"Accessing Hevy workout tracking app data" matching MCP servers:

MCP server for the hy-app component library, enabling AI editors to search components, retrieve APIs and examples, check platform support, access hooks and tools, and validate component usage.

A Model Context Protocol server that extracts embedded data (such as i18n translations or key/value configurations) from TypeScript/JavaScript source code into structured JSON configuration files.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

Enables powerful code search across millions of public GitHub repositories using grep.app API, with capabilities to discover code examples, retrieve specific files, and batch fetch multiple files for learning from open source projects.

This MCP server bundles a filtered copy of the Mern-Chat-App repository and exposes tools to list, read, and search files, as well as retrieve architecture overviews and file summaries.

Provides AI coding assistants with persistent memory, AST-based project indexing, and tools for live context management and hallucination detection. It enables saving lessons to local files, trimming conversation history, and verifying code symbols to prevent errors.

Provides AI agents with a hardened code security scanner via scan_code and scan_path, detecting leaked secrets and insecure patterns while enforcing strict path confinement and resource bounds.

Deterministic code-churn measurement and AI-agent detection from the CodeDelta engine: measure churn between two directories or git refs, scan for code that runs AI agents, and get AI-authorship pointers. Runs fully locally.

Enables AI assistants to diagnose and fix iOS app issues that cause App Store rejections, such as missing privacy manifests, permission strings, and test credentials, before submission.

An MCP server that reverse-engineers a rebuild spec (CLAUDE.md, config, and mutation-tested tests) from an existing app, enabling coding agents to rebuild the app cleanly against that spec.

Provides AI assistants with complete visibility into AL dependencies and symbols by exposing compiled .app files through the Model Context Protocol, enabling smart object discovery, deep code analysis, and reference tracking for Business Central development.

Enables analysis of GitHub Pull Requests, including details, diff, file lists, and review tracking.

Provides AI assistants with direct access to Laravel documentation, coding rules, and implementation templates stored locally. It enables searching documentation, retrieving design system guides, and accessing domain-specific code examples to streamline Laravel development.

Visualize anything from Claude Code as a mermaid diagram in a locally hosted web app.

Enables scanning code for deprecated annotations and comments, building a deprecation catalog, and tracking deprecated endpoints and features.

Enables AI assistants to work with Chapel code, including compiling, linting, and accessing educational primers.

MCP server that gives AI assistants impact analysis, cross-project reference tracking, and code health scoring.

Enables asking a consuming app in plain language to audit its design system usage, returning tables that sort every value and component by origin—design system, app-named, or none—along with token and contrast checks.

A comprehensive MCP server that provides a graph database for tracking software codebase components, their relationships, and associated tasks/goals.

A local, zero-dependency MCP server that cuts coding-agent round-trips by aggregating search, read, edit, exec, git, and savings tools into single, validated calls, with cost tracking and no data leaving the machine.