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

An MCP server providing SQLite database access for AI agents, enabling SQL execution, schema inspection, CRUD operations, and data export.

A general-purpose MCP server that lets AI work with multiple databases within clear boundaries.

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.

Analyzes SQL queries for performance issues, provides optimization suggestions with automated rewriting, and recommends indexes across multiple database dialects (PostgreSQL, MySQL, Oracle, SQL Server).

Exposes a metrics query API to AI assistants, allowing them to issue queries and interact with the server's tools such as ping.

Exposes a governed semantic layer to AI agents so they select metrics and dimensions from a closed, engineer-declared vocabulary instead of writing SQL. Validates every typed call against the contract, rejects out-of-contract requests with structured errors and suggestions, and returns query rows together with the compiled SQL and dbt lineage for auditing.

Enables agents to interact with a governed semantic layer for querying and authoring metrics, providing tools for discovery, planning, validation, and execution of analytics queries.

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.

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.

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.

A local MCP server for secure database querying from Claude Code, with Apple Keychain credential storage, permission-based access control, and audit logging.

MCP server for querying and analyzing TensorBoard event files, enabling AI agents to extract scalar data, statistics, and correlations without a running TensorBoard server.

MCP-native persistent memory for AI agents. Stores and retrieves encrypted memories with Trust Quotient scoring, cross-agent handover protocol, and immutable audit trail. 13 tools. Remote endpoint available.

Provides a semantic intelligence layer for SQL Server databases, enabling AI agents to understand schema structure, relationships, and safely interact through policy-gated tools.

Lets AI agents retrieve source-linked passages from a local folder of Markdown notes or an Obsidian vault, optionally following the explicit links between notes, and can also fetch passages from pinned public GitHub files you configure. It returns bounded evidence packets with original text, file paths and hashes so agents can answer from your material rather than generating content themselves.

MCP server that verifies restaurant booking outcomes by reconciling API claims against independent re-reads, with a recovery dial for stakes-based retry policies.

A semantic router that enables discovery, management, and execution of tools across multiple MCP servers using natural language.

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.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

Enables MCP clients to serve and query semantic models, providing tools for entity descriptions, metric lookups, context resolution, and operation validation for AI agents.