"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 an AI agent to reverse-engineer a compiled program with Ghidra — inspecting decompiled code, instructions, callers, memory bytes, and strings — without any source code. Every reply is labelled as fact, computed, or guess, and each claim traces back to hash-checked saved evidence.

Enables teams to structure metric definitions, compare semantic differences, detect JOIN fanout and drift, reconcile mismatched numbers layer by layer, and persist metric contracts as YAML.

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.

Enables MCP-compatible AI assistants to inspect local application internals by analyzing JavaScript/Electron/ASAR and .NET artifacts, tracing dependencies, interfaces, and data flows, and preserving traceable evidence.

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.

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.

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.

Enables agents to reverse-engineer software across native binaries, managed code, Electron apps, websites, and live runtime behavior, returning findings linked to the evidence and limitations behind each conclusion. It also maintains a durable workspace where those behaviors can be inspected, questioned, compared against real execution, and exported as runnable handoffs.

Enables AI coding tools to load design guidelines, tokens, components, templates, and migration mappings before writing UI, then validates generated snippets locally and returns fixes so the tools can correct themselves before the code is shown.

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.

Analyzes JSX prop usage in React/TypeScript codebases using AST parsing, enabling prop usage tracking, component analysis, and TypeScript support.

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.

Lets AI coding agents ask a Rails app about itself: 35 read-only tools answer from the schema, models, routes, controllers, views, jobs and conventions, over MCP or the command line, even when the app cannot boot.

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.

Provides LLMs with live introspective access to a running Emacs instance to evaluate Elisp and inspect the internal environment. It enables tools for reading documentation, browsing Info manuals, querying completions, and accessing source code for functions and variables.

Enables LLM agents to interact with the Lean theorem prover through the Language Server Protocol, providing tools for analyzing Lean projects, accessing diagnostics, goal states, documentation, and searching for theorems using both local and external search services.

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.

Provides structured, token-efficient code context for AI assistants by indexing codebases with AST-level analysis, supporting 17+ languages, enabling semantic search, call-site tracking, and PR attribution.

Enables tracking and querying the provenance of AI-generated code, showing which sources influenced each line of code.

A Python MCP server that decompiles Android APK/XAPK/JAR/AAR files and extracts HTTP APIs used by the app.

Enables offline Android package analysis by loading APK, DEX, JAR, class, and XAPK files to inspect app metadata, manifests, classes, methods, resources, strings, and cross-references, and to export decompiled Java sources through CRUDE MCP-AQL tools.

MCP server for comprehensive PyPI package intelligence, providing tools for dependency analysis, security scanning, health scoring, license compliance, and trend tracking.

Intelligent code search MCP server with AST analysis, call graphs, dependency tracking, and semantic embeddings for developers.

Enables AI coding agents to interact with Gel databases by executing queries, testing changes in transactions, and accessing code examples and rules for advanced workflows.

Enables Claude to audit AI/ML projects for production readiness through deterministic static analysis, check ML system descriptions, compare model configurations, and generate readiness reports or deployment checklists. It returns scored, evidence-based findings with explicit unknowns while never executing submitted code or accessing the network.

A Model Context Protocol server for local repository indexing and code understanding, enabling semantic search, AST analysis, dependency tracking, and symbol resolution.

Enables Claude to analyze GitHub repositories with tools for health scoring, contributor analysis, issue tracking, code search, and more.

Enables MCP-compatible clients to inspect Next.js codebases, analyze App Router and Pages Router structure, discover API routes, and audit build performance through controlled tools.