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"Semantic layer for SQL query execution with dimensions and measures" matching MCP servers:

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    An MCP server providing SQLite database access for AI agents, enabling SQL execution, schema inspection, CRUD operations, and data export.
    Last updated
    MIT
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    MCP server for the OrionBelt Semantic Layer. Enables LLMs to explore governed semantic models, compile YAML metric definitions into optimized SQL across 8 engines (BigQuery, ClickHouse, Databricks, Dremio, DuckDB, MySQL, PostgreSQL, Snowflake), and execute analytics queries via natural language. Works with Claude, Cursor, Windsurf, and Copilot.
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    3
    Apache 2.0
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    MCP server for answering questions about official statistics from a single publication URL, with tools to check query scope, retrieve overview, text, data files, and search.
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    5
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    Enables AI agents to read and optionally write to PostgreSQL databases through a multi-project setup with schema inspection, query execution, and performance analysis tools.
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    5
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    A local MCP server for secure database querying from Claude Code, with Apple Keychain credential storage, permission-based access control, and audit logging.
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    5
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    A governed SQL gateway that exposes typed tools to AI agents, compiling safe read-only queries from a semantic layer while blocking PII before execution, supporting SQL Server, Postgres, and SQLite.
    Last updated
    9
    MIT
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    Provides a semantic intelligence layer for SQL Server databases, enabling AI agents to understand schema structure, relationships, and safely interact through policy-gated tools.
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    3
    MIT
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    A semantic router that enables discovery, management, and execution of tools across multiple MCP servers using natural language.
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    MIT
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    Security scanner and trust verification layer for MCP ecosystem, enabling users to scan PyPI packages and GitHub repos for secrets, dangerous patterns, and prompt injection vectors, and compare security scores.
    Last updated
    MIT
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    Enables MCP clients to serve and query semantic models, providing tools for entity descriptions, metric lookups, context resolution, and operation validation for AI agents.
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    MIT
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    ChromaDB-powered semantic memory server that enables semantic search across past conversations, health snapshots, and cross-silo topics for the nanobot platform.
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    Enables efficient AI agent operations through sandboxed Python code execution with progressive tool discovery, PII tokenization, and skills persistence, achieving up to 98.7% token reduction by processing data in a sandbox rather than in context.
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    Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.
    Last updated
    13
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    MIT