abap2UI5 MCP Server
OfficialThe abap2UI5 MCP Server gives AI coding agents a local, no-SAP abap2UI5 development loop: write ABAP, validate/screenshot views, deploy, build, run, inspect, and test apps.
Discover and scope:
capabilities,scope_of,generation_rules,setup_status.Validate views fast:
validate_view,fix_view,screenshot_view.Deploy and manage apps:
deploy_app,read_app,remove_app.Build the transpiled Node backend:
build_backend,build_log.Run and visually check apps:
run_app,interact_app; operate apps via snapshots likeapp_start,app_describe,app_act,app_list.Test and verify:
run_unit_tests,verify_app.Learn/build resources:
app_guide,api_reference,examples,read_example,pitfalls,docs_search,scaffold_app,add_agent_setup,migrate_report.Manage the backend and integrate with clients:
backend; stdio MCP install for Claude Code, Cursor, VS Code; MCP Apps/Adaptive Cards chat screens.
Provides tools for developing abap2UI5 applications for the SAP platform, enabling AI agents to validate UI5 views, deploy ABAP classes, build transpiled backends, and run apps in a headless browser to capture screenshots and errors.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@abap2UI5 MCP ServerCreate an ABAP class with a table, validate it, deploy, build, and run with screenshot."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-server
The MCP server for abap2UI5 — gives any AI coding agent (Claude Code, Cursor, VS Code Copilot, or any MCP client) the full abap2UI5 development loop, without an SAP system:
examples -> app_guide -> validate_view + screenshot_view -> deploy_app -> build_backend -> run_app -> pitfalls
(has somebody (how an app (SECONDS, no system: (write ABAP, (transpile (boot headless, (what a green
built it is built) is the view legal, lint) to Node) errors + run still
already?) and what does it LOOK like) SCREENSHOT) does not prove)The agent writes an ABAP class, validates the view and looks at a picture of it in seconds, deploys it, boots it in a real browser and looks at the running app — then iterates. Everything runs locally on infrastructure that already guards the abap2UI5 ecosystem in CI: the abaplint transpiler + open-abap runtime, the framework's express shim, the samples-controls build and boot gates, and the linter validation core.
Documentation
→ The MCP server, in full — what MCP means here, the three setup levels and what each one buys, how to register the server with your client, every tool with what the agent gets from it, and the loop they are meant to be used in.
→ Building with AI — the whole AI setup in rising order of effort. This server is the top rung; the cheaper ones matter first.
Related MCP server: @ui5/mcp-server
Quick start
One-click install
Every button registers the same stdio server, named abap2ui5:
{"command":"npx","args":["--yes","-p","@abap2ui5/mcp-server","abap2ui5-mcp"]}The badges go through a web redirect because GitHub does not render links to
an editor's own URL scheme; the direct links, to paste into a browser or
xdg-open/open, are:
vscode:mcp/install?%7B%22name%22%3A%22abap2ui5%22%2C%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22--yes%22%2C%22-p%22%2C%22%40abap2ui5%2Fmcp-server%22%2C%22abap2ui5-mcp%22%5D%7D
cursor://anysphere.cursor-deeplink/mcp/install?name=abap2ui5&config=eyJjb21tYW5kIjoibnB4IiwiYXJncyI6WyItLXllcyIsIi1wIiwiQGFiYXAydWk1L21jcC1zZXJ2ZXIiLCJhYmFwMnVpNS1tY3AiXX0%3D(vscode-insiders: in place of vscode: for Insiders.) Claude Code:
claude mcp add abap2ui5 -- npx --yes -p @abap2ui5/mcp-server abap2ui5-mcpClaude Code plugin. The abap2UI5 plugin bundles the framework's agent skills (building an app, the ABAP and UI5 pitfall catalogues, the view-chain layout, ...) together with this server, registered the same way as above — one install for both:
/plugin marketplace add abap2UI5/abap2UI5
/plugin install abap2ui5@abap2ui5Releases from 1.0.0 on are also listed in the
official MCP Registry as
io.github.abap2UI5/mcp-server (server.json), for clients
that install from the registry.
That is level 1 below: validate_view and fix_view work out of the box,
and setup_status tells the agent what else works on this machine and what
is missing.
What each install brings
Level 1 — validate_view, fix_view and screenshot_view, the tools most
work happens at. They need the linter,
which the server declares as a peer dependency (@abap2ui5/linter,
>=0.8.0 <0.10.0 — the range this server is built against). npm 7+ installs a
non-optional peer by itself, so the one-liner below brings the linter along
and validate_view/fix_view work out of the box. The render gate behind
screenshot_view needs the UI5 libraries and Playwright on top
(@abap2ui5/linter-render, declared as an optional peer: the compatible
range is stated, the package is NOT installed for you — plan on
~150–200 MB and a few minutes the first time you add it):
claude mcp add abap2ui5 -- npx --yes -p @abap2ui5/mcp-server abap2ui5-mcp # validate_view, fix_view
claude mcp add abap2ui5 -- npx --yes -p @abap2ui5/mcp-server -p @abap2ui5/linter-render abap2ui5-mcp # + screenshot_viewThe server looks for the linter, in this order: AI_VIEW_CHECK_HOME; a
linter checkout next to the server (which is also where npm puts the peer
for an npx run: node_modules/@abap2ui5/{mcp-server,linter}); the project it
is started in (node_modules/@abap2ui5/linter — app-template has it as a
devDependency, so npm install there is all it takes); the server's own
node_modules; whatever Node's module resolution finds from the server's
location. A set env var decides alone. Or work from a checkout:
git clone https://github.com/abap2UI5/linter # AI_VIEW_CHECK_HOME
cd linter && npm ciThe server itself is on npm, so it needs no checkout; the registration above
is the whole install. (With npm 6 or --legacy-peer-deps, add
-p @abap2ui5/linter yourself.)
(-p … abap2ui5-mcp names the bin explicitly, which every published version
answers. The shorter npx --yes @abap2ui5/mcp-server needs a bin named after
the package, which 0.2.0 and earlier do not have — npx stops there with "could
not determine executable to run".)
(The install is ~47 MB: 19 MB of it a Playwright driver only run_app uses,
2 MB the linter — paid on the first start, cached after. From a checkout instead:
git clone https://github.com/abap2UI5/mcp-server && cd mcp-server && npm ci,
then claude mcp add abap2ui5 -- node /path/to/mcp-server/server.mjs.)
Cursor, VS Code and Claude Desktop take the standard stdio shape — the documentation has the JSON, and the two further levels (the sample catalogues and deploying, then the headless build-and-boot loop). A tool whose prerequisites are missing answers with a message naming what it needs; the server starts either way.
The abap2UI5 VS Code extension registers this server for you, and adds a second one of its own for the tools that need a real SAP system.
Everything in one command — for a machine (or
Codespace) dedicated
to the full build-and-boot loop, setup.sh clones the framework,
corpus and linter checkouts next to this repo, installs their dependencies and
the headless browser (existing checkouts are reused, safe to re-run):
git clone https://github.com/abap2UI5/mcp-server && ./mcp-server/setup.shsetup.sh --no-corpus clones the framework and the linter only, which is
the whole loop for an app of your own since the framework sandbox exists;
the corpus is for scope_of, the full build and the locally served UI5.
A Claude Code started inside the checkout picks the server up automatically
via the committed .mcp.json; the
devcontainer runs the same setup on create.
Level 0 — no checkout at all. The knowledge tools (app_guide,
api_reference, pitfalls, capabilities, examples, read_example,
docs_search, scaffold_app, generation_rules) read committed files, so
when no checkout resolves and no env var is set they read them from GitHub
instead: the files land in a per-user cache (<tmp>/abap2ui5-mcp-remote, a
day at a time - used only while it is a directory of the user's own, created
0700; A2UI5_MCP_REMOTE_DIR moves it) that the server treats as a read-only checkout; add_agent_setup
reads the template from there too, and writes only into the project it is
given. And the
expensive half runs on the npm package @abap2ui5/node-runtime (below), so
npx --yes -p @abap2ui5/mcp-server abap2ui5-mcp in a fresh project covers
deploy_app, build_backend, run_app, interact_app, run_unit_tests
and verify_app too, with no clone of anything. What still needs a checkout
is the corpus' part (scope_of, build_backend mode full, the locally
served UI5). A2UI5_MCP_REMOTE=0 or A2UI5_MCP_OFFLINE=1 switches the
mirror off. The docs mirror lists its pages through GitHub's API, which
allows 60 unauthenticated requests an hour per address; a GITHUB_TOKEN (or
GH_TOKEN) in the environment raises that and is sent to api.github.com
only.
The backend without a framework checkout: @abap2ui5/node-runtime.
The framework is published on npm already transpiled, with the ABAP sources
of the same release commit to transpile apps against. With no abap2UI5
checkout (and none configured), build_backend installs that package once
per release into ~/.abap2ui5-mcp/runtime/<version> — with the transpiler
it names, express, and app-template's abaplint for the lint, at exact
versions with a lockfile, --ignore-scripts — fetches open-abap-core at the
commit the release was built with, and then transpiles only the deployed
apps against it: seconds, never the framework. deploy_app writes into
~/.abap2ui5-mcp/sandbox (and lints with app-template's own config against
the package's sources), run_app boots the package's server with the apps
registered, run_unit_tests runs the apps' own tests. The release is the
registry's latest (asked through npm, cached a day) unless
A2UI5_MCP_RUNTIME_VERSION pins one. Measured here with a cold npm cache:
the first install about 10 s, open-abap-core about 1 s, then 7–8 s per build
and about a second for the unit tests. setup_status shows the release, the
workspace and what the next build would do.
With a framework checkout (A2UI5_HOME or a sibling) everything works
as before: build_backend downloads the release's prebuilt backend
(backend-<version>.tar.gz) into the checkout the first time and
re-transpiles the deployed apps from its node/zz_dev afterwards.
A2UI5_MCP_BACKEND=npm runs on the package beside a checkout;
build_backend mode prebuilt (or A2UI5_MCP_BACKEND=clone) clones the
release into ~/.abap2ui5-mcp/abap2UI5 instead — the path this server took
without a checkout until the package existed. The full corpus build stays
available as mode: "full" for a checkout on an unreleased commit.
Tools
Every tool reads live from a sibling checkout, and each one needs a specific
sibling — there is no "optional" repository, only tools you do or do not use.
The Needs column says which checkout a tool is dead without: the linter
alone carries validate_view, fix_view and screenshot_view (the fast
loop, where most iterations happen — and the one dependency npm installs with
the server, as its declared peer), the framework checkout carries the guide, the pitfalls and
the interface (all three mirrored from GitHub when it is absent), the npm
package @abap2ui5/node-runtime carries the backend when no framework
checkout is there, and the corpus carries almost everything else. A tool
whose checkout is missing answers with the clone command and env var that
fix it.
Tool | What it does | Needs |
| What resolves, what is built, what is missing and how to fix it — one read, call it first | nothing |
| Whether abap2UI5 can express a UI5 feature at all, from the verified capability map | samples-controls |
| How to build an app, live from the framework checkout | abap2UI5 |
| The client API ( | abap2UI5 |
| The files a new project starts from, live from app-template; | app-template |
| Set up an existing project for AI work — what | app-template |
| Search the three sample catalogues, verification status and all — answers with a class to read, never a snippet to trust | any of samples / samples-controls / samples-stack (or the GitHub mirror) |
| Read the source of a sample an | the sample's repository (or the GitHub mirror) |
| Full-text search over the documentation site's pages: page, heading, snippet and the published URL | docs |
| The rulebook for porting a UI5 demo-kit sample into samples-controls | samples-controls |
| The defects a green run does not catch: | abap2UI5 |
| In/out-of-scope verdict for a UI5 control | samples-controls + an OpenUI5 checkout |
| The linter's gates in seconds, judged by your project's own | linter |
| Apply the linter's mechanical fixes and get the corrected source back — writes nothing | linter |
| See the view in seconds — no build, no backend | linter |
| Write the class + abapGit sidecar (+ test include) into the dev sandbox, then abaplint it | nothing (the npm backend's |
| Read a deployed dev app's source back, and whether the built backend already carries it | the sandbox (as deploy_app) |
| Get the transpiled Node backend: without a checkout | nothing (npm), or abap2UI5 ( |
| Page through the last build's full output — the error the result's short tail cut off | nothing (reads the record the last build left) |
| Boot an app headless: status, real page errors, and a screenshot | a build (npm backend or abap2UI5 checkout); samples-controls serves UI5 locally when present, the CDN otherwise |
| Boot an app, then click, fill, press and wait through a short script — the event branch, photographed | a build (same as run_app) |
| The app classes the built backend can start — the deployed dev apps and the framework's own | a build (npm backend or abap2UI5 checkout) |
| Start an app and get its agent snapshot: fields (path, label, kind, value), actions (event + arguments), tables, messages — over the abap2UI5 JSON protocol, no browser; in an MCP Apps host also an interactive screen, with | a build (same as run_app) |
| The current agent snapshot of a session, from memory — no roundtrip | a session from app_start |
| Fill fields and fire an event by name — validated against the snapshot, sent as the real model delta — and get the next snapshot | a session from app_start |
| Run the deployed apps' test classes (on a checkout: or the whole transpiled tree) in the open-abap runtime: assertions, not pictures | a build (npm backend or abap2UI5 checkout) |
| The whole loop in one call — validate, deploy, build, unit, boot — stopping at the first stage that fails | what the stages need |
| Convert a classic ABAP report into an abap-cloud-gui app class (report2cloud): the class files, the migration report (TODOs, unreleased tables with successors), the refusals with | abap-cloud-gui (with |
|
| a build (start/restart; status and stop always work) |
| Delete a dev app from the sandbox, or list the deployed ones | the sandbox (as deploy_app) |
verify_app is the loop in one call: validate, deploy, build, unit tests and
boot, stopping at the first stage that fails and reporting every stage before
it. interact_app is run_app with hands: after the boot it clicks, fills and
presses through a short script and photographs the result, which is how the
event branch of an app becomes visible without a system; run_unit_tests
runs the test classes deploy_app wrote beside the app (a local
z2ui5_if_client double, see the app guide's chapter 9) in the open-abap
runtime and answers with assertions.
scaffold_app and add_agent_setup both execute app-template's own
template.json, for the two kinds of project. scaffold_app hands back a
whole NEW project, app class included, and writes nothing. add_agent_setup
is for the abapGit repository that already exists - most of them never
started from the template - and does what
npm create abap2ui5-app -- --agent-setup does there, by the template's
agentSetup key: it writes the agent setup and the two gates into
project_dir (default: the server's
working directory, when that looks like a project), never overwrites a file
the project has (each one is listed as skipped), merges package.json and
.gitignore by only adding what is missing (each value kept that differs
from the template's is a warning), points the configs at the source folder
.abapgit.xml names and writes nothing into it. A second run changes
nothing; dry_run: true answers the same report without writing. Every path
it writes comes from the template's file list and is checked first - a plain
relative path, a shared file of the template, outside the source folder; one
that fails refuses the whole call before anything is written. A symbolic
link, or a folder that leads out of the project through one, is never
written through: that file is skipped, and the answer says why.
Every answer fits the client. A client shows the agent nothing of a
tool result over its cap (Claude Code: 25,000 tokens), so no answer is
larger than about 60,000 characters. The tools whose answers grow with
upstream content page instead of cutting: matches counts every hit,
returned the page, and more names the exact call for the rest
(offset - examples, capabilities, api_reference, app_guide,
pitfalls, docs_search; files - scaffold_app; from_line -
read_app, read_example; build_log names its next offset under
cut). A list that is shortened rather than paged says so in a ...Cut
field (findingsCut, rulesCut, errorsCut, ...), and a last-line backstop trims anything that still
passes the cap and marks it (__answerGuardCut). The resources follow the
same budget (below).
examples degrades per catalogue instead of failing: it searches the
checkouts it finds and names the ones it could not, so a thinner answer never
reads as "nobody has built this". It reads each repository's committed
catalogue.json where the checkout has one — which is what carries a control
port's verification status (checked over reviewed over generated, used to
break ranking ties), the learning-path stage, and what a stack sample needs
from the system — and falls back to parsing SAMPLES.md on a checkout from
before that file existed. Where samples and samples-controls commit
catalogue-derived.json beside it, a query also matches the control types
each sample's view builds, so sap.m.Dialog finds every sample that builds
one, not only those filed under it. screenshot_view and run_app answer the
same question at three orders of magnitude apart: the first photographs the
reconstructed view with no backend, the second the running app after a
build. Most iterations should end at the first.
Operating an app without a browser
app_start, app_act and app_describe make every abap2UI5 app
agent-operable: they speak the JSON protocol the UI5 frontend speaks — the
app start, the event with its arguments, the model delta of what was typed —
against the local backend, and answer with an agent snapshot derived from
the response's view XML and model: the fields an agent may fill (model path,
label, kind, current value, choice values, editable), the actions it may fire
(event name, static and row-dependent arguments), the tables (columns, the
first rows, selection - a SelectDialog/TableSelectDialog is a table too, and
app_act with row picks from it), the messages (toast, message box,
MessageStrip, field value states, MessagePopover and MessageView items) and
some static text. An act is validated against the snapshot
before anything is sent: an unknown event, a field that is not on the screen
or not editable, a choice outside its values is refused with the list of what
is allowed. A short session against z2ui5_cl_smp_app_009 of
abap2UI5/samples:
app_start { app: "z2ui5_cl_smp_app_009" }
-> fields f1..f5 (f3 "Input with value", /S_SCREEN/COLOR_02, text, ""),
actions a1 POPUP_TABLE_VALUE (valueHelpRequest of f3), ..., a5 BUTTON_SEND
app_act { session, values: { f4: "Smith" }, event: "POPUP_TABLE_VALUE" }
-> layer "popup", table t1 /T_SUGGESTION_SEL (6 rows, Single, editableCells [SELKZ]),
action a1 POPUP_TABLE_VALUE_CONTINUE
app_act { session, values: { "/T_SUGGESTION_SEL/2/SELKZ": true }, event: "POPUP_TABLE_VALUE_CONTINUE" }
-> layer "main", f3 = "BLACK", f4 = "Smith", message { toast, "value selected" }The snapshot shape is shared with the VS Code extension and the ABAP agent
addon; docs/agent-snapshot.md is its reference —
the derivation rules, the operations, the deviations from the original
contract and what the snapshot cannot see yet. interact_app stays the tool
for what only a browser shows (the rendered page, client-side behaviour).
The screen in the chat: MCP Apps and Adaptive Cards
The snapshot is text for the agent. Two optional surfaces show the same
screen to the user in the chat, and send what the user does back through
app_act - the same validation as the agent's own acts, so nothing the
snapshot does not allow can be fired, and the agent sees the act:
MCP Apps (SEP-1865, stable 2026-01-26, extension
io.modelcontextprotocol/ui).app_start,app_describeandapp_actname the UI resourceui://abap2ui5/app-screen(_meta.ui.resourceUri, MIME typetext/html;profile=mcp-app) when the client advertises the extension in itsinitialize. A host that renders MCP Apps (the extension's own README lists Claude, ChatGPT, VS Code and Goose) shows it as a sandboxed iframe beside the result: fields to fill, buttons, row actions, table cells, messages. A click is atools/callofapp_actthrough the host, built from the snapshot on screen; afterwards the page tells the model what happened (ui/update-model-context) and renders the new snapshot. The page is one self-contained HTML document - no external URL, no network, no eval - so it runs under the spec's restrictive default CSP; it needs nothing else.A2UI5_MCP_UI=ondeclares the screen for a host that renders MCP Apps without advertising the extension,offnever; the tools' text answers do not change either way.Adaptive Cards for Copilot/Teams-style hosts:
format: "adaptive-card"onapp_start,app_describeandapp_act(orA2UI5_MCP_APP_FORMAT=adaptive-cardfor every call) adds the screen as an Adaptive Card 1.5 - an embedded resource of typeapplication/vnd.microsoft.card.adaptiveafter the unchanged snapshot - rendered by abap2UI5/protocol's renderer (vendored underlib/vendor/adaptive-cards). Off by default. EachAction.Submitcarriessession(androwfor a row's action); a submitted payload maps to oneapp_actcall:eventandrowas they are, every input whose value changed asvalues["<binding path>"], a message box button as the box's close action with the button as its$actionargument, the popup/popover close wire as@CLOSE_POPUP/@CLOSE_POPOVER(cardSubmitToActinlib/adaptive-card.mjs; the full table is in docs/agent-snapshot.md).
Migrating a classic report
migrate_report takes the source of a classic report (REPORT,
PARAMETERS, SELECT-OPTIONS, the event blocks, a WRITE list, an ALV) and
its .prog.xml text pool, and answers the class of the
abap-cloud-gui addon
that keeps its programming model — selection_screen( ),
start_of_selection( ), write( ), alv( ), message( ) — together with
the migration report (the TODOs, the tables and APIs not released on ABAP
Cloud with their successors, what the list does not carry over) and the
statements it refused with file:row:col (dynpros, batch input, SUBMIT,
native SQL). A report that carries open-abap's @KERNEL escape (WRITE '@KERNEL ...'., JavaScript once transpiled) is refused before anything is
converted, and a dynamic WHERE (cond) is converted but flagged under
warnings - the runtime evaluates it with eval() (SECURITY.md). The
converter is the addon's own report2cloud, run from a checkout of the
addon — it is not on npm:
git clone https://github.com/abap2UI5-addons/abap-cloud-gui # ABAP_CLOUD_GUI_HOME
cd abap-cloud-gui && npm ciWith deploy: true the class is also written into the dev sandbox together
with the addon's runtime (src/01, its database tables included - the
backend creates a deployed table at boot) and the popups it calls (a checkout of
popups at POPUPS_HOME, else
the addon's .deps/popups, ../popups beside it or the addon's
build/popups, in that order), the backend is
built, and the answer carries app_start's snapshot of the selection screen —
app_act with CGUI_EXECUTE runs the report. The database tables a report
reads are not in the local backend: the screen runs, a run that reads them
does not.
System mode: operating apps on a real SAP system
Everything above runs without a system. With A2UI5_MCP_SYSTEM_URL set, the
same server is something else: the app tools against a real SAP system,
logged on as the configured user — for MCP clients that are not the VS Code
extension (whose own system server does this for the clients of its window):
Claude Desktop, Claude Code, Cursor, any stdio client. The sandbox tools are
not offered in this mode; register the server twice, once with and once
without the variable, to have both.
Tool | What it does |
| The endpoint and user (never the password) and ONE request that shows whether the host answers, the certificate is accepted and the logon works — |
| Class names on the system, from the ADT quick search ( |
| Start an app on the system and get its agent snapshot — the same snapshot, arguments and refusals as the sandbox's |
| The current snapshot of a session, from memory — nothing is sent |
| Fill fields and fire an event, validated against the snapshot, sent as the real model delta — runs for real |
Claude Desktop (claude_desktop_config.json: Settings → Developer → Edit
Config), the sandbox server beside the system one:
{
"mcpServers": {
"abap2ui5": {
"command": "npx",
"args": ["--yes", "-p", "@abap2ui5/mcp-server", "abap2ui5-mcp"]
},
"abap2ui5-dev-system": {
"command": "npx",
"args": ["--yes", "-p", "@abap2ui5/mcp-server", "abap2ui5-mcp"],
"env": {
"A2UI5_MCP_SYSTEM_URL": "https://host:44300/sap/bc/z2ui5?app_start={class}&sap-client=100",
"A2UI5_MCP_SYSTEM_USER": "DEVELOPER",
"A2UI5_MCP_SYSTEM_PASSWORD_CMD": "security find-generic-password -s abap2ui5-dev -w"
}
}
}
}Claude Code, the same as one command:
claude mcp add abap2ui5-dev-system \
-e A2UI5_MCP_SYSTEM_URL='https://host:44300/sap/bc/z2ui5?app_start={class}&sap-client=100' \
-e A2UI5_MCP_SYSTEM_USER=DEVELOPER \
-e A2UI5_MCP_SYSTEM_PASSWORD_CMD='security find-generic-password -s abap2ui5-dev -w' \
-- npx --yes -p @abap2ui5/mcp-server abap2ui5-mcpVariable | |
| The launch URL as the VS Code extension's F9 knows it, |
| The SAP user (Basic authentication) |
| Its password — in the client's configuration file, in plain text |
| Instead: a shell command that prints the password, run once and kept in memory. macOS keychain: store it with |
|
|
What to know before switching it on:
It acts for real. An
app_actevent runs on the system as the configured user and may save, post or delete data. Use a development system and a user whose authorizations fit what the agent may do; the agent sees only what that user may see.A rejected logon is sent once. SAP locks a user after a few failed logons, and an agent retries. After a
401nothing more is sent with that password — every tool says so — until the server is restarted with the corrected one (Claude Desktop: quit and reopen), or the password command answers a different one.Basic authentication only. SAML, X.509 or SSO logons are not supported in this mode; the VS Code extension's system server (its auth proxy) is the way there.
One system per registration. Two systems are two entries with two names.
No proxy support. The requests go straight to the host;
HTTPS_PROXYis not read.The protocol client is the sandbox's (
lib/appclient.mjs): it does the CSRF token handshake and keeps the stateful session'ssap-contextiditself; this mode adds the logon, the system's cookies and the start location (lib/system.mjs).app_listneeds the ADT services and the user's authorization for them —app_startworks without both when the class name is known.
Unit tests in CI, without a system
The same runtime runs an app repository's ABAP Unit tests in GitHub Actions
(or at a terminal): @abap2ui5/node-runtime at the release the project's
abaplint.jsonc pins, installed once and cached, the classes transpiled
against it, the tests run through the generated runner — no framework
clone, none of the framework's devDependencies, and no npm ci of this
package either (the runner imports none of its dependencies). Against
app-template's starter app, measured on one machine: 16 s cold and 8 s
with the cache, where the clone of 0.2.0 took 30 s and 23 s and left a
231 MB workspace instead of 67 MB.
- uses: abap2UI5/mcp-server@v1
with:
paths: srcframework: X.Y.Z pins another release than the project's, backend: clone takes the old path (the release cloned, its backend downloaded or
built) — which is also what a pin older than the package (1.145.0) gets by
itself.
@v1 is a floating major tag: the release workflow moves it to every 1.x
release it publishes, so a workflow naming it picks up fixes without
picking up a breaking change. It is created by the 1.0.0 release; until that
release is out, pin @v0 (0.3.0, where it stays) or a release tag such as
@v0.3.0.
npx -p @abap2ui5/mcp-server abap2ui5-unit src # the same, locallyEvery class and interface under paths is deployed with all of its files
(an app or not; test and local-class includes too), and every test include
runs. On the package the run uses a sandbox and a build of its own, so an
MCP session's apps on the same machine are neither part of it nor touched by
it. The result is the job's verdict plus a step summary naming every test
method and the first failure; an object that cannot be deployed (a
namespaced name) fails the run rather than leaving its tests out. app-template
ships the job in its check.yml and the command as npm run test:unit. What
the runner cannot see is what the open-abap runtime cannot model (see
pitfalls, area abap); a test that passes here passes on the system short of
that, and a PARTIALLY IMPLEMENTED test double has to implement every method
the code under test calls, because the runtime generates no empty stubs.
Resources
The knowledge documents behind those tools are also MCP resources, for
clients that surface them (context pickers, attach-a-document UIs) and for
agents that want a document whole instead of sliced. Same live reads from the
same sibling checkouts: listing is free (no checkout needed), reading a
resource whose checkout is missing answers with the same actionable error the
tool gives. A document longer than a client shows of one read (about
60,000 characters - today the abap-check catalogue and the capability map)
is cut at a section heading, and its last line names the tool call that
reads on from the first section left out (pitfalls { area, offset },
app_guide { offset }, capabilities { status }).
The one resource that is not a document is ui://abap2ui5/app-screen, the
MCP Apps screen of the app tools (see "The screen in the chat" above); it is
part of this server and needs no checkout.
Resource | Content | Needs |
| The app-building guide, whole ( | abap2UI5 |
| One guide chapter, by number or heading keyword (a resource template) | abap2UI5 |
| The client API summary — every | abap2UI5 |
| abap-check — the ABAP defects a green CI does not catch | abap2UI5 |
| ui5-check — the view defects a green CI does not catch | abap2UI5 |
| CAPABILITIES.md — the verified capability map | samples-controls |
| The rulebook for porting a UI5 demo-kit sample | samples-controls |
| The MCP Apps screen of the app tools ( | - |
Prompts
Two prompts — one per job this server serves — put an agent straight into the loop instead of leaving it to reconstruct the order from the tool descriptions alone. Each renders an orchestration script over the tools above and duplicates none of their content:
build-an-abap2ui5-app(argument:task, what the app should do) — orient withexamples/capabilities, learn the shape fromapp_guide, write the class, iterate throughvalidate_view/screenshot_viewin seconds, prove it withdeploy_app→build_backend→run_app, close withpitfalls.port-a-ui5-sample(argument:sample, the demo-kit sample) — the corpus job:generation_rulesas the brief,scope_ofandcapabilitiesbefore writing, neighbouring ports fromexamples, then the same validate/screenshot/deploy/run loop.
Notes
Dev sandbox: deployed apps land in the samples-controls checkout's gitignored
src/zz_dev/, else the abap2UI5 checkout's gitignorednode/zz_dev/, else~/.abap2ui5-mcp/sandbox(A2UI5_MCP_WORKSPACEmoves it) — nothing an agent deploys can leak into a commit.Backend: without a framework checkout the npm package
@abap2ui5/node-runtime—A2UI5_MCP_RUNTIME_VERSIONpins its release (default: the registry's latest, asked once a day),A2UI5_MCP_BACKEND=npmuses it beside a checkout too,A2UI5_MCP_BACKEND=clonerestores the framework clone as the default. Everything it installs lives in~/.abap2ui5-mcpand is safe to delete.Port: the backend listens on 3000 (
A2UI5_MCP_PORToverrides).The lint:
deploy_applints with app-template's abaplint config. On the npm backend a long-lived worker keeps@abaplint/core(installed beside@abaplint/cliat the same version) with the framework parsed, so a deploy's lint takes milliseconds instead of a fresh abaplint process (2-3 s, mostly the framework's parse); whenever the worker cannot stand in for the CLI - a checkout without that core, a config it does not mirror - the CLI runs as before.A2UI5_MCP_LINT_WORKER=0switches the worker off;A2UI5_MCP_LINT_IDLE_MS(default 10 minutes, 0 = never) is how long it is kept without a request - with the framework parsed it holds about 300 MB - before it exits and the next lint starts a fresh one.Timeouts: every spawned child is killed (whole process tree) when it exceeds its limit — lint/scope 5 min, unit tests 10 min, build (with the prebuilt download and the npm install) 30 min by default;
A2UI5_MCP_LINT_TIMEOUT_MS,A2UI5_MCP_SCOPE_TIMEOUT_MS,A2UI5_MCP_UNIT_TIMEOUT_MSandA2UI5_MCP_BUILD_TIMEOUT_MSoverride (values in ms).Download limits: what is read from GitHub is capped by size as well as by time - 8 MB for a file of the read-only mirror, the docs tree listing and the release list (the largest today is 0.34 MB), 200 MB for the prebuilt backend archive (2.7 MB for 1.146.0). An answer over the cap is refused (when it declares its size) or cut at the cap, and the tool says so by URL; a cached mirror or the previous build stays in place.
UI5 sources are served from the samples-controls checkout's
@openui5packages when that checkout is there, so booting needs no network; without itrun_appandinteract_appload UI5 from the CDN (and say so at once when it cannot be reached, instead of waiting out the boot timeout). The built theme CSS is not in those packages — with network access it loads from the CDN (styled screenshots); without, apps render unstyled but structurally complete. What the CDN answers is kept in memory across calls (A2UI5_MCP_UI5_CACHE_MB, default 64; 0 switches it off), so a second boot does not download UI5 again.A2UI5_MCP_OFFLINE=1forces the hermetic behaviour.Chromium:
A2UI5_MCP_CHROMIUM(orCHROMIUM_BIN, which the linter reads too) names the executable; otherwise the Playwright-managed browser (npx playwright install chromium); otherwise a system chromium.setup_statussays which one it found and where it came from.validate_view's warm render gate keeps 2 pages of that browser, which concurrent calls queue on;A2UI5_MCP_RENDER_PAGES(1 to 8) sets it.Screenshots:
run_appandinteract_appwrite their PNG to<tmp>/abap2ui5-mcp-screenshots/<class>-<stamp>.png(the newest 50 are kept, older ones of that pattern removed) and return the path beside the image — deliberately not into the install directory, which is insidenode_moduleswhen you install from npm. The last build's log, whichbuild_logreads after a restart, lives there too. That directory is created 0700 and used only while it is your own (/tmpis shared on Linux); otherwise the image is returned but not saved, andscreenshotNotSavedsays why.A2UI5_MCP_SCREENSHOT_DIRputs them somewhere you keep.scope_ofneeds an OpenUI5 checkout as well as the corpus: it reads the JSDoc fromOPENUI5_SRC, or from../fork-openui5beside the samples-controls checkout when that variable is unset.If you set this up earlier: the corpus repository was
ai-demokit, thenabap2UI5-api, and issamples-controlstoday. Nothing needs changing — an existing checkout is still found under any of the three directory names, andAI_DEMOKIT_HOMEis still read alongsideSAMPLES_CONTROLS_HOME.Real-system deployment stays what it is today: abapGit. This server is the inner dev loop; the real-system half lives in the VS Code extension, whose own MCP server exposes it as
run_app_on_system. Both servers are registered in the same editor window, which is why that tool is not calledrun_app.
Working on this repository
npm ci
npm testAGENTS.md carries the conventions, CONTRIBUTING.md and RELEASING.md the
rest of the workflow.
bench/ is abap2UI5-bench: how often an AI agent's
abap2UI5 app passes the template's gates, with and without this server. It is
a package of its own and not part of the published server.
Available Tools
9 toolsbackendA
Manage the local express backend serving the transpiled apps: status | start | stop | restart. run_app starts it automatically; use this for diagnostics or to free the port.
| Name | Required | Description | Default |
|---|---|---|---|
| action | No | default: status |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It mentions actions and port freeing, but does not disclose side effects such as making served apps unavailable when stopping or restarting. This is a significant gap for a management tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately states the resource and actions, with no filler or redundant content. It is optimally concise and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one param, no output schema), but the description lacks details about the status command's output and the impact of stop/restart on running apps. It is adequate for basic understanding but not fully complete given the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides 100% coverage with an enum describing the action parameter. The description merely lists the same enum values without adding extra meaning or details about each action's behavior, so it meets the baseline but adds no new value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Manage the local express backend serving the transpiled apps' and enumerates specific actions (status | start | stop | restart). This distinguishes it from siblings like run_app, which is mentioned as a separate tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit guidance: 'run_app starts it automatically; use this for diagnostics or to free the port.' This tells when to use this tool and names the alternative (run_app) for normal start.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
build_backendA
Rebuild the transpiled Node backend so run_app picks up deployed/edited ABAP. mode auto (default) is incremental when a prior full build exists: only src/zz_dev/ is re-copied and re-transpiled (~1-2 min). mode full runs the complete e2e-build (downport + transpile, tens of minutes) — needed once initially, or when framework/port sources changed, or when the incremental transpile rejects a construct (then simplify the ABAP or go full). Stops a running backend first.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | default auto |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the tool stops a running backend first, explains the two modes (incremental vs full) with timing, and highlights the failure condition for incremental builds. It does not cover error handling or side effects beyond stopping the backend, but it provides substantial behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense paragraph of three sentences. It front-loads the primary purpose and then provides mode details. While not broken into bullets or sections, it is efficient and every sentence conveys necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of rebuilding a backend, the description covers all essential aspects: the modes, timing, prerequisites (prior full build), trigger conditions for full mode, and the fact that it stops a running backend. There is no output schema, but for a build tool this is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only lists 'mode' with enum values and default 'auto.' The description adds rich meaning: it explains what auto does (incremental when prior full build exists), what full does (complete e2e-build), and when to choose each. This goes far beyond the schema's raw enum values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource pair: 'Rebuild the transpiled Node backend' and connects it to the downstream effect 'so run_app picks up deployed/edited ABAP.' This clearly distinguishes the tool from sibling tools like run_app or deploy_app, which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly explains when to use each mode: auto is incremental when a prior build exists, full is needed for initial builds or framework/port changes. It also mentions when incremental fails (rejects a construct) and suggests fallback actions. It lacks an explicit 'use this instead of X' comparison, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
capabilitiesA
Query what abap2UI5 can express, from the verified capability map (CAPABILITIES.md — every entry names a proving port). Call this BEFORE deciding a UI5 feature cannot be built. Without arguments returns a summary; with query returns matching entries (status: direct | workaround | needs-live-test | not-expressible).
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | keywords matched against feature/how/evidence, e.g. "tree binding" or "dialog" | |
| status | No | optional filter on the capability status |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses return behavior: 'Without arguments returns a summary; with `query` returns matching entries' and enumerates status values. It also adds context that the map is verified and every entry names a proving port. No contradictions with annotations (since none exist).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, front-loaded with the core purpose. Each sentence serves a distinct function: what it does, when to use it, and how the arguments affect the response. No filler or redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description explains the two primary return modes (summary vs matching entries) and the status filter. It could provide more detail on entry structure, but for a capability discovery tool this is adequate and the sibling context reinforces its role.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds meaningful behavior: it explains the effect of omitting vs providing `query` and enumerates the `status` options. This goes beyond the schema's bare descriptions and helps the agent understand parameter influence.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'Query what abap2UI5 can express.' The reference to a 'verified capability map (CAPABILITIES.md)' distinguishes this from sibling tools like 'validate_view' or 'build_backend', making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit when-to-use guidance: 'Call this BEFORE deciding a UI5 feature cannot be built.' This is a clear directive. It doesn't name explicit alternatives or when-not-to-use, but the context is strong enough for an agent to know this is the first stop for capability checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
deploy_appA
Deploy an abap2UI5 app: writes .clas.abap (+ abapGit sidecar) into the gitignored dev sandbox src/zz_dev/ and lints it with the repo abaplint config. The class must implement z2ui5_if_app. After deploying, run build_backend once (rebuilds the transpiled Node backend), then run_app to see it. Set lint:false to skip the lint (faster, not recommended).
| Name | Required | Description | Default |
|---|---|---|---|
| lint | No | run abaplint after writing (default true) | |
| class_name | Yes | lowercase class name matching ^z2ui5_cl_..., <= 30 chars, e.g. z2ui5_cl_my_app | |
| abap_source | Yes | full ABAP source of the class (CLASS ... DEFINITION + IMPLEMENTATION) | |
| description | No | short class description (abapGit DESCRIPT) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full disclosure burden. It discloses that it writes to a gitignored dev sandbox, creates a sidecar file, lints using repo config, and requires the class to implement z2ui5_if_app. This gives the agent essential behavioral context for a file-writing operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The three-sentence description is efficient, covering purpose, side effects, prerequisites, and follow-up actions without redundancy. A minor typo 'ababGit' does not detract from overall clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete for a deploy tool: it specifies the target directory, the linting step, the required interface, and the post-deploy build/run sequence. It lacks error-handling details but given the tool's straightforward nature, this is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, but the description adds value by explaining the lint parameter's default and trade-off, and by stating the interface prerequisite for class_name. This goes beyond the schema's basic parameter types and provides decision-relevant guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Deploy an abap2UI5 app' and details the concrete actions: writing a .clas.abap file with a sidecar into a specific directory and linting. It distinguishes from sibling tools by referencing build_backend and run_app as subsequent steps, making the deploy scope explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides workflow guidance, explicitly stating to run build_backend and run_app after deployment, and explains the lint:false flag to skip linting. While it doesn't explicitly list exclusions, it clearly defines where this tool fits in the deployment pipeline compared to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generation_rulesA
The canonical rulebook for writing an abap2UI5 app with the generic view builder (dispatcher skeleton, view/attribute idioms, binding and event rules). Read it once before generating ABAP.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the burden of behavioral disclosure. The 'rulebook' and 'Read it' phrasing imply a read-only, informational tool, but it doesn't explicitly state that it has no side effects or describe the output format. This is adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose and followed by a direct usage instruction. Every word earns its place; no fluff or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple documentation tool with no parameters or output schema, the description is nearly complete. It states what the tool provides, what it covers, and when to use it. The only minor gap is the absence of explicit statement that it's a read-only reference, which would fully satisfy contextual completeness in the absence of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, which warrants a baseline of 4. The description adds context about the content of the rulebook, which is meaningful given no input schema exists.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as 'the canonical rulebook' for writing abap2UI5 apps, specifying the exact areas covered (dispatcher skeleton, view/attribute idioms, binding and event rules). This is specific and distinguishes it from operational siblings like deploy_app and run_app.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The instruction 'Read it once before generating ABAP' provides a clear when-to-use directive. While it doesn't explicitly name alternatives, the context implies this is a prerequisite learning tool, distinct from the other sibling tools that perform actions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
remove_appA
Remove a previously deployed dev app from src/zz_dev/ (takes effect in the served backend after the next build_backend). Without class_name lists the deployed dev apps.
| Name | Required | Description | Default |
|---|---|---|---|
| class_name | No | the dev app class to remove; omit to list deployed dev apps |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that removal takes effect only after the next build_backend, which is important behavioral context, and that omitting class_name lists deployed apps, indicating a non-destructive mode. This adds meaningful detail beyond the obvious 'remove' action, though it could go further (e.g., error states or permanence).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the primary action, followed by the critical timing detail and the alternate usage. Every word earns its place, with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-optional-parameter tool with no output schema, the description covers the main function, the deferred effect, and the list mode. It does not describe return values or error behavior, but given the low complexity and schema richness, this is not a critical gap. The tool's dual nature is fully explained, making it largely complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already fully describes the class_name parameter, including the 'omit to list' behavior, achieving 100% coverage. The description's mention of 'Without class_name lists the deployed dev apps' largely duplicates the schema text, adding no substantial new meaning. Thus a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Remove a previously deployed dev app'), specifies the resource location ('from src/zz_dev/'), and distinguishes itself from siblings like deploy_app and build_backend by noting the deferred effect. The alternate listing behavior ('Without class_name lists the deployed dev apps') adds further specificity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (to remove a previously deployed dev app) and notes the dependency on build_backend for the effect to take place. It does not explicitly mention when not to use it or name alternatives, but the dual listing behavior implies the useful single-argument use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_appA
Boot an app class headless in Chromium against the local backend (?app_start=) and LOOK at it: returns booted/ok, real page errors + failed backend calls (benign UI5 noise filtered), and a full-page screenshot as an image. The visual verification step of the loop — also works for the 276 existing ports and z2ui5_cl_ai_app_overview.
| Name | Required | Description | Default |
|---|---|---|---|
| class_name | Yes | the app class to start, e.g. z2ui5_cl_my_app or z2ui5_cl_ai_app_005 | |
| timeout_ms | No | boot timeout in ms (default 60000) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses key behaviors: runs headless, filters benign UI5 noise, returns page errors and failed backend calls, and produces a full-page screenshot. It doesn't mention side effects or required permissions, but for a dev-loop tool this is solid transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with a front-loaded action and clear deliverables. Every clause adds value, and there is no redundant repetition of schema fields.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but the description enumerates the key return values (boot status, real page errors, failed backend calls, screenshot) and the environment. It doesn't explain the 'loop' in detail, but for a 2-parameter tool this is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds extra meaning by showing how class_name is used ('?app_start=<class>'), going beyond the schema. timeout_ms is not elaborated, but the schema covers it sufficiently.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Boot') and identifies the resource ('app class'), environment ('headless in Chromium against the local backend'), and clear outputs ('booted/ok', errors, screenshot). It distinguishes itself from sibling tools like validate_view by positioning as the visual verification step.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool ('The visual verification step of the loop') and notes compatibility with existing ports and specific app classes. It doesn't explicitly name alternatives or exclusion criteria, but the context is strong enough for an agent to choose it over other tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scope_ofA
Authoritative in/out-of-scope verdict for UI5 control entities (exists since UI5 <= 1.71, not deprecated), read from the OpenUI5 source JSDoc. Needs an OpenUI5 checkout (OPENUI5_SRC or ../fork-openui5).
| Name | Required | Description | Default |
|---|---|---|---|
| entities | Yes | control entities, e.g. ["sap.m.Wizard", "sap.f.SidePanel"] |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses the data source (OpenUI5 source JSDoc), the version constraint, and the required checkout, which are meaningful behavioral details beyond the tool's basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the key purpose, and includes essential caveats without any fluff. Every word contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with a single simple parameter and no output schema, the description explains the core purpose, source, version constraints, and prerequisite. It does not detail return format or error behavior, but these are reasonably inferable and the description is largely complete for its simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'entities' is fully described in the schema with examples. The description adds no additional semantic detail beyond what the schema already provides, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool provides an 'authoritative in/out-of-scope verdict for UI5 control entities', which is a specific verb+resource combination. It also adds clarifying context (exists since UI5 <= 1.71, not deprecated) and distinguishes from siblings by its focus on scope determination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use the tool (when needing a scope verdict for UI5 control entities) and states a prerequisite (OpenUI5 checkout). It does not explicitly compare with sibling tools or list exclusions, but the purpose is distinct enough that an agent can infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_viewA
Fast static validation via ai-view-check, BEFORE the build/run loop: reconstructs the view from the z2ui5_cl_ai_xml builder calls (or takes raw view XML), runs the UI5 property gate (@since floor, deprecation) and renders it headless with a typed mock model. Seconds instead of a build+boot — use it after writing ABAP, then deploy_app once it is clean.
| Name | Required | Description | Default |
|---|---|---|---|
| xml | No | alternatively: raw view/fragment XML | |
| allow | No | accepted deviations, e.g. ["sap.m.GenericTile.systemInfo"] | |
| render | No | run the headless render gate (default true) | |
| min_ui5 | No | UI5 floor for the property gate (default 1.71) | |
| abap_source | No | ABAP class source building its view with z2ui5_cl_ai_xml |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool reconstructs the view from builder calls or raw XML, runs two gates (property gate and headless render), and uses typed mock models. It also mentions performance ('Seconds'). It does not elaborate on edge cases like how deviations are handled, but the schema covers 'allow'. Overall, it provides substantive behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The first sentence telegraphs the core purpose and mechanism, the second adds the speed benefit and workflow recommendation. Every clause earns its place, and the structure is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete enough for a validation tool with 5 params and no output schema. It explains the two validation gates, the input forms, the performance win, and the next step. It doesn't detail return values or failure messages, but since there's no output schema, that burden falls somewhat on the agent. The lack of explicit mention of the 'allow' parameter is minor, as the schema covers it. Overall, solid.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds meaning by tying parameters to behavior: it references 'z2ui5_cl_ai_xml builder calls' (abap_source), 'raw view XML' (xml), 'UI5 property gate' (min_ui5), and 'renders it headless' (render). It doesn't explicitly explain the 'allow' parameter, but the schema does. The description adds context beyond the schema, hence a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Fast static validation via ai-view-check', a specific verb+resource combination. It details that it reconstructs the view, runs a UI5 property gate, and renders headless, distinguishing it from build/run tools. The explicit mention of 'BEFORE the build/run loop' and 'then deploy_app once it is clean' clearly separates it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage context: 'use it after writing ABAP, then deploy_app once it is clean'. It contrasts with the build+boot cycle and names deploy_app as the next step after validation passes. This makes the tool's position in the workflow clear and provides a concrete alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
9 tool updates
v0.1.0- First observed
backend - First observed
build_backend - First observed
capabilities - First observed
deploy_app - First observed
generation_rules - First observed
remove_app - First observed
run_app - First observed
scope_of - First observed
validate_view
TDQS
Scored across 9 tools
Each tool has a clearly distinct purpose: capabilities and scope_of differ (abap2UI5 feature map vs. UI5 control entity verdicts), validate_view/deploy_app/run_app form a sequential loop, and backend/build_backend/run_app/remove_app manage separate aspects of the dev environment. No two tools appear to do the same thing.
The action tools mostly follow verb_noun (deploy_app, validate_view, build_backend, run_app, remove_app), but the knowledge/management tools use noun-style names (capabilities, generation_rules, scope_of, backend). All are snake_case and readable, but the pattern is not consistent throughout.
Nine tools is well-scoped for an MCP server focused on abap2UI5 development. Each tool earns its place in the workflow, from capability lookup and rule reading to validation, deployment, backend management, and cleanup.
The tool set covers the full development lifecycle: understand what's possible (capabilities, generation_rules, scope_of), validate (validate_view), deploy (deploy_app), build and run (build_backend, run_app, backend), and remove (remove_app). No obvious dead ends or missing critical operations.
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