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AI NetCafé

by com.ainetcafe

Build and deploy a web app from a description

build_app

Turn one plain-language description into a LIVE single-page web tool: code is generated, deployed to managed hosting with HTTPS, and listed — you get the public URL in ~1-2 minutes. Best for tool-style apps: calculators, converters, checklists, timers, generators, small games. Async — poll with check_job. Example — tools/call build_app {"description":"a tip calculator web app"} → poll check_job

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoOptional short app name (defaults to the description).
refineNoSlug of an app you built earlier (e.g. "u-1a23e679") to modify instead of building from scratch — describe only the change in `description`.
visibilityNo"public" (default, listed in the store) or "unlisted" (URL-only, not in the store).
descriptionYesWhat the tool should do, in any language. Be specific about inputs/outputs.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations offer no safety info (all false), so the description carries the burden. It transparently discloses async behavior ('Async — poll with check_job'), latency ('~1-2 minutes'), deployment details (managed hosting with HTTPS), and listing. It does not mention failure modes or resource limits, which would warrant a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long, front-loaded with the primary action and outcome, then usage guidance, and finally an async note with a concrete example. Every sentence earns its place with no unnecessary filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the availability of an output schema, the description appropriately focuses on the lifecycle: what it does, what you get, time estimate, async workflow, and ideal use cases. It covers the key aspects an agent needs to decide and invoke correctly, including an example call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions cover 100% of parameters, so the baseline is 3. The description adds only a single example invocation for the 'description' parameter and does not elaborate on 'name', 'refine', or 'visibility'. It does not meaningfully enhance the schema's parameter explanations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Turn one plain-language description into a LIVE single-page web tool' and details the outcome (code generated, deployed to HTTPS hosting, listed, public URL). It distinguishes itself from siblings by specifying its niche ('Best for tool-style apps') and the async polling pattern with check_job.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage context: best for 'calculators, converters, checklists, timers, generators, small games' and instructs to poll with check_job after invocation. However, it does not explicitly say when not to use or name alternative tools like get_app for existing apps, so it lacks explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose with detailed descriptions that explicitly differentiate even close pairs like diff_tables vs reconcile_ledger and list_models vs model_costs. No two tools appear to do the same thing, and the what_can_you_do tool further resolves any confusion.

Naming Consistency3/5

The majority of tools follow a verb_noun snake_case pattern (build_app, fetch_page, list_tasks), but several notable deviations exist: ai_visibility, china_reachability, model_costs, json_yaml, pdf_to_markdown, what_can_you_do, recall, remember, and jwt_decode. This mixed convention, while still readable, is not fully consistent.

Tool Count3/5

With 34 tools, the count is high and exceeds the typical comfortable range for an MCP server. However, the server is a broad AI utility platform covering web, data, LLM, conversion, and scheduling tasks, and each tool appears to serve a distinct purpose with little redundancy, making the large but organized set borderline appropriate for its scope.

Completeness3/5

The tool surface covers a wide array of common workflows (search, fetch, table operations, PDF extraction, model comparisons, task scheduling, memory). However, check_job references deep_research, translate_pdf, and make_slides which are not present in the tool list, and there is no update tool for tasks/apps or a way to delete memories, leaving some user journeys incomplete.

Resources