tbd

A small Clojure HTTP API built on Ring (Jetty) and reitit, with a ClojureScript re-frame front end (ui/), Swagger UI docs, unit tests, dockerized integration tests, and joint line coverage.

Requirements

• Docker, for the integration tests and the script/docker-* tasks (all CI needs)
• For running tasks natively (optional): Clojure CLI (tools.deps) with JDK 21+, Node 20+ (22.22+ for the UI tests; with a JDK) for the UI build, and Babashka for script/coverage-report

Running

script/ui-build        # compile the UI (once, and after UI changes)
script/server [port]

The server listens on port 3000 by default; pass a port argument or set PORT to change it (the argument wins):

script/server 8080
PORT=8080 script/server

This is a thin wrapper around clojure -X:run, which also honors PORT. Then open http://localhost:3000 for the UI. Without script/ui-build the server still starts, but / shows a placeholder.

Or build and run the container:

docker build -t tbd .
docker run --rm -p 3000:3000 tbd

API

Method	Path	Description
GET	/	The UI: an HTML shell that loads the compiled ClojureScript from /js/main.js
GET	/api/ping	Health check, returns pong
GET	/api/add?x=2&y=3	Adds two numbers: {:result 5} (EDN by default, see Formats) (400 if either is missing or not a number)
POST, DELETE	/mcp	The MCP server (JSON-RPC over Streamable HTTP; DELETE ends a session; GET is 405). Needs an OAuth access token (401 without one)
GET	/.well-known/oauth-protected-resource, /.well-known/oauth-authorization-server	The MCP OAuth discovery documents (RFC 9728 and 8414)
POST	/oauth/register	Dynamic client registration (RFC 7591) for MCP clients
GET, POST	/oauth/authorize	Where an MCP client sends the user's browser: Gitea login if needed, then a consent page
POST	/oauth/token	Trades an authorization code (with its PKCE verifier) or a refresh token for tokens
POST	/api/chat	Login required. Body {"messages": [{"role": "user", "content": "hi"}]} (the conversation so far, 1 to 40 messages of up to 8000 characters, roles user, assistant or tool; and optionally "page", the id of the UI page the user is on ("tic-tac-toe", "ai-setup": a lowercase slug of up to 64 characters), which the LLM is told in a system message, and "tools", the page's tools as MCP describes them: see the chatbot's tools) sends it to the chatbot's LLM and streams the reply back as server-sent events (text/event-stream): data: {"thinking":"Let me"} for each piece of the model's reasoning, data: {"content":"Hel"} for each piece of the reply, data: {"tool-calls":[{"id":"call_1","name":"add","arguments":"{\"x\":1,\"y\":2}"}]} if the model asks for tools to be called, and last data: {"done":true} or data: {"error":"..."}. Failures before the reply starts are ordinary errors: 400 for an invalid conversation, 502 if the LLM server can't be reached or refuses, 503 if LMSTUDIO_URL isn't set
GET	/api-docs	Swagger UI
GET	/swagger.json	OpenAPI/Swagger spec
GET	/auth/login	Starts the Gitea OAuth login (redirects to Gitea)
GET	/auth/callback	OAuth redirect target: completes the login, sets the session cookie, redirects to /
POST	/auth/logout	Ends the session
GET	/auth/me	The logged-in user, {:login "alice" :id 7 :admin? true} (401 if not logged in)
GET	/api/todos	Login required. Your todos: {:todos [{:id "3f2b8c1e-5a47-4d0e-9b6a-1c2d3e4f5a6b" :owner-id 7 :title "buy milk" :done false :created-at 1700000000000 :updated-at 1700000000000}]}
POST	/api/todos	Login required. Body {"title": "buy milk"} creates a todo and returns it with 201 (400 if the title is blank or over 200 characters)
GET	/api/todos/:id	Login required. One todo (404 if it doesn't exist or isn't yours)
PATCH	/api/todos/:id	Login required. Body with title and/or done ({"done": true}) updates the todo and returns it (400 for an empty or invalid change, 404 as above)
DELETE	/api/todos/:id	Login required. Deletes the todo: {:deleted true} (404 as above)
GET	/nrepl	Admin only. Whether the nREPL debug server is on: {:enabled false}, or {:enabled true :bind "127.0.0.1" :port 7888}
POST	/nrepl	Admin only. Body {"enabled": true} (JSON or EDN) starts the nREPL debug server, false stops it; returns {:enabled true :bind "127.0.0.1" :port 7888}

curl localhost:3000/api/ping           # pong
curl 'localhost:3000/api/add?x=2&y=3'  # {:result 5}
curl 'localhost:3000/api/add?x=2&y=3' -H 'Accept: application/json'  # {"result":5}

Formats

The API speaks EDN, JSON and Transit (application/transit+json, application/transit+msgpack). Responses follow the request's Accept header, and request bodies are read by their Content-Type. EDN is the default: with no Accept (or */*, or a type the API can't produce) you get EDN, and a body sent with no Content-Type is read as EDN. Errors (400, 401, 403, ...) use the same format as everything else. Two exceptions: /api/ping is always text/plain, and /swagger.json is always JSON.

Handlers return plain data ((tbd.http/respond 200 {:result 5})), and the muuntaja middleware in tbd.core encodes it; don't pr-str a body yourself.

Todos

Each logged-in user has their own todo list, keyed by their Gitea user id. Every query is filtered by that id, so users can't see or change each other's todos; someone else's todo answers 404, exactly like a missing one. Without a login the todo routes answer 401. The operations themselves (ownership, validation, storage) live in src/tbd/todo/service.clj, which the HTTP routes and the MCP tools both call, so they behave the same and are tested once; a route or tool only translates its request into a service call and the result into a response, and never touches the database.

Todos are stored in a local SQLite file, data/todo.db by default (git-ignored), which is created and migrated on the first todo request. Set TODO_DB_PATH to put it elsewhere. In Docker, mount a volume at that path (e.g. docker run -v tbd-data:/app/data ...), or the todos are lost with the container.

Every id the app issues (a todo's, an OAuth grant's) is a UUID, never a number: a model that reads 7 as a todo's id mixes it up with a 7 in a title. (owner-id is the user's id in Gitea, which it issues, so it stays a number.) A database from before that is migrated when it is opened: rows keep their order and get UUIDs, so the ids in URLs or notes from before no longer work.

# after logging in in a browser, copy the `tbd-session` cookie value:
curl -XPOST localhost:3000/api/todos -H 'Cookie: tbd-session=...' \
     -H 'Content-Type: application/json' -d '{"title": "buy milk"}'
curl localhost:3000/api/todos -H 'Cookie: tbd-session=...'

MCP server

The server also speaks MCP (Model Context Protocol) at /mcp, built on PluMCP and running inside this same process; there is nothing extra to start. It uses the Streamable HTTP transport, and, like the todo API, it acts as the logged-in user: a client has to be approved by a user first (see below), and from then on every tool call knows who is calling.

claude mcp add --transport http tbd https://tbd.example.com/mcp   # <BASE_URL>/mcp

The first time the client connects it gets a 401, opens your browser, you log in with Gitea as usual and approve the client, and the client keeps its tokens from then on. It needs the Gitea login to be configured, including BASE_URL (which must be the address the client uses; without them /mcp answers 503 rather than being open).

It offers these tools: ping, add (the same logic as /api/add), whoami (which user this connection acts as, and whether they are an admin), and, on the caller's own todos (the same ones as /api/todos), list_todos, get_todo, create_todo, update_todo and delete_todo. To add a tool, add a tool to src/tbd/mcp/tools.clj; it should call the group's service (or pure core functions), not a database, and if the group has no service yet, that is the thing to add, so the HTTP routes can share it. Its arguments are described by the group's Malli schema in src/tbd/<group>/schema.clj, the same schema the HTTP routes use for their parameters, so a shape of data is written down once: the tool's input schema is generated from it, and the arguments a client sends are coerced and validated against it (plumcp only checks that required ones are present). A tool that touches a user's data or does something admin-only gets the caller from current-user there ({:login :id :admin? :client-id}), and must not trust anything else about who is asking.

(The UI has tools of its own for a browser agent, see below; they are not part of this server.)

Sessions are kept in memory, so they end when the server restarts (clients just re-initialize; their tokens are in the database and keep working). Server-to-client streaming (GET /mcp) is deliberately off: nothing here pushes messages, and the response would hang under Jetty. PluMCP needs JDK 21+.

Connecting an MCP client

tbd is its own OAuth 2.1 authorization server for /mcp (in src/tbd/oauth/), with the Gitea login as the way a user proves who they are. Gitea's own tokens never reach an MCP client, and the tokens tbd issues are worth nothing anywhere else. The flow is the one the MCP authorization spec describes, so any compliant client does it by itself:

1. POST /mcp with no token gets 401 and WWW-Authenticate: Bearer resource_metadata="<BASE_URL>/.well-known/oauth-protected-resource/mcp".
2. The client reads that document, then /.well-known/oauth-authorization-server, and registers itself at /oauth/register (dynamic client registration, public clients only).
3. It opens /oauth/authorize in the browser (authorization-code flow, PKCE S256 required). If you aren't logged in you go through the Gitea login first and come back; then a consent page names the client and the address you'll be sent back to, and asks you to Approve or Deny.
4. The client trades the code at /oauth/token for an access token (1 hour) and a refresh token (30 days, rotated on every use), and sends Authorization: Bearer ... on every /mcp request.

The consent page is what stops a stranger's client (anyone can register one) from getting a token by sending you a link: nothing is issued without your click. Redirect URIs must be https, http on localhost/127.0.0.1/[::1] (the port may vary), or an app's own scheme (cursor://...).

What's stored is in OAUTH_DB_PATH: registered clients, and for each approval the user and a hash of the current access and refresh tokens. To cut a client off, delete its row from grants. Admin-ness isn't in the token: it is looked up in ADMIN_USERS on every request, so removing someone from the list takes effect at once. A Gitea account that is disabled keeps working until its refresh token expires (up to 30 days without use) or its grant is deleted, since Gitea isn't asked again after the login.

Authentication and the admin API

Login is OAuth2 (authorization-code flow) against a Gitea instance. Anyone with a Gitea account can log in, but routes marked :admin? true (currently GET and POST /nrepl) also require the user to be on the ADMIN_USERS allowlist: 401 without a login, 403 for a logged-in non-admin. With no allowlist, nobody is an admin.

1. In Gitea (Settings > Applications) create an OAuth2 application, a confidential client, with the redirect URI <BASE_URL>/auth/callback.
2. cp .env-template .env and fill it in. script/server loads .env; for Docker use docker run --env-file .env. .env is git-ignored and kept out of the image.

Variable	Purpose
GITEA_URL, GITEA_CLIENT_ID, GITEA_CLIENT_SECRET	The Gitea OAuth2 application. Without all three (and BASE_URL), /auth/login answers 503
BASE_URL	Public URL of this app; an https:// one also marks the session cookie Secure. It is also the address of the MCP server: the OAuth issuer, and what MCP clients are told to use (<BASE_URL>/mcp)
ADMIN_USERS	Comma-separated Gitea usernames (case-insensitive) allowed to use the admin API
SESSION_SECRET	Signs and encrypts session cookies. Unset = a random key per start, so logins don't survive a restart
TODO_DB_PATH	SQLite file for the todo group (default data/todo.db)
OAUTH_DB_PATH	SQLite file for the MCP OAuth server: registered clients, codes and tokens (default data/oauth.db). Mount a volume for it in Docker too, or every MCP client has to be approved again after each restart
LMSTUDIO_URL, LMSTUDIO_MODEL, LMSTUDIO_API_KEY, LMSTUDIO_TIMEOUT_SECONDS	The LM Studio server behind the chatbot. Only the URL is needed
CHAT_TRACE_FILE	Optional. A file to append a JSON trace of every chatbot turn to, with the model's full reasoning and replies. Off by default
NREPL_PORT, NREPL_BIND	Where the debug nREPL listens (default 7888 on 127.0.0.1)

Sessions live in an encrypted, signed, HttpOnly, SameSite=Lax cookie and expire after 8 hours (enforced server-side). The cookie is stateless, so POST /auth/logout clears it in the browser but can't revoke a copy someone already captured; it just expires on its own.

The nREPL endpoint

# after logging in in a browser, copy the `tbd-session` cookie value:
curl -XPOST localhost:3000/nrepl -H 'Cookie: tbd-session=...' \
     -H 'Content-Type: application/json' -d '{"enabled": true}'
clj -Sdeps '{:deps {nrepl/nrepl {:mvn/version "1.2.0"}}}' -M -m nrepl.cmdline --connect --host 127.0.0.1 --port 7888

nREPL has no authentication and evaluates arbitrary code, so enabling it is effectively remote code execution for whoever can reach the port. It therefore binds to loopback by default; reach it through an SSH tunnel (or docker exec) rather than setting NREPL_BIND=0.0.0.0. Each enable/disable is logged with the admin's Gitea username. It stays on until an admin disables it (or the server restarts), so switch it off when you're done.

UI

ui/ is a re-frame app (ClojureScript, React 19 via Reagent) built with shadow-cljs. It has four pages, as tabs across the top. Each has its own address in the URL's fragment (/#/tic-tac-toe, /#/calculator, /#/todos, /#/ai-setup), so the server only ever serves /, and a page can be bookmarked or reloaded.

Tab	What's on it
Tic-tac-toe	Two players on one board, all client side
Calculator	A small form that calls GET /api/add and shows the sum (or the server's validation error)
Todos	Your todo list (add, tick off, delete), backed by /api/todos. The tab only shows once you're logged in; its address shows a "Log in" prompt otherwise
AI setup	The claude mcp add ... command to copy (with this page's own address in it) and the steps to sign in, for adding the MCP server to Claude Code; and how to connect an MCP client to the page's own browser tools

A "Log in" / "Log out" link sits in the footer row next to the API docs links.

A green square in the bottom left corner (on every page, opposite the blue WebMCP one) opens the chatbot.

shadow-cljs compiles it into resources/public/js/ (git-ignored). The server serves that directory at /js, and serves resources/public/index.html, the shell that mounts the app, at /.

Task	What it does
script/ui-build	Optimized release build into resources/public/js
script/ui-watch	Rebuilds on every change, with hot reload; run script/server alongside and refresh the page

The Docker image builds the UI in its own stage, so docker build needs no local Node. The UI's tests run in another stage of the same Dockerfile (--target ui-test), which the server image doesn't include.

Chatbot

The green square opens a small chat panel. What you type goes to POST /api/chat, and the backend forwards the conversation to an LM Studio server (its OpenAI-compatible /v1/chat/completions) and returns the reply. The browser never talks to the LLM directly, so the server's address (and key) stay on the backend. The reply is streamed: the panel shows "Thinking…" until the model's first word (a reasoning model can take a while), then the reply appears as it is generated. What the model reasons is never shown, only that it is thinking; the tool calls (see below) show as small grey lines between the replies, in full, however long, and the conversation scrolls. The conversation lives in the page only (a reload or logging out clears it), and the UI sends the last 40 messages of it. Chat needs a login, like the todos: every message costs the LLM server's time.

The conversation scrolls. It follows the reply while you are at its end; scroll up to read back and it stays put until you send a message or scroll down again. Drag the panel's top right corner to make it wider or taller (the size is kept while the page is open, through Clear and closing the panel).

curl -N shows the stream (-N turns off curl's own buffering; use your session cookie):

curl -N -H "Cookie: $COOKIE" -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"user","content":"hi"}]}' http://localhost:3000/api/chat

The chatbot's tools

The chatbot can use the page. The browser tools (the calculator, the todos, tic-tac-toe) are registered in the page's model context, ui/src/tbd/ui/model_context.cljs, which is what WebMCP's navigator.modelContext is to an agent outside the page. The chat is that agent, embedded:

1. Each POST /api/chat carries "page", the page the user is on, and "tools": the tools' metadata, exactly what an MCP server answers tools/list with ({"name", "description", "inputSchema"}, the schema being JSON Schema). The backend gives it to the LLM as OpenAI-style tools (LM Studio's own tool use), and stays stateless. Only the tools of the page being shown are listed: a tool registered with :pages #{:calculator} is offered on the Calculator page and nowhere else (tic-tac-toe's tools are the Tic-tac-toe page's, the todos' are the Todos page's), and one with no :pages (ping, whoami, navigate) on every page. The list follows the page the user is on each time the conversation is sent (so does "page", which the backend puts in the system message ahead of the conversation for the LLM to know where the user is, with the names of the tools it has), and a call for a tool the page doesn't offer is refused with an error the LLM reads. The LLM can move between pages with navigate; its next request then carries the new page and that page's tools. An agent outside the page, through WebMCP, is still offered all of them.
2. The LLM never runs anything. When it wants a tool, it says so: the reply ends with a {"tool-calls": [...]} event (the calls are put together from the pieces the model writes, the arguments being the JSON text it wrote). The backend only relays them.
3. The page intercepts them and calls the tools right there (one after the other), through the same handlers WebMCP uses, so they act on the page as a user would and you see it happen. The chat panel shows each call and its result in small print between the messages.
4. The results go back as messages with the role tool ({"role": "tool", "tool-call-id": "call_1", "content": "..."}, after the assistant message that asked, which carries "tool-calls"), in another request with the conversation so far, for the LLM to go on from, until it answers with words.

A call that can't be made (a tool that isn't there, arguments that aren't a JSON object, a tool that fails) is a result with the reason, for the model to read and try again from. To keep a model that never stops calling tools from acting on your page for ever, one message of yours gets it at most 12 calls; then the chat stops, and says so. Results are cut to the 8000 characters the server accepts.

The chatbot is told to be an agent, not a conversationalist: the system message (tbd.agent.prompt/chat, in shared/tbd/agent/prompt.cljc, which default-system-prompt in src/tbd/chat/core.clj is) says that what it does shows on your screen at once, so it should call a tool in the same turn rather than offer to, chain calls (navigate, list, then act), take obvious defaults instead of asking, and answer in one short sentence, since the call and its result are already shown. If a model still answers requests with offers, that prompt (and its four examples, which name tools) is what to tune for it; the tool descriptions in webmcp.cljs are the other lever. The same file gives an MCP client outside the page the same instructions, see below.

To try it, use a model that supports tool use in LM Studio, log in, and ask the chat something the page can do: "add 2 and 3 with the calculator", "add a todo to buy milk", "play tic-tac-toe with me, you're O".

A reverse proxy in front of tbd must not buffer responses, or the reply arrives all at once at the end (the server sends X-Accel-Buffering: no, which nginx obeys; for others, turn buffering off for /api/chat). The thinking events (the reasoning) also keep the connection from looking idle to a proxy while the model reasons; the UI drops them. Anything that goes wrong is logged on the server's stdout, with a [tbd] chat: prefix.

Point the backend at your instance with environment variables (see .env-template):

LMSTUDIO_URL=http://192.168.1.20:1234 script/server

Only LMSTUDIO_URL is required (the address of LM Studio's server, without /v1; for a remote instance turn on "Serve on Local Network" there). LMSTUDIO_MODEL picks a model by id (default: the one LM Studio has loaded), LMSTUDIO_API_KEY is sent as a bearer token if the server wants one, and LMSTUDIO_TIMEOUT_SECONDS (default 120) is how long the LLM server may stay silent, before it starts answering or between two pieces of it, before the server gives up. Without LMSTUDIO_URL the server still starts (it says so), and the chat panel shows "chat is not configured".

Watching the chatbot think

Every request to /api/chat is one turn of the model, and every turn is logged on stdout: one turn-start line when it goes out and one turn-end line when it ends. The tool loop runs in the browser, so the server sees each tool result as a fresh request; the turns of one loop are tied together by a run id (a hash of the user's last message), with step counting them from 0.

[tbd] chat turn-start req=9c1e4a07 run=d4a6cdb3 step=3 page=tic-tac-toe model=qwen messages=8 tools=6 prompt-chars=1180 run-calls=3
[tbd] chat loop-suspect req=9c1e4a07 run=d4a6cdb3 step=3 kind=repeated-call tool=get_tic_tac_toe count=3 args={}
[tbd] chat loop-suspect req=9c1e4a07 run=d4a6cdb3 step=3 kind=repeated-thinking count=8 text="Let me check the board again."
[tbd] chat turn-end req=9c1e4a07 run=d4a6cdb3 step=3 outcome=tool-calls duration-ms=18040 ttft-ms=2210 think-ms=15100 events=1830 thinking-chars=9210 content-chars=0 calls="get_tic_tac_toe{}" flags=repeated-thinking

turn-end says how it ended (outcome: done, tool-calls, error, upstream-error, or aborted when the browser went away), how long it took (ttft-ms is the wait for the first piece, which includes the LLM reading the prompt; think-ms the time spent reasoning before answering), how much it wrote (events is close to a token count), and finish-reason and token counts when the LLM server sends them. flags is the grep handle for a stuck model, with a loop-suspect line for each trip as it happens:

Flag	Means
repeated-call	The same tool was called with the same arguments 3 or more times in one run (checked when the turn starts)
repeated-thinking	The reasoning said the same sentence again, ignoring case and punctuation: at the 4th time, then the 8th, 16th, ...
long-reasoning	The reasoning passed 8000 characters, then 16000, ... (only the ones without an answer yet are worrying)
truncated	The LLM stopped with finish_reason: length: it ran out of tokens, typically in a reasoning loop

These are heuristics for where to look, not verdicts, and nothing is stopped: the server's timeout only counts silence, so a model that reasons forever, out loud, is never cut off by it.

The log has no text of the conversation. To read what the model actually thought, set CHAT_TRACE_FILE (say data/chat-trace.jsonl, which git ignores): each turn's entries are appended there as JSON lines, with the full reasoning, reply and (in turn-start) the input messages of the run, the tool results the model was reacting to. It holds what the user typed, so treat it like the database.

# the runs that got stuck, and how
jq -c 'select((.flags // []) | length > 0) | {run, step, flags, thinking_chars: ."thinking-chars"}' data/chat-trace.jsonl
# what the model said in one turn
jq -r 'select(.req == "9c1e4a07" and .event == "turn-end") | .reasoning' data/chat-trace.jsonl

Browser MCP tools (WebMCP)

The page also offers tools of its own to your MCP client (Claude), through WebMCP: a small library that adds a widget to the page and lets the page register tools with it. They are the browser-side twin of the server's MCP tools: those work on your data on the server, these work the page, doing what you could do with the mouse and keyboard, so you see it happen. A tool that changes something opens the tab it acts on first (the todo tools open Todos, the game tools open Tic-tac-toe, the calculator tools open Calculator). They are in ui/src/tbd/ui/webmcp.cljs, registered with registerTool on a WebMCP instance (the @jason.today/webmcp npm package):

Tool	What it does
ping, whoami	Health check; who is logged in on the page, if anyone
navigate	Opens a page (tic-tac-toe, calculator, todos or ai-setup) as a click on its tab would; todos needs a login, like its tab. The chatbot is then offered the new page's tools
get_calculator	What the calculator form holds: its two fields and the result it shows
add	Types x and y into the calculator, presses Add, and returns the sum the page shows
list_todos	The todos the page shows (login required, like the todo list itself)
get_todo	Reads todos by id (todos: a list of {id})
create_todo	Types each title into the new-todo field and presses Add (todos: a list of {title}), returns the new todos
set_todo_done	Ticks or unticks the checkboxes of several todos in one call (todos: a list of {id, done}), so a bulk change is one tool call
delete_todo	Presses the Delete button of several todos (todos: a list of {id})

The todo tools take their work as a list, so a batch ("untick all of them") is one tool call: the chatbot is capped in the tool calls it may make in a turn. A batch is checked in full before its first change is made, and then made one todo at a time; if one fails part way, the error says how many were done.

Each tool dispatches the same re-frame events the views do and waits for the result to show up in the app-db, so what the agent sees is what the page shows, server errors included. There is no update_todo with a title as on the server, because the page can't edit one.

Next to the tools the page registers prompts and resources (prompts and resources in webmcp.cljs), so a client's lists of them are not empty. They carry the same prompt engineering as the chatbot, from shared/tbd/agent/prompt.cljc (a .cljc, on the classpath of both the server and the UI build): act instead of offering, chain the calls, take obvious defaults, report in a sentence, and play tic-tac-toe by the turn. The WebMCP bridge sends no server-level instructions, so this is how Claude or Codex learns them:

Prompt	What it is
drive_the_app	The instructions, then the task you gave it (Claude Code lists it as a slash command). Without a task, the agent asks what you want
play_tic_tac_toe	Plays a game with you, one move on its own turn, as side (x or o, default o)

Resource	What it is
webmcp://app/guide	The instructions, as Markdown: read this first
webmcp://app/state	JSON: the page that is open, the pages (which need a login) and who is logged in

The bridge prefixes their names with the page's channel, like the tools'. A client does not read a resource on its own: mention it (@) or ask the agent to read the guide.

To connect (the page's "Browser tools (WebMCP)" section says the same):

1. Add the WebMCP server to your MCP client, once: npx -y @jason.today/webmcp@latest --config claude (restart Claude Desktop afterwards).
2. Ask the client to make a webmcp token.
3. Click the blue square in the bottom right corner of the page, paste the token, and press Connect. The token works once, and the page has no field for it: the widget is where it goes.
4. The tools show up in the client, prefixed with the page's host.

Development

Every task is a bash script in script/. Start an nREPL server (with CIDER middleware) on the port in .nrepl-port, or 3000 if that file doesn't exist:

script/repl

Tests

Task	What it does
script/unit-test	Runs the Clojure tests in test/
script/ui-test	Runs the ClojureScript tests in ui/test/ under Node, in a jsdom window (needs Node and a JDK)
script/integration-test	Builds the app image, starts it on a random port, runs the integration suite against it, then tears it down
script/test	UI tests, unit tests, then integration tests (stops at the first failure)
script/docker-unit-test	Same as script/unit-test, but inside the app image (Docker only)
script/docker-ui-test	Same as script/ui-test, but inside a Node + JDK image (Docker only)

The UI tests render the real app in jsdom against a fake server and drive it like a user (type, submit, click), next to plain tests of the re-frame events and subscriptions, the game rules and the WebMCP tools (ui/test/tbd/ui/; the shared fakes are in test_util.cljs). ui/test/dom-setup.cjs gives them a window and the real WebMCP library, and fails a run that hangs, rejects a promise nobody handles, or ends without a result (a test that never calls done would otherwise pass or stall silently). npm test in ui/ preloads it, so use that (or the scripts) rather than running the build's output directly.

Coverage

Line coverage of the Clojure code via Cloverage, and of the UI via c8:

Task	Output
script/unit-coverage	target/coverage/unit/
script/integration-coverage	target/coverage/integration/
script/ui-coverage	target/coverage/ui/ (V8 coverage of the compiled UI, mapped back to ui/src/**/*.cljs through the source maps)
script/coverage	Runs all three, then writes the merged Clojure report to target/coverage/merged/ (the UI's stays separate)
script/docker-unit-coverage, script/docker-ui-coverage, script/docker-coverage-report	Dockerized versions of the unit run, the UI run and the merge step, so script/docker-ui-coverage, script/docker-unit-coverage, script/integration-coverage, script/docker-coverage-report is script/coverage with no local Clojure, Node or Babashka (this is what CI runs)

Each report directory has an index.html and an lcov.info. The UI's numbers are approximate where one ClojureScript form compiles to several JavaScript blocks (a case or cond, say): a line can show as uncovered when only part of the form ran. See integration-test/README.md for how integration coverage is collected.

Project layout

src/tbd/
  core.clj            Router, Ring app, and server startup
  <group>/routes.clj  Each route group (health, math, ...) owns its route subtree
  <group>/core.clj    Business logic for the group, with no HTTP concerns
  <group>/schema.clj  Malli schemas of the group's input, shared by its routes and the MCP tools
  auth/               Gitea OAuth (core), session + admin middleware (middleware), /auth routes
  mcp/                MCP server (PluMCP) mounted at `/mcp`: tools (tools.clj), Malli-to-tool glue (schema.clj) and the Ring handler (routes.clj)
  oauth/              tbd as the OAuth authorization server for MCP clients: rules (core), SQLite storage (db), `/oauth` + discovery routes, and the bearer-token middleware guarding `/mcp` (middleware)
  chat/               The chatbot: request to the LM Studio server, tool calls included (core), the request's Malli schema (schema), `/api/chat` route, per-turn logging and loop detection (trace)
  nrepl/              On-demand nREPL server (core) and the admin-only `/nrepl` routes
  todo/               Per-user todos: pure rules (core), Malli schemas of the input (schema), SQLite storage (db), `/api/todos` routes
shared/                .cljc read by both the server and the UI (tbd.agent.prompt: the instructions an LLM is given)
ui/                   ClojureScript re-frame app (shadow-cljs project); ui/src/tbd/ui/{db,events,subs,views,webmcp,model_context,core}.cljs, tests in ui/test/
.env-template         Settings the server reads from the environment; copy to .env (git-ignored)
resources/public/     index.html (the shell served at /) and js/ (compiled UI output, git-ignored)
test/                 Unit tests
integration-test/     Dockerized integration tests and coverage harness
script/               Bash entry points (server, ui-build, ui-test, test, coverage, repl, ...) and coverage helpers

To add a new group, create tbd.<group>.routes with a routes fn that returns a reitit route vector, then add it to the router in src/tbd/core.clj. To make a route admin-only, put :admin? true in its route data (admin allowlist), or :login? true (any logged-in user; the user is passed to the handler as :user). A group with storage keeps it in its own tbd.<group>.db, apart from its business logic in core.clj.