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Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.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)
Find GitHub implementations of arXiv research papers — paper lookup, ranked code, search.
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.
CODEOFPAPER_API_URLYou need
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Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"codeofpaper": {
"command": "uvx",
"args": [
"codeofpaper"
],
"env": {
"CODEOFPAPER_API_URL": "your-value"
}
}
}
}Run this in your terminal
claude mcp add codeofpaper --env CODEOFPAPER_API_URL=your-value -- uvx codeofpaperFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"codeofpaper": {
"command": "uvx",
"args": [
"codeofpaper"
],
"env": {
"CODEOFPAPER_API_URL": "your-value"
}
}
}
}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": {
"codeofpaper": {
"type": "stdio",
"command": "uvx",
"args": [
"codeofpaper"
],
"env": {
"CODEOFPAPER_API_URL": "your-value"
}
}
}
}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.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
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CODEOFPAPER_API_URL envThese 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 6, 2026
The registry name was verified with the GitHub account isagelswann, 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 (0.3.2) reached the registry 5 months 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.
Maintenance mode. This CLI and MCP server are not actively developed. They keep working as long as api.codeofpaper.com is up; issues may go unanswered.
Discover GitHub implementations of research papers — from the terminal.
Search 181k+ arXiv papers and find their code on GitHub. Works for humans and AI agents alike.
pip install codeofpaper
Or with pipx for isolated installs:
pipx install codeofpaper
Requires Python 3.10+. No API key needed — works anonymously at 60 requests/minute.
# Search for papers
codeofpaper search "vision transformers"
codeofpaper paper 2010.11929 codeofpaper paper https://arxiv.org/abs/2010.11929
codeofpaper code 1706.03762
codeofpaper trending --has-code
codeofpaper -o json trending | jq '.trending[] | {title, stars: .max_stars}'
codeofpaper -o json paper 2010.11929 | jq '{title, repos: [.repos[] | {full_name, tier}], forks: [.fork_graph[] | .full_name]}'
codeofpaper research "reinforcement learning" --depth deep
Use -o / --output before the subcommand:
codeofpaper -o json paper 2010.11929
codeofpaper -o quiet search "attention" | head -5 codeofpaper -o csv trending --has-code > papers.csv
| Format | Flag | Use case |
|---|---|---|
| Table | -o table | Human reading (default) |
| JSON | -o json | Scripts, jq pipelines, full response data |
| Quiet | -o quiet | IDs only, one per line — pipe into other commands |
| JSONL | -o jsonl | Streaming, append to files, batch processing |
| BibTeX | -o bibtex | Citation managers, LaTeX bibliographies |
| CSV | -o csv | Spreadsheets, pandas, data analysis |
The shorthand -q is equivalent to -o quiet:
codeofpaper -q search "transformers" | head -3
You can set a default format via config so you don't need -o every time:
codeofpaper auth setup # then edit ~/.config/codeofpaper/config.json
| Command | Description | Example |
|---|---|---|
search | Full-text paper search | codeofpaper search "vision transformers" --has-code |
paper | Paper details by arXiv ID or URL | codeofpaper paper 2010.11929 |
code | GitHub repos implementing a paper | codeofpaper code 1706.03762 |
similar | Semantically similar papers | codeofpaper similar 2010.11929 |
suggest | Autocomplete / quick lookup | codeofpaper suggest "attention" |
random | Random interesting paper | codeofpaper random --quality high |
| Command | Description | Example |
|---|---|---|
trending | Trending papers by category | codeofpaper trending --category cs.CV --sort hot |
categories | List categories or get details | codeofpaper categories cs.AI |
conferences | List all conference series | codeofpaper conferences |
conference | Papers from a specific conference | codeofpaper conference neurips_2024 --has-code |
code-drops | Recent conference papers with new code | codeofpaper code-drops --days 7 |
repo | Reverse lookup: repo → paper | codeofpaper repo google-research/vision_transformer |
open | Open paper or repo in browser | codeofpaper open 2010.11929 --code |
| Command | Description | Example | |
|---|---|---|---|
research | Structured research overview | codeofpaper research "RL" --depth deep | |
batch | Process multiple IDs from stdin/file | `cat ids.txt \ | codeofpaper batch paper` |
export | Paginated bulk export | codeofpaper export trending -o csv > out.csv |
| Command | Description | Example |
|---|---|---|
auth | Manage API key (setup/status/clear) | codeofpaper auth status |
status | Check API health and stats | codeofpaper status |
codeofpaper search "reinforcement learning" --sort has_code --has-code
codeofpaper search "GAN" --limit 20 --offset 10
Options: --limit, --offset, --sort (relevant, recent, has_code), --has-code, --category.
Multi-step orchestrated overview of a research topic:
codeofpaper research "vision transformers" --depth deep
| Depth | Steps | API calls |
|---|---|---|
shallow | Search only + landscape statistics | 1 |
medium | Search + repos for top papers (default) | ~6 |
deep | Search + repos + similar papers from #1 | ~8 |
Process multiple queries or IDs from a file or stdin. Always outputs JSONL regardless of -o:
# From a file
codeofpaper batch paper ids.txt
echo -e "2010.11929\n1706.03762" | codeofpaper batch paper
codeofpaper -q similar 2010.11929 | codeofpaper batch code
Supported commands: paper, search, code, similar, suggest.
Each line produces one JSON object:
{"input": "2010.11929", "status": "ok", "data": {...}}
{"input": "9999.99999", "status": "error", "error": "Not found"}
Options: --delay (seconds between calls, default 0.5).
Paginated bulk export from trending, conference, or search:
codeofpaper export trending --category cs.CV --has-code -o csv > cv.csv
codeofpaper export conference neurips_2024 --has-code -o bibtex > neurips.bib codeofpaper export search "transformers" --max 500 -o jsonl > data.jsonl
Auto-paginates through results (100 per page, 0.5s delay). Options: --max (default 200), --has-code, --category, --days.
The CLI is designed for AI agent consumption. Key features:
Agents can branch on exit codes without parsing error messages:
| Exit Code | Meaning | Agent Action |
|---|---|---|
| 0 | Success | Parse stdout |
| 1 | General error | Log and report |
| 2 | Connection error | Retry with backoff |
| 3 | Not found (404) | Skip or try different ID |
| 4 | Rate limited (429) | Wait and retry |
| 5 | Auth required (401/403) | Run codeofpaper auth setup |
# JSON for structured parsing
codeofpaper -o json paper 2010.11929 | jq '.title'
codeofpaper -q search "attention" | head -5
codeofpaper -o jsonl trending --has-code | while read -r line; do echo "$line" | jq -r '.arxiv_id' done
Responses are cached on disk for 30 minutes (~/.cache/codeofpaper/http/), so repeated calls are instant and free.
Code of Paper ships an optional Model Context Protocol server that exposes paper / code lookup as tools to any MCP-compatible agent — Claude Desktop, Cursor, Continue, Cline, Zed, etc.
Install with the mcp extra:
pip install 'codeofpaper[mcp]'
uv tool install 'codeofpaper[mcp]'
This adds a codeofpaper-mcp entry point that speaks MCP over stdio.
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS), %APPDATA%\Claude\claude_desktop_config.json (Windows), or ~/.config/Claude/claude_desktop_config.json (Linux):
{
"mcpServers": { "codeofpaper": { "command": "uvx", "args": ["--from", "codeofpaper[mcp]", "codeofpaper-mcp"] } } }
This pulls + runs the server on demand — no global install needed. If you've already pip install'd the package, you can replace the command with just "codeofpaper-mcp" and drop args.
Most MCP clients accept the same shape:
{
"mcpServers": { "codeofpaper": { "command": "codeofpaper-mcp", "env": { "CODEOFPAPER_API_URL": "https://api.codeofpaper.com" } } } }
| Tool | Purpose |
|---|---|
paper_lookup(paper_id_or_url) | Paper metadata + confident-tier repos |
code_for_paper(paper_id_or_url, limit, include_possible) | Ranked GitHub repos implementing a paper |
search_papers(query, limit, year, venue, has_code) | Free-text paper search with filters |
All tools are read-only, return plain JSON, and surface API errors as {"error": "...", "status": N} rather than throwing — agents get structured output either way.
# Find papers with code about a topic
codeofpaper search "reinforcement learning" --sort has_code --has-code
codeofpaper code 1706.03762
codeofpaper paper https://arxiv.org/abs/2010.11929
codeofpaper repo google-research/vision_transformer
codeofpaper open 2010.11929 codeofpaper open 2010.11929 --code
# What's trending in computer vision?
codeofpaper trending --category cs.CV --sort hot
codeofpaper code-drops --days 7
codeofpaper conference neurips_2024 --track oral --has-code
codeofpaper trending --category cs.CV --days 1 -o json >> ~/research/daily_cv.jsonl
# Find implementations of a paper's related work
codeofpaper similar 2010.11929 -o quiet | codeofpaper batch code
cat reading_list.txt | codeofpaper batch paper > enriched.jsonl
codeofpaper code 1706.03762 -o quiet | head -1 | xargs gh repo view
codeofpaper export conference neurips_2024 --has-code -o bibtex > neurips2024_code.bib
codeofpaper export trending --category cs.CV --has-code --days 365 -o csv > cv_with_code.csv
# Find a paper → get top repo → clone it
REPO=$(codeofpaper search "attention is all you need" -o quiet | head -1 | \ xargs codeofpaper code -o json | jq -r '.top_repos[0].full_name') gh repo clone "$REPO"
ID=$(codeofpaper random --quality high -o quiet) codeofpaper paper "$ID" codeofpaper similar "$ID" codeofpaper code "$ID"
ARXIV_ID="2010.11929" TITLE=$(codeofpaper -o json paper "$ARXIV_ID" | jq -r '.title') echo "Paper: $TITLE"
Config is stored as JSON via platformdirs:
| OS | Path |
|---|---|
| Linux | ~/.config/codeofpaper/config.json |
| macOS | ~/Library/Application Support/codeofpaper/config.json |
| Windows | %APPDATA%\codeofpaper\config.json |
Fields:
{
"api_url": "https://api.codeofpaper.com", "api_key": null, "default_format": "table", "ca_bundle": null }
No config file needed — everything works out of the box with sensible defaults.
Options are resolved in this order (first wins):
--api-url, --api-key, --ca-bundle, -o)CODEOFPAPER_API_URL, CODEOFPAPER_API_KEY, `CODEOFPOnly the beginning is shown; read the rest in the repo