment threads for people already describing your problem, and surfaces uncommon commonalities worth opening with.

MCP server that connects to a sales CRM database, enabling AI agents to search prospects, view pipeline status, manage follow-ups, and update records using natural language commands.

Provides AI assistants with access to sales data from a Northwind-style database through GraphQL queries, enabling natural language requests for orders, customers, products, and analytics.

Enables users to query enterprise sales data from a local SQLite database and investigate anomalies through an inline dashboard with human-in-the-loop review buttons.

Enables AI assistants to search and retrieve B2B leads and company data from the LeadBrew database, including contact details and company information.

Provides read-only, MCP-based access to due follow-ups stored in a Postgres view, letting an assistant list overdue follow-ups with days overdue and count them per channel without any write path to the database.

MCP server for Apollo.io B2B database enabling people and company search, enrichment, and insights through conversational interfaces.

Enables AI assistants to securely interact with the Little Green Light CRM database for tasks like searching constituents, logging gifts, managing groups, and generating reports without third-party middleware.

Gives MCP clients such as Claude, ChatGPT and Cursor access to a recruitment firm's database — candidates, contacts, companies, jobs, pipelines, notes and outreach — so assistants can search, match, draft and update records. Every request runs under the signed-in user's own account, workspace and role permissions, with sensitive actions confirmed as a preview first.

A production-style MCP server exposing six business tools (web search, database, CRM, email, calendar, analytics) to AI clients via a secure, authenticated interface, with mock mode and a reference agent client.

Connects a podcast-sponsorship database to AI assistants, allowing users to search for brands that sponsor podcasts and reveal buyer contacts by name and email.

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 AI assistants to securely triage SuperOffice CRM support cases by retrieving tickets, running database diagnostics, searching knowledge bases, and orchestrating cross-system incident investigations.

Read-only MCP server for querying the lead database and pipeline statistics.

Secure local MCP server for SendPilot Lead Database searches and workspace read operations. Exposes tools for creating searches, checking status/results, credits, and campaigns without sending messages or modifying campaigns.

Provides a customer database with MCP for local agents, featuring a complete admin panel with dashboard, AI chat, terminal, plugins, skills, PWA, and installer.

Enables secure, natural language access to private Cloud SQL PostgreSQL data for catering CRM management, exposing database CRUD operations as MCP tools via Cloud Run.

Enables natural language queries, CRUD operations, and analytics on a PostgreSQL database (Neon) via Claude, supporting customers, tickets, products, and analytics.

Turns a recruitment database into an AI-queryable pipeline, enabling natural language queries to search contractors, build shortlists, draft outreach, and book engagements.

An open-source, self-hosted MCP server for Apollo.io that gives AI agents direct access to Apollo's B2B sales intelligence database.

AI-native, self-hostable private business card database for ChatGPT using Remote MCP with Firebase authentication and Cloudflare storage.

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 natural language management of clients and products via a chat interface, using Gemini LLM and MCP to interact with a SQLite database.

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 activity and conversations, and performing semantic search over stored interactions while using vector storage and caching for faster responses.

A local, privacy-first knowledge graph for Salesforce orgs. It live-syncs your org to a SQLite + vector index on your machine and exposes 26 MCP tools to Cursor, Claude Code/Desktop, and VS Code, so the AI you already use can reason about Apex, LWC, Flow, Vlocity, OmniStudio, security, and integrations without your code or schema ever leaving your laptop.

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, including creating contacts and companies, retrieving recent activities and conversations, and performing semantic search over previously retrieved data. It uses built-in vector storage and caching to work around API limitations and improve response times.