netcafe-build

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds critical behavioral context: the polling interval, the terminal statuses, the handling of intermediate responses (retry_after_seconds, next_action), and the preference for structured_result. It also includes a concrete GET URL example, fully disclosing the interaction pattern.

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 information-dense but not verbose, delivering purpose, polling guidance, response handling, and an example in three sentences. Each sentence earns its place, and the example is clearly separated for easy scanning.

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?

With an output schema present, return values are already specified, so the description correctly focuses on the asynchronous polling protocol. It covers when to call, how to poll, what to do while pending, what to do on completion, and the exact endpoint format, making it fully self-contained for correct invocation.

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 fully documents job_id (100% coverage), so the baseline is 3. The description enhances this by specifying that the job_id comes from deep_research, translate_pdf, or make_slides, and by showing its placement in the URL example, which adds context beyond the schema's generic 'returned when the task was started'.

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 verb ('Get'), the resource ('the status or result of a job'), and the originating tools (deep_research, translate_pdf, make_slides). This differentiates it from sibling creation tools like make_badge or render_diagram, which are all about generating artifacts rather than checking job 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?

The description provides explicit usage guidance: poll every 15-30 seconds until status is 'done' or 'error', follow retry_after_seconds and next_action while pending, and prefer structured_result when complete. It also indicates the tool is for jobs started by specific tools, which serves as both an inclusion and exclusion criterion.

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

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

Annotations already establish read-only and idempotent behavior. The description adds value by disclosing the specific categories of information returned and providing a concrete example. It does not mention any side effects, which is consistent with the read-only annotation.

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, information-dense sentence followed by a useful example. It avoids redundancy and front-loads the core 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?

With a single parameter, full schema coverage, and an output schema present, the description sufficiently covers what the tool does, how to call it, and what to expect. The example and parameter sourcing guidance make it complete for this simple tool.

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 fully describes the slug parameter, including its source (list_apps). The description reinforces this with the example URL, but doesn't add new semantic meaning beyond the schema's 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?

The description clearly states it returns full details of one hosted application, listing specific content (what it does, usage, benchmarks, repo, URL). This distinguishes it from siblings like list_apps (which lists apps) and build_app (which builds).

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 after list_apps, as the slug parameter is explicitly sourced from list_apps. It gives an example HTTP request showing how to invoke it. However, it doesn't explicitly state when not to use this tool vs checking jobs or building apps, though the context makes it fairly obvious.

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

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds useful behavioral context by detailing the return categories (live with URL, rejected with reason, unknown), which goes beyond the annotations. No contradiction exists.

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 compact: two sentences plus a relevant example. It front-loads the key action and lists possible outcomes efficiently. Every element serves a purpose, with no filler 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?

The tool has an output schema, so return-value details are already covered. The description adequately covers purpose, usage timing, and parameter format. Minor gaps like error handling are not critical for tool selection or invocation.

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% for the single 'repo' parameter. The description reinforces this with a concrete example URL, clarifying the expected format. This adds practical value beyond the schema's description without being redundant.

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 (repository status on AI NetCafé). It enumerates the possible outcomes (live, pipeline, rejected, unknown), which makes the tool's function explicit and distinguishes it from siblings like 'check_job' or 'get_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 description gives explicit temporal guidance: 'Use after submit_project.' This clearly indicates when to invoke the tool. It does not explicitly contrast with sibling tools, but the stated context is sufficient for an agent to know this is the follow-up status check.

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

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

Annotations only declare the generic profile (not read-only, not idempotent, not destructive), while the description discloses the real behaviour: automated review, containerization, dedicated subdomain with HTTPS, pre-wired multi-LLM gateway, store listing, agent exposure, subsidy during beta, and measured cost returned as metadata. That is substantial added context beyond 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.

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

It is front-loaded with the core action and pipeline, then triggers, then cost/call example, so the important content comes first. A few clauses (revenue notifications, subsidy) add length without much decision value, but nothing is truly wasted.

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?

With an output schema present, the description need not explain return values, and it still mentions the cost metadata. The main gap is that it does not warn about the non-idempotent behavior (submitting the same repo twice likely creates duplicate deployments), which the annotations hint at.

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 schema already documents repo (owner/name or URL), note, and contact; the description adds only an example call with the repo format. That matches the baseline 3 when the schema does the heavy lifting.

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 names a specific verb and resource (submit a GitHub repository to the AI NetCafé hosting platform) and enumerates the concrete pipeline steps, so an agent knows exactly what the tool produces. It does not explicitly contrast itself with siblings like build_app or check_job, which is the only thing keeping it from a 5.

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 gives explicit user-utterance triggers: "deploy my project", "publish my app somewhere", "I built something with AI, where can people use it?" That is strong, concrete routing guidance. It stops short of saying when NOT to use it or naming build_app/check_job as alternatives for related needs.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral traits not visible in annotations: deterministic, calls no model, costs nothing, and never runs out of quota. 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.

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

Four dense sentences, front-loaded with the core purpose in the first sentence and supported by recipes, cost/quota guarantees, and when-to-use guidance. Every sentence earns its place; no filler 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?

Given the simple context (single required string parameter, rich annotations, and an output schema), the description is complete. It covers the tool's behavior, return value highlights, performance/cost characteristics, and usage context without leaving important 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?

The input schema already describes the 'task' parameter with examples, and schema coverage is 100%. The description adds the nuance 'plain language (any language)' and emphasizes that example calls are generated, providing modest additional meaning 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's purpose with a specific verb+resource: describe a task and get back exactly which tools on the server do it, with ready-to-run example calls. It explicitly distinguishes itself from sibling tools by positioning itself as the meta-guide that prevents 'reading the whole catalogue and guessing.'

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?

Provides explicit when-to-use guidance: 'Call this FIRST when you are not sure what this server offers.' It also clarifies the intended use case (plain-language task discovery) and contrasts with the alternative of scanning the catalogue manually, which is sufficient for a meta-tool.

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