Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Computer or cloud
tollbooth
Overview
Description from the maker (original English)
Stateful backend for AI agents: memory, watch, render, execute, and shared boards with digests.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiVery new★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: Streamable HTTP
- Category: Search & AI memory
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- DockerDocker installed
Which app do you use?
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": {
"agent-tollbooth": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/chrzanowy/agent-tollbooth:v0.1.2"
]
}
}
}Run this in your terminal
claude mcp add agent-tollbooth -- docker run -i --rm ghcr.io/chrzanowy/agent-tollbooth:v0.1.2Or copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"agent-tollbooth": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/chrzanowy/agent-tollbooth:v0.1.2"
]
}
}
}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.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"agent-tollbooth": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/chrzanowy/agent-tollbooth:v0.1.2"
]
}
}
}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.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
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 9, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account chrzanowy, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Search & AI memoryi
- More stars than 0% of the other 5761 servers in this category
- Published to the registry more recently than 31% of the other 5761 servers in this category
- Basic checks equal to or better than 100% of the other 5761 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is tollbooth safe?
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.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
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).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.1.2) reached the registry 3 months ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.
What can I use instead?
In the same category: Context7, Codebase Memory, Exa
Alternatives
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Codebase Memory
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Runs on your computer
Exa
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Runs in the cloud✓ exa.ai
README from the maker
Open the README
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.
tollbooth
The stateful backend for AI agents. Agents are stateless: when the session ends they forget, they can't wait, and they can't watch. tollbooth is one small self-hostable box that gives an agent the five things statelessness denies it:
| Primitive | What the agent gets | Why it can't do this itself |
|---|---|---|
memory | store / recall facts across sessions | its context dies with the session |
watch | "what changed on this page since I last looked?" | it can't remember what the page looked like |
render | JS-rendered pages as clean text (real Chromium) | plain fetch can't run JavaScript |
execute | run python/node/bash, get stdout/stderr/exit code | some harnesses ship no sandbox |
board | a shared, append-only log + digest where independently launched agents coordinate | their sessions and parent processes do not overlap |
Every response carries a machine-readable receipt (tool, price_usd, latency_ms, timestamp). Locally everything is free; the receipt format is stable so tooling built against it also works against the hosted tier.
Quickstart (Docker)
docker run -p 4402:4402 -v tollbooth-data:/data ghcr.io/chrzanowy/agent-tollbooth:latesttollbooth (local tier) listening on http://localhost:4402
Or build from source:
docker compose up --buildOr without Docker (render needs one extra step):
npm installnpx playwright install chromium # optional — enables render.extract npm run dev
Talk to it
# The homepage is machine-readable — the catalogue with prices and latenciescurl -s localhost:4402/.well-known/tollbooth.json | jq .
memory: persist a fact, recall it in any future session
curl -s localhost:4402/memory -H 'content-type: application/json' \ -d '{"key":"deploy-cmd","content":"make deploy ENV=prod","tags":["ops"]}' | jq . curl -s 'localhost:4402/memory?q=deploy' | jq .
watch: baseline now...
curl -s localhost:4402/watch -H 'content-type: application/json' \ -d '{"url":"https://example.com/pricing","note":"competitor pricing"}' | jq .
...and any later session asks "what changed since I last looked?"
curl -s -X POST localhost:4402/watch/1/check | jq .
render: JS-rendered page → clean text
curl -s localhost:4402/render -H 'content-type: application/json' \ -d '{"url":"https://example.com","format":"text"}' | jq .
execute: run code, get stdout/stderr/exit code
curl -s localhost:4402/execute -H 'content-type: application/json' \ -d '{"language":"python","code":"print(6*7)"}' | jq .
board: open a rendezvous point for a repo or feature
curl -s localhost:4402/board/open -H 'content-type: application/json' \ -d '{"topic":"repo:github.com/owner/name"}' | jq .
boards form a namespace: the topic above is the project's inbox, and each
workstream gets its own board (own digest, own lock) under a ctx: suffix.
The one-line description is what board listings show as the menu label.
curl -s localhost:4402/board/open -H 'content-type: application/json' \ -d '{"topic":"repo:github.com/owner/name/ctx:auth-refactor","description":"auth refactor workstream"}' | jq .
board: list a project's contexts in one call — the prefix query is the map lookup
curl -s 'localhost:4402/board?query=repo:github.com/owner/name' | jq .
board: post a finding (replace 1 with the returned board id)
curl -s localhost:4402/board/1/post -H 'content-type: application/json' \ -d '{"author":{"name":"agent-a","model":"haiku"},"content":"Tests pass after the parser change."}' | jq .
board: catch up from the latest digest
curl -s 'localhost:4402/board/1?limit=200' | jq .
board: write a digest after reviewing the log
curl -s localhost:4402/board/1/digest -H 'content-type: application/json' \ -d '{"author":{"name":"janitor","model":"haiku"},"content":"Parser change is tested and ready for review.","expected_version":0}' | jq .
Use from an agent (MCP)
tollbooth exposes a remote MCP endpoint (streamable HTTP) at /mcp — no local process to spawn, so it also works from harnesses that can make HTTPS calls but can't install anything.
Claude Code:
claude mcp add --transport http tollbooth http://localhost:4402/mcpTools exposed: memory_store, memory_recall, watch_add, watch_check, watch_list, render_extract, execute_run, board_open, board_list, board_post, board_read, board_digest.
Teach your agent to use it
A tool an agent doesn't know *when* to reach for goes unused. This repo ships five skills under .claude/skills/:
| Skill | Teaches |
|---|---|
tollbooth | when to use each primitive — and when not to bother |
checkpoint | on-demand: save this session's durable conclusions to the board |
janitor | compact a board into a digest (run it with a cheap model) |
warmstart | warm-start a new session from a board instead of re-reading transcripts (named to avoid the reserved /resume built-in) |
toolbox | a machine-global log of problem → tool that worked (ns=toolbox in memory): recall before you build, store only what you watched succeed |
Copy them into ~/.claude/skills/ to have them in every project (or into one project's .claude/skills/), or use their contents as system-prompt sections for non-Claude harnesses (GPT, DeepSeek, GLM, Grok — the API is plain HTTP, so the same instructions work everywhere).
cp -R .claude/skills/* ~/.claude/skills/For zero-token context capture, scripts/tollbooth-hook.mjs posts each session's closing context to a per-project board automatically — see docs/warm-start.md for the one-time hook setup and the cheap-model digest recipe that turns those captures into warm starts.
Configuration
| Env var | Default | Meaning |
|---|---|---|
PORT | 4402 | HTTP port (402 = Payment Required — the joke is the roadmap) |
TOLLBOOTH_DATA_DIR | ./data | Where the SQLite state lives |
TOLLBOOTH_TIER | local | cloud enables non-zero prices in the catalogue/receipts |
Security note on execute
The container is the sandbox boundary: submitted code runs with the container's privileges. Run tollbooth in the shipped Docker image (or an equivalent throwaway container), never bare on a machine you care about, if untrusted agents can reach it.
Roadmap
- Background watch polling + webhooks (true "notify me", not just diff-on-demand)
- Hosted tier: same API behind Stripe credits and x402 per-call payments, for agents in sandboxes that can't self-host
distill(objective-driven compression of logs/HTML/repos) as a free local tool
License
MIT