Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Terminal output, project snapshots and semantic diffs as LLM-ready context
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"codigest": {
"command": "uvx",
"args": [
"codigest",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add codigest -- uvx codigest mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"codigest": {
"command": "uvx",
"args": [
"codigest",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"codigest": {
"type": "stdio",
"command": "uvx",
"args": [
"codigest",
"mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account NaNIsaac, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.13.0) reached the registry 9 days ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Share the code context you choose with an AI: your project, a few files, what changed, or the command that failed.
Documentation: [nanisaac.github.io/codigest](https://nanisaac.github.io/codigest/) (installation, guides, command reference). 한국어
Automated release-specific security scan (A–F), not an independent security certification. How ratings work .
Suppose a test fails and you want a coding assistant to help. Pasting the error alone may omit the function under test; pasting the whole repository buries the problem in unrelated files.
Codigest lets you choose what context to send. From the terminal, it can collect selected files, their local imports, recent changes, or a failed command and the files mentioned in its output. It packages the selection as text for you to inspect and paste into ChatGPT, Claude, or another tool. It does not choose a fix or call a model on its own.
pack gives the reason for each file and names the ones it left out..env files and known key patterns and attempts to mask tokens in code or output. Review a bundle before sharing it; automated filtering cannot guarantee that every sensitive value is caught.Codigest does not call any AI model; it only prepares what you send to one. Everything runs on your machine.
One of the workflows below: a test fails, and one command collects what the model needs.
Prebuilt binary, no Python needed. Windows (cmd or PowerShell):
powershell -ExecutionPolicy Bypass -c "irm -useb https://raw.githubusercontent.com/NaNIsaac/codigest/main/install.ps1 | iex"
Linux, macOS and Termux:
curl -fsSL https://raw.githubusercontent.com/NaNIsaac/codigest/main/install.sh | sh
This installs cdg to %LOCALAPPDATA%\Programs\codigest (added to your user PATH) on Windows, ~/.local/bin on Linux and macOS, and $PREFIX/bin in Termux. Run the same line again to update. CDG_VERSION=0.12.1 pins a version and CDG_INSTALL_DIR=... picks the folder (then PATH is left alone). Supported: Windows x64, Linux x64 and ARM64, macOS Intel and Apple Silicon, Android ARM64 (Termux; the tests also run inside Termux in CI). Archives for download by hand are on the releases page. No setup is needed in your projects. Both scripts check the archive against the SHA-256 GitHub records for it. On Linux and macOS, install.sh will not replace a cdg from the Python package in the same folder unless CDG_FORCE=1 is set.
The same commands are on PyPI as codigest. Install it with uv, which also fetches the Python version it needs (3.15) and keeps it apart from your projects:
winget install --id astral-sh.uv # Windows (skip if you have uv)
curl -LsSf https://astral.sh/uv/install.sh | sh # macOS/Linux
uv tool install codigest uv tool update-shell # then restart your terminal
The command is codigest, or cdg for short. Both installs provide a command called cdg, so keep only one of them on PATH. If the install fails with a syntax error on lazy import, run uv self update and try again.
Next, cdg term install hooks your shell so the output of every command is kept for cdg pack and cdg term. It is off until you run it; see Terminal capture.
On Windows, PowerShell's help codigest is a separate command from codigest --help. For optional native Get-Help integration without automatic command capture:
cdg shell-help install # register comment-based PowerShell help
help codigest # or: Get-Help codigest cdg shell-help uninstall # remove it when no longer needed
This adds a small forwarding function to your PowerShell profile; other help commands are unchanged. The CLI's own full command reference is still available via cdg --help.
In a terminal, every command below copies its result to the clipboard. Add -s to also print it, -o FILE to also write it to a file, --no-copy to leave the clipboard alone, and -m "..." to put your question or instruction in it. When the output goes to a pipe or a file instead (cdg scan > ctx.xml, cdg digest | less, a script, a coding agent's shell), it is printed and the clipboard is left alone; add --copy to copy it anyway. Over SSH the copy goes to the clipboard of the machine you type on (OSC 52), not the remote one; see output options.
Run scan in your project and paste:
cdg scan
$ cdg scan
Copied snapshot to clipboard Snapshot 3 files, ~121 tokens
The clipboard now holds the file tree and every file, honoring .gitignore:
<project_snapshot project="project">
├── .gitignore ├── prices.py └── test_prices.py
.venv/ pycache/ .pytest_cache/
def apply_discount(price, percent): return price * (1 - percent)
from prices import apply_discount
def test_ten_percent(): assert apply_discount(200, 10) == 180
Large projects ask before copying (above 30k tokens or 100 files). For those, share less, as in the next section.
cdg scan src/app.py -r # one file and the local files it imports, recursively
cdg scan src/billing/ docs/ # folders, files, or both cdg scan src/app.py -l # with line numbers, so answers can say "line 42" cdg scan src/app.py:40-80 # only lines 40 to 80, numbered (src/app.py:42 for one line) cdg digest # signatures and docstrings only, much smaller cdg digest src/billing/ # the same for one folder cdg tree -L 2 # the file tree, two levels deep
The examples from here on are the same project a little later: prices.py has grown a Cart class, and an old MATLAB version sits in legacy/.
digest is the overview to start a conversation with. It lists every function, class and top-level variable of Python files, and the functions and %% sections of MATLAB files, in the order they appear, with the line they start on and the first line of their docstring (or, in MATLAB, of their help comment). Class attributes and dataclass fields are listed with their values, shortened when long:
$ cdg digest -s
├── .gitignore ├── legacy │ └── discount.m ├── prices.py └── test_prices.py
1: function p = discount(price, percent) Price after taking off percent percent. 3: %% Apply
1: TAX_RATE = 0.1 4: def apply_discount(price: float, percent: float) -> float Price after taking off percent percent. 9: class Cart Items and the discount that applies to all of them. 12: def init(self, percent: float=0) 16: def total(self) -> float Sum after discount and tax.
4: def test_ten_percent()
Digest 3 files, ~192 tokens
A model that has seen this can ask for exactly what it needs ("show me Cart.total"), and cdg scan prices.py:16-19 gives it just that method, numbered the same way:
$ cdg scan prices.py:16-19 -s
└── prices.py
16: def total(self) -> float: 17: """Sum after discount and tax.""" 18: subtotal = sum(apply_discount(p, self.percent) for p in self.items) 19: return subtotal * (1 + TAX_RATE)
tree -L keeps a large or vendored folder from flooding the output:
$ cdg tree -L 1
├── .gitignore ├── legacy/ (1 file) ├── prices.py └── test_prices.py
To share what changed while you work, set a baseline first:
cdg mark # e.g. before a refactor
cdg diff # text diff since the mark cdg semdiff # Python: which functions and classes changed
A baseline is a private copy kept in .codigest/; your Git history is not touched.
Run the command through cdg cap. The output looks exactly as it would without it; Codigest keeps a copy. Then ask for a pack:
cdg cap -- pytest -q # or npm test, cargo test, go test ./..., make
cdg pack
$ cdg pack
Packed ~370 / 30,000 tokens failed pytest -q (exit 1, just now) changes uncommitted changes (git)
Copied pack to clipboard
Read it as: the failing command, your uncommitted Git changes, test_prices.py because the error points at its line 5, and prices.py because you edited it.
What exactly was copied
<context_pack project="project">
F [100%] ================================= FAILURES ================================== _____________________________ test_ten_percent ______________________________
def test_ten_percent():
assert apply_discount(200, 10) == 180
E assert -1800 == 180 E + where -1800 = apply_discount(200, 10)
test_prices.py:5: AssertionError ========================== short test summary info ========================== FAILED test_prices.py::test_ten_percent - assert -1800 == 180 1 failed in 0.11s
diff --git a/prices.py b/prices.py index 3d691df..428752a 100644 --- a/prices.py +++ b/prices.py @@ -1,2 +1,2 @@ def apply_discount(price, percent):
1: from prices import ap
Only the beginning is shown; read the rest in the repo