"semantic-release" matching MCP servers:

Enables natural language search of local photo archives using AI-powered semantic understanding, with integration into Claude Desktop via the Model Context Protocol.

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.

Enables a coding agent to answer "is this iOS release ready?" by running read-only App Store Connect checks: build processing, App Review state, a 13-point submission preflight, release-notes policy linting, and App Store Server Notification reconciliation. The lone write capability is a dry-run-first, seven-gate submission tool that always requires explicit confirmation.

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.

Query Apple Health export data with an LLM via a semantic layer that avoids common data traps, enabling accurate natural-language queries about health metrics. Optionally imports CPAP therapy data for correlation.

A local stdio MCP server that exposes compact semantic observations from UI elements annotated with data-agent-* hints, designed to work alongside the official Playwright MCP for browser automation.

A read-only Model Context Protocol server that converts local Git changes and JUnit XML reports into traceable release evidence, risk findings, and a rollback checklist.

Local semantic recall MCP server for citation-integrity workflows. Enables semantic search over a text corpus, returning file paths and page numbers for verification, with 100% local processing using ONNX embeddings and LanceDB.

Enables natural-language semantic search of a project's code through a local vector index, so questions that plain grep cannot match return ranked file:line references. It also indexes or refreshes projects on demand, reports index status, and optionally flags near-identical code across files.

Generates multi-platform release announcements (X, Reddit, GitHub profile, awesome-list, MCP directory) and a publish-readiness checklist for any public GitHub repository.

An MCP server that drives the full App Store release cycle for iOS and macOS apps: version bump, archive + TestFlight upload, metadata, review submission, and status.

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 semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

Enables semantic search over local markdown specs, RFCs, and docs using Ollama and LanceDB, allowing AI agents to find relevant information by meaning rather than exact keywords.

Drives a real Chromium browser to extract a page's DOM into compact Semantic JSON with Playwright-native locators, enabling AI agents to generate consistent, accurate Playwright tests.

Validates release hygiene in local repositories with network-free, read-only tools for checking repo structure, version alignment, and generating release checklists.

Enables AI assistants to perform Git workflows with semantic commit messages, pre-PR secret audits, conflict resolution, and release changelog generation.

Provides direct access to 200M+ academic papers from Semantic Scholar, enabling paper search with advanced filters, author discovery, citation analysis, and AI-powered paper recommendations through natural language.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.