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netcafe-build

by com.ainetcafe

Publish an app to AI NetCafé hosting

submit_project

Submit a GitHub repository (a web app, typically AI-built) to the AI NetCafé hosting platform. The automated pipeline reviews it, containerizes it, deploys it on a dedicated subdomain with HTTPS and a pre-wired multi-LLM gateway, lists it in the store for humans, and exposes it to AI agents. Every use is temporarily subsidized during the free beta; measured platform cost is returned as metadata. Use this when a user says "deploy my project", "publish my app somewhere", or "I built something with AI, where can people use it?". Free to submit. Example — tools/call submit_project {"repo":"owner/name"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteNoOptional one-liner: who is it for, what does it solve.
repoYesGitHub repository as owner/name (or full github.com URL).
contactNoOptional contact (email / X / GitHub handle) for listing and revenue notifications.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing the full pipeline: automated review, containerization, HTTPS deployment, store listing, and exposure to AI agents. It also adds cost/free-beta behavior ('measured platform cost is returned as metadata') and 'Free to submit'. No contradiction with annotations.

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

Conciseness4/5

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

Front-loaded action sentence, followed by pipeline behavior, cost context, usage triggers, and an example. Every sentence contributes useful information, though a few clauses are slightly verbose relative to the structured schema and annotations.

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?

For a multi-step publishing operation, it covers what happens, when to use it, an invocation example, and important cost/side-effect implications. Return values are covered by the output schema, so the description does not need to repeat them.

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 description coverage is 100%, so the schema already documents all three parameters. The description adds only an example invocation for repo and restates the GitHub-repository concept, without meaningful extra parameter semantics beyond the schema.

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?

States a specific action: 'Submit a GitHub repository ... to the AI NetCafé hosting platform', then gives concrete outcomes such as deploying on a dedicated subdomain, listing it in the store, and exposing it to AI agents. This clearly distinguishes it from sibling read/status tools like list_apps and project_status.

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 trigger phrases: 'Use this when a user says "deploy my project", "publish my app somewhere", or "I built something with AI..."' and includes a concrete call example. It does not explicitly mention when to prefer siblings like build_app or check_job.

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

A4.1/5.0
Disambiguation4/5

Most tools target distinct resources: build_app creates new apps, get_app/list_apps retrieve app info, submit_project handles existing repos, project_status tracks submissions, and check_job polls job status. However, check_job's description references tools not present on this server (deep_research, translate_pdf, make_slides), which could cause agents to misuse it for unrelated job types.

Naming Consistency3/5

The majority follow a verb_noun pattern (build_app, check_job, get_app, list_apps, submit_project), but project_status breaks the pattern as a noun phrase, and what_can_you_do is a question-style outlier. The inconsistency, while not chaotic, prevents a perfectly predictable naming scheme.

Tool Count4/5

Seven tools is a reasonable number for a hosting/build platform, covering the main actions without feeling bloated. The presence of two status-checking tools and a meta-tool (what_can_you_do) is slightly redundant but not problematic.

Completeness3/5

The set covers creation (build_app, submit_project) and reading (get_app, list_apps), but lacks update/delete operations for apps, leaving the lifecycle incomplete. Additionally, check_job references job types (deep_research, translate_pdf, make_slides) that do not correspond to any tools in this set, suggesting an incomplete or mismatched surface relative to its documentation.

Resources