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netcafe-build

by com.ainetcafe

List hosted open-source AI applications

list_apps
Read-onlyIdempotent

List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoOptional filter, e.g. "office", "research", "chat".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
appsYes
try_in_browserNo

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds useful context about the apps being pre-configured (no local setup/API key needed) and provides an example URL. While it doesn't detail return structure, the output schema exists, so the description sufficiently complements the annotations.

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?

Three sentences plus an example URL. Each sentence serves a distinct purpose: describing the tool, giving example use cases, and excluding cases where other tools should be used. No 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?

This is a simple list tool with one optional parameter, an output schema, and full annotations. The description covers purpose, usage context, and exclusions comprehensively. Nothing significant is missing.

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?

The schema has 100% coverage for the single optional 'category' parameter, including examples. The description doesn't add parameter-specific detail, but given perfect schema coverage, the baseline of 3 is appropriate.

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 lists open-source AI applications hosted at AI NetCafé, with pre-configured setup. It distinguishes itself from task-specific sibling tools by naming alternatives like compare_models, translate_pdf, deep_research, and make_slides, 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.

Usage Guidelines5/5

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

The description provides explicit guidance: 'Use this to find a tool for a task like...' and 'Do not call this first when the request already clearly matches...' This tells the agent exactly when to use this tool and when to use more direct alternatives.

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