code2prompt and Context7 for AI coding context

Google AI Studio is my main development environment. Two tools give Gemini the context it needs in my AI-first workflow: code2prompt and Context7.

Precise, relevant context matters as much as the model. Both tools ground an LLM in real information instead of what it remembers from training.

In my project Turning the ai-sdlc CLI into an Electron GUI, code2prompt handled the planning phase. I converted the whole original ai-sdlc Python CLI repository into one Markdown file, and I did the same for the Electron desktop app that would host the new GUI. With both codebases in context, Gemini got grounded in them before we planned how to add ai-sdlc as a GUI module. Detailed, accurate context turns a general assistant into a specialized development partner.

code2prompt for codebase context

Use code2prompt when an LLM must understand, refactor or build on an existing codebase. That codebase can be your own project or a repository you cloned from GitHub. Feeding the model the whole raw project is usually inefficient, because LLMs get overwhelmed and lose focus.

code2prompt extracts, filters and formats the relevant code from any selected codebase into structured Markdown prompts. In the ai-sdlc project, it turned the public Python CLI repository into the specification that Gemini used to re-implement the tool as an Electron and Vue 3 GUI. I also built a Tkinter GUI helper for code2prompt to manage the process on larger applications, such as my Desktop AI Assistant.

code2prompt follows the KISS principle, Keep It Simple, Stupid, which I also apply to throwaway scripts. The aim is the least possible context with the best possible quality. The approach is deterministic, which gives you a controlled way to feed information to an LLM.

Use code2prompt for these tasks:

• Generating documentation and docstrings for any codebase.
• Finding and fixing bugs in an existing codebase.
• Refactoring modules or functions.
• Writing tests that fit a project's structure and logic.
• Learning a new technology by reading how an existing codebase uses it.
• Re-platforming or adding features to an application written by someone else, as in the ai-sdlc migration.

code2prompt helps with two common LLM problems.

• It reduces hallucinations. The model works from the repository's real code and is less likely to invent functions, arguments or variables.
• It lowers token costs and can improve performance. Filtering out irrelevant code shrinks the prompt, which saves money and often improves answers. The 15 tips for AI-first development in AI Studio cover the same goal of using the context window well.

code2prompt has three integration methods.

• CLI, for terminal use and quick, one-off context from any code directory.
• Python SDK, for custom scripts and automated workflows across codebases.
• MCP server, where AI agents request context from a repository as a tool.

It supports glob patterns (--include and --exclude) for precise file selection. Its core library also keeps state, so an agent can add or remove files as a task evolves.

Context7 for up-to-date library knowledge

Ask an LLM to build a feature with a third-party library such as Next.js, Supabase or a Python package, and it may return outdated code. Models trained up to a cutoff date often invent API calls for newer versions. Context7 supplies current, version-specific documentation taken from the original sources.

Its documentation comes from automated scraping and community contributions, and you can refresh it to keep it current. The docs are split into focused snippets with many examples. Examples are one of the most effective ways to help a model write reliable code for external tools. Context7 covers reportedly around 1,900 tools and frameworks.

Context7 is mainly an MCP server, built for AI coding assistants and agents. It exposes two tools:

• resolve-library-id turns a library name, such as Supabase, into a Context7-compatible ID. It takes a libraryName parameter.
• get-library-docs fetches documentation for a library ID. It performs retrieval-augmented generation (RAG) over the curated docs. It takes a context7CompatibleLibraryID parameter, plus an optional topic, such as "authentication", and an optional tokens limit that controls the context length.

Agents use these tools for targeted lookups. They pull current snippets into their answers, which cuts hallucinated library usage.

The core difference is the source material

The difference is where the context comes from. code2prompt reads any codebase you point it at, whether it is yours or a cloned repository. Context7 reads external library and framework documentation.

They solve different problems. code2prompt helps the LLM understand a specific codebase. Context7 helps it use external tools and libraries correctly.

Side-by-side comparison

MCP and agentic AI

Both tools are part of Software 3.0, where LLMs act as agents that invoke external tools and resources. The Model Context Protocol (MCP) standardizes that, so agents can request context when they need it. I covered this in How n8n ties together workflows, agents and MCP.

• code2prompt works with MCP through its server mode. Agents treat any specified codebase as an interactive tool, and its stateful context suits tasks that change as they run.
• Context7 is an MCP server at its core. Its tools let agents run RAG over its documentation.

With MCP, an agent can decide when to query code2prompt for a codebase and when to query Context7 for a library's latest usage. That makes its problem solving more dynamic.

Choosing a tool for the task

The choice depends on the task. The two tools are slightly different but potentially complementary.

Choose code2prompt when:

• The LLM needs to understand, change or build on code from a specific codebase, yours or one you obtained.
  • You want it to debug, document or test files from a particular repository.
  • You need a focused, filtered Markdown context of a codebase.
  • Example: "Refactor the handlePayment function in the Python codebase context (generated by code2prompt from project_X/services/billing.py) to improve error handling and logging."
  • Example (re-platforming): "Analyze this Markdown context of the ai-sdlc Python CLI tool (generated by code2prompt from its GitHub repo) and develop a plan to re-implement its core features as an Electron/Vue 3 application." This is the task in Turning the ai-sdlc CLI into an Electron GUI.

Choose Context7 when:

• The LLM needs to understand how to use an external library, framework or API.
  • You ask it to write code that integrates a third-party tool.
  • It needs current syntax or examples for a specific package version.
  • Example: "Show me how to implement OAuth 2.0 authentication in my Vue 3 application using the latest Supabase JavaScript client library." Context7 supplies the current Supabase documentation.

Use both when:

• An agent works on a task that needs a specific local codebase and external libraries.
  • It fetches project context through code2prompt's MCP server, then gets library documentation through Context7's MCP tools.
  • Example: an agent adds a weather API to my PlantDex MVP, a Vue 3 and Supabase app, to make plant care recommendations. It would use:
    1. code2prompt, to understand the existing plantStore.ts (Pinia store) and PlantDetailPage.vue component structure.
    2. Context7, for the latest documentation and usage examples of the weather API, such as Open-Meteo.
    Guided by my AI-first development principles, the agent would then generate the Vue 3 component changes and the Supabase logic that calls the new weather API.