Database
| Server | Language | Stars | Downloads |
|---|---|---|---|
| memora Give your AI agents persistent memory. | Python | 726 | - |
| Memory SQLite-backed persistent memory server with FTS5 full-text search, entity relationship tracking, and project scoping for AI agents. | — | 3 | - |
| Memory Engine MCP Local-first graph memory for AI assistants with SQLite, semantic search, and MCP tools. | — | - | - |
| Memory Kernel Local-first memory for AI agents: SQLite FTS5, deterministic recall, no vector DB, no cloud. | Python | - | - |
| memory-mcp-server A Model Context Protocol server that provides knowledge graph management capabilities. | Swift | 106 | - |
| Memory Service (Rust) High-performance Rust drop-in replacement for mcp-memory-service with SQLite storage and ONNX embeddings. | — | 2 | - |
| MemWhale Persistent, local memory for developers and their coding agents. Records commands, output, errors, and the fixes that worked into SQLite and serves them over MCP. | Rust | 120 | - |
| Mengi Cloud Manage Kubernetes clusters, deployments, databases, secrets and observability on Mengi Cloud. | — | - | - |
| menjometre Menjometre is an independent observatory of Catalan public spending. This MCP server gives any LLM client read-only access to the full dataset and the statistical scoring layer built on top of it. ## Coverage - **~19.5 M** grant records from the Registre d'Ajuts i Subvencions de Catalunya (RAISC), 2016–present - **~1.7 M** public procurement contracts from Dades Obertes de Catalunya - **~496 K** beneficiary entities with normalised identities - A **public-figure graph** linking Parlament deputies, Generalitat officials, and the boards of publicly-funded entities - The **Menjometre score** — a statistical indicator flagging concentration and recurrence patterns in public spending ## What the 48 tools cover | Domain | What you can ask | |---|---| | `entities` | Search, profile, network, activity timeline for any funded organisation | | `grants` | RAISC lookups, concentration analysis, purpose trees, year-over-year trends | | `contractes` | Procurement lookups, sole-source detection, fragmentation patterns | | `organs` | Granting-body rankings and profiles | | `xarxa` | Public-figure graph, shortest path between two people, cluster investigation | | `pressupostos` | Generalitat budget surface | | `meta` | Methodology, scoring explanation, upstream attribution manifest | ## Example prompts - *"Which ten Catalan entities received the most RAISC funds in 2024?"* - *"Show me the concentration profile of the entity with CIF A08000143."* - *"Which grant-giving bodies award the largest share of their budget to a single beneficiary?"* - *"What percentage of Ajuntament de Girona's contracts are sole-source?"* - *"Explain how the Menjometre score is computed."* ## Response envelope Every tool response ships with four things: 1. **Attribution** — a `sources` array citing the upstream dataset, licence (CC-BY-4.0 / Llei 37/2007), and publisher 2. **Scope predicate** — personal-data filters enforced at the database query, not client-side 3. **Framing** — scored responses embed a `score_meta` block with a `not_an_accusation` disclaimer and a link to the methodology page 4. **Neutral statistical language** — the server returns "top decile", "around the median", never accusatory framing Safe for journalism, academic research, and civic-tech tooling. ## Install via npm ```json { "mcpServers": { "menjometre": { "command": "npx", "args": ["-y", "menjometre-mcp"] } } } Also reachable directly over Streamable HTTP at https://www.menjometre.cat/mcp. | — | - | - |
| mentedb-mcp MCP server for MenteDB, persistent memory for AI agents | TypeScript | - | - |
| mentor-mcp-server A Model Context Protocol server providing LLM Agents a second opinion via AI-powered Deepseek-Reasoning R1 mentorship capabilities, including code review, design critique, writing feedback, and idea brainstorming through the Deepseek API. | TypeScript | 31 | - |
| metabase-mcp Metabase MCP server provides integration with the Metabase API, enabling LLM with MCP capabilites to directly interact with your analytics data, this server acts as a bridge between your analytics platform and conversational AI. | Python | 73 | - |
| metabrain MCP server giving AI agents persistent SQLite memory that proves which lessons hold. | Python | - | - |
| MetaEngine Spec-first code generation: OpenAPI, GraphQL, Protobuf, and SQL to typed clients in 11 languages. | TypeScript | - | - |
| metaharness 🛠️ The meta-harness for AI agents — scaffold your own focused, branded agent harness with its own npx CLI, MCP server, memory, learning loop, and witness-signed releases. Works with Claude Code, Codex, pi.dev, Hermes, OpenClaw, and RVM (hardware-isolated sandbox). | TypeScript | 668 | - |
| m_flow A bio-inspired cognitive memory engine — a new paradigm for Graph RAG. | Python | 4.5k | - |
| Michelin Guide Search 18,900+ Michelin Guide restaurants worldwide with multi-criteria filtering and SQLite FTS5. | — | 3 | - |
| Microsoft Access Database Read and edit Microsoft Access databases (.accdb/.mdb) - forms, VBA, tables, queries. Windows. | Python | - | - |
| @microsoft/postgres-mcp PostgreSQL MCP server for AI assistants over the Model Context Protocol. | TypeScript | - | - |
| Microsoft SQL Server Read-only MCP for Microsoft SQL Server: metadata discovery, parameterized SELECT, plans. | — | - | - |
| Microsoft SQL Server (MSSQL) Securely integrates with Microsoft SQL Server databases for data analysis, reporting, and management. | Python | 392 | - |
| Microsoft To Do Self-hosted Microsoft To Do with SQLite workaround for personal account list limitations. | — | 5 | - |
| Midplane Safe-by-default SQL guardrails for AI agents: AST-checked queries, per-table policy, audit log. | TypeScript | - | - |
| Mi Fitness Data Bridge Local-first bridge: export your own Mi Fitness (Xiaomi) health data to SQLite/JSON/CSV and MCP. | — | - | - |
| mig2schema Extracts database schema from PostgreSQL migration files by running them in temporary containers and outputting human-readable schema information or SQL DDL, enabling developers to understand final database structure without manually tracking changes across multiple migration files. | — | 2 | - |