"Understanding the concept of dependency" matching MCP servers:

MCP server that audits npm dependencies against the live registry, providing per-dependency reports on versions behind, deprecation, and license.

An MCP server that performs comprehensive health checks on project dependencies for JavaScript and Python projects, detecting outdated packages and fetching changelogs.

Enables scanning repositories for hardcoded service URLs and clients, and building service-to-service API dependency maps.

An MCP server that analyzes local or remote GitHub repositories, providing intelligent code context and structure to AI coding assistants.

Monitors and analyzes dependency health in Node.js projects, providing tools to check outdated packages, vulnerabilities, licenses, and suggest safe upgrades.

Read-only npm dependency diagnostics: inventory, audit and health checks from package-lock.json, no account needed.

A hosted remote MCP server that provides tools for inspecting A2A dependencies, extracting message edges, detecting dependency cycles, issuing rollout receipts, and exporting dependency logs.

Provides structured migration briefs for LLM coding agents covering breaking changes, codemod coverage, and hallucination warnings for major version bumps of packages like tailwindcss, next, and react.

Automatically extracts technical concepts from AI coding conversations, organizes them into a searchable knowledge base with hierarchy and categories, and links them to specific locations in your codebase.

Enables checking project files like Dockerfile, .nvmrc, and CI workflows to determine whether pinned runtime, database, and OS versions are still supported, providing end-of-life dates, latest patches, and upgrade targets for 470+ products.

Enables AI assistants and developers to analyze code for language-specific best practices and idiomatic patterns across programming languages, CI automation, and configuration formats.

Enables coding agents to analyze monorepo dependency graphs, detect and resolve circular dependencies, and plan a topological build order via Tarjan SCC and Kahn algorithms. It exposes deterministic, zero-dependency Python AST static analysis over MCP for auditing and refactoring codebases.

Enables checking whether software packages are deprecated, archived, stale, active, or unknown with supporting evidence, auditing dependency manifests, and looking up runtime support and end-of-life dates.

Enables deterministic, AST-based rewriting of Python imports and callsites, so module paths can be re-aliased and migrated across a codebase without regex errors. It also supports dependency graph auditing and refactoring planning with token-efficient output for coding agents.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

Enables analysis of multi-file git workspaces to synthesize dependency diffs and refactor sequences.

Enables coding agents to parse multi-file AST dependency graphs and extract minimum context symbol call-trees through MCP.

Enables coding agents and MCP-compatible clients to parse multi-file AST dependency graphs and extract minimum context through symbol call-tree slicing. It provides deterministic, zero-dependency execution over JSON-RPC stdio.

Enables coding agents to analyze Python source with AST-based static analysis, detect circular dependencies across monorepo packages via Tarjan SCC, and produce a topological build order with Kahn's algorithm along with refactoring plans and graph metrics. Runs with zero external dependencies over MCP from Claude Desktop, Cursor, or any compliant client.

Enables developers and coding agents to refactor Python codebases by re-aliasing imports and migrating dependencies, safely rewriting module paths and callsites through deterministic AST analysis instead of error-prone regex. It also surfaces dependency graphs and refactoring plans that minimize LLM token usage.