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

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.

Verifies AI agent completion claims by checking that claimed output files exist and are fresh, acting as a lightweight physical telemetry gate.

Enables agent harnesses to route bounded choices and record execution receipts through MCP, with replay, supervision, context filtering, and fail-open behavior while the host retains permissions and execution.

An MCP server that helps AI coordinate sequential tool calls and maintain a comprehensive journal of execution workflows, decisions, and actions.

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 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.

Semantic React/TSX analysis MCP server that saves 70-90% tokens by using AST to retrieve precise component usages, prop flow, and state update information for AI coding assistants.

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.

Embedding space analysis toolkit that measures semantic drift, traces trajectory dynamics, and projects passages onto caller-defined semantic axes, exposed as MCP tools for agentic integration.

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.

Provides cryptographic governance receipts for AI agents, enabling pre-execution evaluation and signed verdicts (EXECUTE/BLOCK/REVIEW/SHADOW) with offline-verifiable audit trails.

A queue-based execution server that monitors a command queue and executes shell commands or Python scripts, moving completed or failed jobs to respective directories.

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.

Enables local, deterministic semantic operations on OWL/RDF ontologies, including Description Logic reasoning, SPARQL querying, TBox/ABox mutation, and interactive graph visualization via an MCP interface.

A local-first, Markdown-native AI agent layered memory system that provides MCP tools for storing, recalling, exporting, and importing memories with types like working, persona, and fact.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

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.

A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.