Kleap
OfficialThe Kleap server enables AI agents to create, modify, publish, and manage live websites programmatically.
App Management
List all websites owned by your account (
list_apps)Retrieve metadata for a specific app — status, slug, production URL, published state (
get_app)List source files of an app (
list_app_files)Rename an app (
rename_app)Resolve a domain, URL, or slug to an app ID (
find_app)
Building & Editing
Create a new website from a natural-language prompt (
create_app)Modify an existing site using plain English instructions (
modify_app)Read current file contents before editing (
read_files)Write exact file contents directly to an app — deterministic, no AI credits consumed (
write_files)
Async Task Handling
Poll a build/edit task until completion (
check_task)Resume a failed or stalled task from where it stopped (
retry_task)
Publishing
Publish an app with a verified-live guarantee — only reports live once provably serving (
publish_app)Confirm whether an app is actually live (
get_publish_status)
Domain Management
Search for available domain names across TLDs (
search_domains)Check a domain's DNS and connection status (
check_domain)Connect a domain you already own to a published Kleap app, including routing and TLS setup (
connect_domain)
Account
Check remaining credit balance and current plan (
get_credits)
Allows creating and modifying Astro-based websites from natural language prompts, with automatic deployment and hosting on Kleap.
Click on "Install 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., "@KleapBuild a portfolio site and publish it."
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.
Kleap — website infrastructure for AI agents
Your agent builds. Kleap ships it live. Let any AI agent — Claude, ChatGPT, Cursor, or a bash-tool agent like Claude Code — build, edit and publish real, live websites for you. Hosting, database, auth and domains included.
An alternative to Lovable / v0 / Bolt — except it's driven by your agent, and every publish comes with the verified-live guarantee: a site is only ever reported online once it is provably serving — never a hallucinated dead link.
This package is both a CLI (kleap create "…", kleap publish 42, …) and an
MCP server (kleap mcp / no args) — same
account, same ~/.kleap/config.json auth, same underlying /api/v1 REST API.
Pick whichever fits your agent: a shell/bash-tool agent (Claude Code, a cron
script, CI) wants the CLI — one compact line per call, no JSON-RPC framing.
An MCP-native client (Claude Desktop, Cursor, ChatGPT connectors) wants the
MCP server, which this package also is, unchanged.

Above: a real run — your agent writes the code with
write_files,publish_appbuilds & deploys it, and the page is live in seconds. Or just ask Kleap's AI in plain English.
No secrets live in this package — it reads your own KLEAP_API_KEY (or the
token saved by kleap auth login) and talks only to kleap.co.
CLI (for agent shells — Claude Code, Codex, scripts)
If your agent drives a bash tool rather than MCP, use the CLI directly.
Output is 1-3 lines by default (token-efficient — built for an agent reading
its own tool output, not a human terminal), clean exit codes (0/1), and a
--json flag whenever you want the full structured response.
npx -y kleap-cli auth login # opens your browser once, no key to paste
# — or, for CI / non-interactive: npx -y kleap-cli auth key kleap_live_sk_...
npx -y kleap-cli create "a one-page site for my bakery, warm palette"
# ✓ created app 4821 — https://warm-bakery-fold.kleap.io
npx -y kleap-cli edit 4821 "change the headline to 'Roasted slow'"
# ✓ edited app 4821 — https://warm-bakery-fold.kleap.io
npx -y kleap-cli publish 4821
# ✓ published https://warm-bakery-fold.kleap.io
npx -y kleap-cli status warm-bakery-fold.kleap.io # by id, slug, kleap.io URL, or connected custom domain
# ✓ Bakery (4821) — live: https://warm-bakery-fold.kleap.ioCommands
Command | What it does |
| Sign in via browser (OAuth, PKCE loopback) — no key to copy |
| Store a |
| Clear / show current auth |
| Create a site, wait for the build (~5-15 min), print the live URL |
| Ask Kleap's AI to change a site, wait for it to redeploy |
| Publish/redeploy with the verified-live guarantee |
| One-line status: name, id, live URL or "not published" |
| Your apps, one tab-separated row each: |
| Available domains, one per line |
| Connect a domain you own; prints the A record to set |
| Capture a preview screenshot, print its URL |
| Run the MCP stdio server explicitly (same as no args) |
<app> accepts a numeric app id, a slug.kleap.io URL, a bare slug, or a
connected custom domain — resolved server-side in one call
(GET /apps/resolve), same as the MCP find_app tool.
Example: a Claude Code / bash-tool agent
# One-shot: build it, publish it, hand back a URL a human can click.
url=$(npx -y kleap-cli create "a landing page for my podcast" --json | node -e \
'let d="";process.stdin.on("data",c=>d+=c).on("end",()=>console.log(JSON.parse(d).url))')
echo "Live: $url"
# Non-blocking flow (agent does other work while it builds):
npx -y kleap-cli create "a landing page for my podcast" --no-wait --json # → { task_id, app_id, ... }
# ... later ...
npx -y kleap-cli status 4821Exit codes are always clean: 0 on success, 1 on any failure, with a single
✗ <reason> line on stderr (or {"error":{"message":...}} with --json) —
safe to check with $? / try/except subprocess.run(..., check=True) without
scraping prose.
Install once (optional — npx -y above needs no install)
npm i -g kleap-cli
kleap auth login
kleap create "a one-page site for my bakery"Related MCP server: webserver-mcp
MCP server (for MCP-native clients — Claude Desktop, Cursor, ChatGPT)
Same package, same account, same ~/.kleap/config.json auth — just a
different transport for clients that speak MCP
instead of a bash tool.
Easiest — connect with OAuth, no key
Add the hosted connector and sign in. Nothing to generate, nothing to paste — you authorize Kleap in your browser like any other app. Works in Claude Desktop, ChatGPT and Cursor.
https://kleap.co/api/mcpClaude Desktop — Settings → Connectors → Add custom connector → paste the URL → Connect → sign in to Kleap.
ChatGPT — Settings → Connectors → add the URL → authorize with OAuth.
Cursor — Settings → MCP → Add server → paste the URL → authorize.
That's it — same 17 tools, no API key. Skip straight to step 3.
Or — local CLI, sign in with your browser (no key)
Prefer a local stdio process? Sign in once — no key to generate or paste:
npx kleap-cli auth loginThis opens your browser, you authorize Kleap, and the token is saved to
~/.kleap/config.json. After that, npx -y kleap-cli just works. (kleap auth logout / kleap auth status are there too.) Then add a keyless stdio entry to
your client, e.g. Claude Desktop claude_desktop_config.json:
{ "mcpServers": { "kleap": { "command": "npx", "args": ["-y", "kleap"] } } }Or — local CLI with an API key
Prefer a key (e.g. for CI or scripting the REST API directly)?
1. Get an API key — at kleap.co → Settings → API key →
MCP / API access → Generate MCP key (kleap_live_sk_...).
2. Add Kleap to your AI client:
{
"mcpServers": {
"kleap": {
"command": "npx",
"args": ["-y", "kleap"],
"env": { "KLEAP_API_KEY": "kleap_live_sk_..." }
}
}
}{
"mcpServers": {
"kleap": {
"command": "npx",
"args": ["-y", "kleap"],
"env": { "KLEAP_API_KEY": "kleap_live_sk_..." }
}
}
}claude mcp add kleap -e KLEAP_API_KEY=kleap_live_sk_... -- npx -y kleap-cli{
"mcpServers": {
"kleap": {
"command": "npx",
"args": ["-y", "kleap"],
"env": { "KLEAP_API_KEY": "kleap_live_sk_..." }
}
}
}{
"mcpServers": {
"kleap": {
"command": "npx",
"args": ["-y", "kleap"],
"env": { "KLEAP_API_KEY": "kleap_live_sk_..." }
}
}
}Add the hosted connector at https://kleap.co/api/mcp and authorize with
OAuth (or paste your kleap_live_sk_ key). Same tools, no install.
Every stdio config is identical —
npx -y kleap-cli+ aKLEAP_API_KEYenv var — so any MCP client works.
Least-privilege keys: when you generate a key, pick a scope — Read-only (inspect sites, no changes), Build, or Full. Buying domains is never included by default. Give a read-only agent a read-only key.
3. Restart the client and just ask:
"Build me a one-page site for my bakery, publish it, and give me the live URL." "Add a contact form to my site and redeploy." "Change the headline to 'Roasted slow' and publish."
Works with any MCP-compatible agent: Claude · ChatGPT · Cursor · Claude Code · Codex.
Tools
Find & build — find_app · create_app · modify_app · read_files · write_files · rename_app · check_task · retry_task
Publish & domains — publish_app · get_publish_status · search_domains · check_domain · connect_domain
Account — list_apps · get_app · list_app_files · get_credits
Tool | What it does |
| Resolve a domain / URL / slug → app_id in one call |
| Create an Astro site from a prompt → returns a task (auto-deploys live) |
| Ask the app's AI to change it → returns a task |
| Read the current contents of files so you can edit them safely (not blind) |
| Write exact files directly (your code, deterministic) → then |
| Rename the display name (URL stays the same) |
| Long-poll a create/modify task to completion ( |
| Resume a failed/stalled build from partial state (new task_id) |
| Publish with verified-live (live-or-rollback, never a false "online") |
| Confirm a site is actually published + live |
| Find available domains (purchase stays user-confirmed in Kleap) |
| Connect a domain you already own to a live app |
| A domain's connection / DNS status |
| Your apps, an app's details, its files (read-only) |
| Remaining credit balance + plan |
App arguments are snake_case: app_id, task_id, prompt, message, visibility.
Recipes
Two ways to put code on a site — pick per task:
write_files(deterministic): your model writes the exact file contents; you push them and Kleap builds + deploys as-is. No Kleap-AI step → no Kleap credits, never stalls. Thenpublish_app. Unlike Lovable/v0/Bolt, your agent can write the code itself.modify_app(Kleap's AI): describe the outcome in plain English and Kleap's AI writes it. Like Lovable's message-passing — kept for when you'd rather it figure out the change.
Either way Kleap hosts it (build, deploy, SSL, DB, auth, domains, verified-live).
Edit existing files SAFELY (don't rewrite blind) —
list_app_files(app_id)→read_files(app_id, ["src/components/Header.astro"])→ edit only what must change with your own model →write_files(app_id, [{ path, content }])→publish_app(app_id). This read→edit→write loop is the reliable way to fix headers/footers, wrong phone numbers, broken links or dead forms without breaking the rest of the site.Edit a site named by its address —
find_app("mysite.ch")→read_files(...)→write_files(...)→publish_app(...), ormodify_app(app_id, "…")→check_task(task_id, wait=45).Many pages (programmatic SEO) — BEST: generate a dynamic route + a data file with your own model and push them in one
write_files, thenpublish_app:write_files(app_id, [{ path: "src/pages/[service]/[city].astro", content: … }, { path: "src/data/locations.json", content: … }])→publish_app(app_id)Deterministic, scales to thousands, no stall, no credits. (Or ask Kleap's AI to do the same in onemodify_app— never loop one call per page.)Don't babysit a 5-15 min build —
check_tasklong-polls (defaultwait=45), or pass awebhook_urltocreate_app/modify_appfor a fully hands-off flow.A build failed —
TASK_TIMEOUT/STALE_TASK= transient, callretry_task; it returns a new task_id — poll that one.TASK_FAILED= read the message, retry once.
The verified-live guarantee
Most tools tell the agent "it's online" the moment a deploy is requested. Kleap reports a site as published only once the new version is provably serving at its live URL — otherwise it rolls back and reports "not confirmed live." Your agent can never hand a user a dead link.
If check_task reports failed (a transient generation stall), call retry_task
with that task_id to resume from where it stopped — it returns a new task_id
to poll, and partial work is kept. Or skip the AI entirely and write_files the
exact code yourself, then publish_app.
FAQ
Do I need an API key? No. The easiest path is the OAuth connector
(https://kleap.co/api/mcp) — you sign in with your browser and never copy a
key. An API key is only needed for the local CLI / direct REST use.
Is it safe? Yes. Whether you connect with OAuth or an API key, an agent can
only ever touch your own Kleap apps. OAuth tokens and kleap_live_sk_ keys are
scoped, sent only over HTTPS, and revocable anytime in Settings → API key.
Credentials from kleap auth login / kleap auth key are stored in
~/.kleap/config.json (permissions 0600); kleap auth logout deletes that
file. A stored OAuth login is bound to the origin that issued it — if
KLEAP_API_URL points anywhere else, the CLI refuses to send the token
(CREDENTIAL_ORIGIN_MISMATCH) so a malicious/typo'd endpoint can't capture it.
For custom endpoints (e.g. staging), use KLEAP_API_KEY or kleap auth key —
an explicit secret you provide is sent where you point it. Details in
SECURITY.md.
How much does it cost? Connecting is free. Builds and edits use Kleap credits
(get_credits reports your balance) — see pricing.
Which agents work? Any MCP client: Claude Desktop, Claude Code, Cursor, ChatGPT (hosted connector), and others.
Requirements & run
Node ≥ 18. Run it directly:
KLEAP_API_KEY=kleap_live_sk_... npx -y kleap-cli
# → [kleap-mcp] ready (stdio) → https://kleap.co. Tools: list_apps, ...Override the API base with KLEAP_API_URL (default https://kleap.co).
Missing key → the server exits with a clear message. Note: a stored OAuth
login only works against the origin it was issued by — with a custom
KLEAP_API_URL, authenticate via KLEAP_API_KEY or kleap auth key instead
(see SECURITY.md).
Links
Kleap: https://kleap.co · MCP & CLI page: https://kleap.co/mcp
Issues & security: https://github.com/kleaphq/cli/issues
Maintained by the Kleap team. MIT © Kleap.
Available Tools
13 toolscheck_domainA
Check a domain's connection / DNS status for a Kleap app.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | The domain, e.g. 'mybakery.com'. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It states the tool checks connection/DNS status, which suggests a read-only operation, but does not explicitly confirm no side effects or disclose prerequisites (e.g., domain must be configured).
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?
A single, front-loaded sentence with no wasted words. Every word contributes to understanding the tool's purpose.
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 tool with one required parameter and no output schema, the description is nearly complete. It could be improved by briefly hinting at the result (e.g., 'returns status or errors'), but as is, it adequately covers the core functionality.
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% and describes the domain parameter with an example. The description adds value by tying the parameter to the 'Kleap app' context, clarifying the purpose beyond the schema's basic type and example.
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 'Check' and resource 'domain's connection / DNS status' within the context of a Kleap app. It clearly distinguishes from sibling tools like connect_domain (action) and search_domains (search).
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 implies usage for checking domain DNS/connection status but does not explicitly state when to use it versus alternatives like check_task or connect_domain. No exclusions or when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_taskA
Poll an async task (app creation or edit). Returns status + result when done. If status is 'failed' (e.g. a transient generation stall), call retry_task with the same task_id to resume — don't start over.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | The task id. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses polling behavior and failure handling, but does not explicitly state read-only nature or other side effects.
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 concise sentences with no wasted words, front-loaded with the core action.
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?
Covers purpose, usage, and retry guidance; lacks details on return format or error handling, but adequate given 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?
Schema coverage is 100% and description adds no extra meaning beyond the schema's parameter description.
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?
Clearly states it polls an async task for app creation or edit and returns status and result. Distinguishes from sibling retry_task by mentioning when to use it.
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?
Explicitly says to call retry_task if status is 'failed' and not to start over, providing clear when-to-use and alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
connect_domainA
Connect a domain the user ALREADY OWNS to a published Kleap app (sets up routing + automatic TLS). The app must be published first; the user points the domain's A record to Kleap. Does not buy anything.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | The app id (must be published). | |
| domain | Yes | The domain to connect, e.g. 'mybakery.com'. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the tool modifies routing and enables TLS, and clarifies that it does not buy domains. However, it does not discuss what happens if the domain is already connected, potential side effects, error states, or permission requirements, leaving gaps in 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?
The description is only two sentences, front-loaded with the core action and key constraints. Every word adds value, with no redundancy or irrelevant 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 tool's moderate complexity (2 required parameters) and no output schema, the description should explain what the tool returns or what constitutes success/failure. It does not mention return values, error handling, or prerequisites beyond ownership and app publishing. Sibling tools are available for checking, but the description does not leverage them for completeness.
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 description coverage is 100%, so the baseline is 3. The tool description adds that 'app_id must be published' and gives an example domain, but these are only marginally helpful beyond the schema. The description does not significantly enhance parameter understanding.
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 verb 'Connect' and the resource 'a domain' with specific conditions: the domain must be owned by the user and the app must be published. It also describes what the tool does (sets up routing and automatic TLS) and differentiates from sibling tools like 'check_domain' and 'search_domains' by focusing on connection.
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 explicit conditions for use: the app must be published, the user must point the domain's A record to Kleap, and it clarifies that it does not purchase domains. It lacks explicit mention of alternatives among siblings or when not to use this tool, but the context is clear enough to guide typical usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_appA
Create a new Kleap website from a natural-language prompt. Returns a task — poll check_task until it completes.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What the website should be. | |
| visibility | No | 'public' (discoverable) or 'personal' (private). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the tool returns a task (asynchronous) but does not mention authorization, rate limits, idempotency, or side effects. With no annotations, the description carries full burden and is adequate but not rich.
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 concise sentences: first states purpose, second gives actionable follow-up. No unnecessary words, front-loaded key 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?
For a simple two-parameter tool with no output schema, the description covers creation and the subsequent polling action. It is complete enough to use correctly, though could hint at task ID usage.
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%, and the description adds no additional meaning beyond what the input schema already provides for both parameters. Baseline 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?
Clearly states it creates a Kleap website from a natural-language prompt and returns a task. The verb 'Create' and resource 'Kleap website' are explicit, and it distinguishes from sibling tools like modify_app and publish_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?
Tells the user to poll check_task after creation, which is helpful for the follow-up. However, lacks explicit when-to-use or when-not-to-use guidance, and no alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_appA
Get one Kleap app's metadata: status, slug, production_url, published state.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | The app id. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. States it 'gets' metadata, implying a read operation, but does not explicitly confirm read-only nature, authentication needs, rate limits, or absence of side effects. Adequate but could be more explicit.
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?
Single sentence of 13 words, front-loaded with action and key fields. No redundant information. Every word earns its place.
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 only one simple parameter (fully described in schema) and no output schema, the description provides the essential fields but does not specify the response structure or whether all listed fields are guaranteed. Slightly incomplete for an agent that needs to parse results.
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 description coverage is 100% (app_id has a description 'The app id.'). Description adds no extra meaning beyond schema; does not explain where to find the app_id or its format. Baseline 3 applies since schema already covers param documentation.
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?
Clearly states the verb 'Get', resource 'one Kleap app's metadata', and lists specific fields (status, slug, production_url, published state). Distinguishes from sibling tools like list_apps (multiple apps) and get_publish_status (specific subset).
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?
No explicit when-to-use or when-not-to-use guidance. Implied usage for retrieving a single app's metadata, but lacks differentiation from list_apps or get_publish_status. With 12 sibling tools, alternative scenarios should be mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_creditsA
Check the authenticated account's remaining credit balance and plan.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 implies a read-only operation ('Check'), but does not disclose any potential side effects, authentication requirements, or rate limits. For a zero-parameter read tool, this is minimally adequate.
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, clear sentence that delivers the essential purpose immediately with no wasted words.
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, parameterless tool with no output schema, the description provides enough context about what is returned ('remaining credit balance and plan'). However, since there is no output schema, the description could be more explicit about the format or structure of the response.
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?
There are no parameters, and the schema coverage is 100% (empty schema). The description adds no parameter-specific information, but given no parameters exist, this is acceptable. A baseline of 4 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 verb 'Check' and the resource 'authenticated account's remaining credit balance and plan.' It is specific and distinguishes itself from sibling tools, none of which appear to deal with credits.
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?
No explicit guidance on when to use this tool, but since there is no sibling tool for credits, the usage context is implicitly clear. A brief note on prerequisites (e.g., authentication) would improve this.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_publish_statusB
Check whether an app is actually published and live (production_url + published state).
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | The app id. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It states the tool checks status, implying read-only behavior, but lacks details on error states, rate limits, or whether modification is possible. This is insufficient for safe invocation.
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?
A single sentence that is front-loaded with the core action and includes clarifying parenthetical details. Every word serves the purpose with no redundancy.
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 one-parameter tool with no output schema, the description adequately states the purpose but omits response format or behavior when the app doesn't exist. It is minimally complete but could be improved.
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 a single parameter 'app_id' described as 'The app id.' The description does not add further meaning beyond the schema, so baseline score 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 uses a specific verb 'Check' and clearly identifies the resource as 'whether an app is actually published and live' with key attributes noted. It distinguishes from sibling tools like 'publish_app' by focusing on status verification, though it does not explicitly differentiate.
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?
No explicit guidance on when to use this tool vs alternatives. The description implies usage for checking publish status, but does not mention prerequisites, when not to use it, or compare with siblings like 'get_app' or 'publish_app'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_app_filesB
List the source files of a Kleap app.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | The app id. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only states it lists files, but with no annotations, it fails to disclose any behavioral details like whether it is read-only, what permissions are needed, or any side effects. The output format is not described.
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 concise sentence with no redundant information. Every word is necessary and contributes to the purpose.
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 no output schema and no annotations, the description is minimally adequate but lacks details about the response format or any filtering behavior. It is functional but not comprehensive.
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 the parameter 'app_id' described as 'The app id.' The tool description does not add extra meaning beyond the schema, so baseline 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 verb 'List' and the resource 'source files of a Kleap app', which is distinct from sibling tools like 'list_apps' (list apps) or 'get_app' (get app details). It is specific and 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 provides no guidance on when to use this tool versus alternatives, such as 'get_app' or 'list_apps'. It does not mention context or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_appsA
List the Kleap apps (websites) owned by the authenticated account.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max apps to return (default 20). | |
| offset | No | Pagination offset (default 0). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries burden. It correctly implies a non-destructive read operation but does not disclose potential limits, rate limits, or empty result behavior. The statement is minimal but accurate.
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?
Single, front-loaded sentence with no extraneous words. Every word contributes to clarity. Highly efficient.
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 list tool with fully described parameters, the description is complete. It specifies ownership filter and result type. Could mention pagination or return format, but not critical given schema.
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 description coverage is 100%, so baseline is 3. The description adds no extra detail about parameters beyond what the schema already provides (limit, offset descriptions). No parameter intuition added.
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 ('List'), the resource ('the Kleap apps/websites'), and the scope ('owned by the authenticated account'). It effectively distinguishes from siblings like 'get_app' (single app) and 'create_app' (creation).
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 implies use for listing owned apps but provides no explicit guidance on when not to use or alternatives. It does not mention pagination parameters or context for limit/offset usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
modify_appC
Ask a Kleap app's AI to change it (edit copy, add a section, fix a bug). Returns a task — poll check_task.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | The app id. | |
| message | Yes | The change to make. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It says 'change it' implying mutation but does not disclose destructive potential, authentication needs, rate limits, or the nature of the asynchronous task. The agent lacks information about side effects or expected behavior.
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 concise: one sentence with an actionable note on polling. It front-loads the purpose and workflow without unnecessary words.
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 simple parameters and no output schema, the description covers the basic action and return type but lacks details on result interpretation, error cases, and the task lifecycle. It is adequate but leaves notable gaps.
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%, and the description adds generic context but no additional meaning beyond the schema's parameter descriptions. Baseline 3 is appropriate as the schema already documents both parameters 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 clearly states the tool modifies a Kleap app's AI with specific examples (edit copy, add section, fix bug) and indicates it returns a task. However, it does not explicitly differentiate from sibling tools like create_app or update operations.
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 mentions polling check_task but provides no guidance on when to use this tool versus alternatives, prerequisites, or when not to use it. The agent is left to infer context from sibling names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
publish_appA
Publish a Kleap app to its live URL with the VERIFIED-LIVE guarantee: it is only reported live once the new version is provably serving (otherwise it reports 'not confirmed live' — never a false positive). Returns a deploy handle — poll get_publish_status.
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | The app id. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes the behavioral guarantee (no false positives) and that it returns a deploy handle. Without annotations, this covers key behaviors well, though it could mention prerequisites.
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 purpose and key guarantee, no wasted words.
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 one-parameter tool with no output schema, the description fully explains what it returns and what to do next, making it 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 covers 100% of the single parameter with description 'The app id.' Description adds context that this is the app to publish, meeting baseline expectations.
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?
Clearly states it publishes a Kleap app to live URL with a unique 'VERIFIED-LIVE' guarantee, distinguishing it from sibling tools like get_publish_status.
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?
Explicitly tells the agent to poll get_publish_status after use, providing a clear workflow. Does not explicitly mention when not to use, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
retry_taskA
Resume a failed or stalled create/modify task from where it stopped (partial files are preserved). Use this when check_task reports 'failed' before starting a brand-new create_app. Returns a fresh task — poll check_task on the new task_id.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | The failed task id to resume. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so description carries full burden. It discloses that partial files are preserved and that a fresh task_id is returned for polling via check_task. It does not mention authorization or error handling, but covers key behavioral aspects.
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, no fluff. Front-loaded with the action verb 'Resume', and every sentence adds value without redundancy.
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-parameter tool with no output schema or annotations, the description is quite complete: it explains purpose, when to use, and expected result (new task_id to poll). Lacks details on response format but points to check_task.
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 has 100% coverage with a clear description for task_id. The description adds context about the task type (create/modify) and state (failed/stalled), enhancing understanding beyond the schema.
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 resumes a failed or stalled create/modify task, preserving partial files. It distinguishes from siblings like check_task (status) and create_app (new), making its 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?
Explicitly advises using this tool when check_task reports 'failed' before starting a new create_app, providing clear when-to-use guidance and suggesting an alternative to avoid.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_domainsA
Search for available domains for a site (e.g. 'mybakery'). Returns available names across TLDs. NOTE: agents cannot buy a domain — purchase is confirmed by the user in Kleap. Use connect_domain for a domain the user already owns.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Base name to search, without a TLD (e.g. 'mybakery'). | |
| tlds | No | Optional TLDs to check, e.g. ['.com', '.io', '.ch']. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses that search is read-only and returns available names across TLDs. It does not mention potential external calls, rate limits, or side effects, but for a search tool this is adequate.
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 concise sentences: first states purpose and example, second gives critical note, third directs to sibling. Front-loaded and every sentence earns its place.
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 no output schema, the description only vaguely says 'Returns available names across TLDs' without details on format, errors, or pagination. Also does not differentiate from check_domain, leaving some gaps.
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% (baseline 3). The description adds value by specifying that query should be without TLD and providing examples for both parameters, clarifying usage beyond schema.
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 verb 'Search for available domains' and the resource 'for a site', with an example 'mybakery'. It also distinguishes from connect_domain, a sibling for user-owned domains.
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?
Explicitly states agents cannot buy domains (user confirms purchase) and directs to connect_domain for owned domains. However, lacks differentiation from check_domain, another sibling.
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. Dates show when Glama detected each change.
13 tool updates
v1.0.0- First observed
check_domain - First observed
check_task - First observed
connect_domain - First observed
create_app - First observed
get_app - First observed
get_credits - First observed
get_publish_status - First observed
list_app_files - First observed
list_apps - First observed
modify_app - First observed
publish_app - First observed
retry_task - First observed
search_domains
TDQS
Each tool targets a distinct action (check, connect, create, modify, publish, list, etc.) with clear boundaries. No two tools have overlapping purposes.
All tool names follow a consistent verb_noun pattern in snake_case, making the set predictable and easy to navigate.
13 tools cover the core workflows of app creation, modification, publishing, domain management, and task handling without excess or deficiency.
The tool surface covers creation, modification, publishing, and domain management, but lacks a delete operation for apps or domains, which is a minor gap.
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