AI NetCafé

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

Annotations are all false and don't provide safety context. The description compensates by disclosing the exact cost in USD, a key behavioral trait. However, it does not mention potential rate limits, error behavior, or whether the call is synchronous, leaving some uncertainty.

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 the action and result. No redundant phrasing. Each sentence earns its place by introducing the purpose and a concrete use case.

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 covered. The tool is simple, and the description covers purpose, usage, and cost. Minor gaps like timeout behavior or error handling are not addressed but are not critical given the tool's 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% with clear descriptions for each parameter (model, prompt, system, max_tokens). The description adds context about the model default ('cheap capable model') but does not elaborate on parameter specifics beyond what the schema already provides. 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 tool sends a prompt to a specific LLM and returns the answer plus cost. It distinguishes itself from siblings like compare_models (which likely compares models) and deep_research (broader research) by focusing on single-model prompting.

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 use cases: 'second opinion from a different model' or 'cheaper model for a bulk subtask.' It implies when to use this tool over alternatives (e.g., not for comparison tasks), but does not explicitly name alternative tools for contrast.

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 the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context: recommended polling interval, terminal statuses ('done'/'error'), and how to interpret response fields (retry_after_seconds, next_action, structured_result). 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?

Two sentences deliver all essential information without redundancy. The most critical function (status/result retrieval) is front-loaded, followed by concise operational instructions. No filler or 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?

Given the tool's simplicity, one required parameter, and the presence of an output schema, the description fully covers necessary context: use case, polling behavior, terminal states, and how to handle pending vs. complete results. Nothing important 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 covers 100% of parameters, with a clear description for job_id. The description does not add further parameter semantics beyond clarifying that job_id originates from specific starting tools, which is useful context but not essential. Baseline 3 is appropriate given full schema coverage.

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?

Description uses a specific verb ('Get') and resource ('status or result of a job'), clearly identifying the tool's function. It also distinguishes from siblings by naming the job-initiating tools (deep_research, translate_pdf, 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?

Explicitly states when to use the tool (for jobs started by three named tools) and provides precise polling guidance (every 15–30 seconds until 'done' or 'error'), along with instructions for handling pending and complete states. This offers excellent contextual guidance beyond what annotations provide.

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 are all false, so the description carries the burden. It discloses parallel execution and the return of measured cost/latency, but does not mention any side effects, quota consumption, or failure behavior. It adds some context beyond annotations but leaves gaps.

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 the action, and no filler. Each clause adds value, making it optimally concise.

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 values are covered. The description provides the essential context (parallel, cost/latency, use case) and leaves technical details to the schema. Minor gaps around failure handling and quota impact prevent a 5.

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%, with each parameter already documented. The description adds no extra detail on parameter values or constraints, so baseline 3 applies.

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 ('Run') and resource ('one prompt across multiple LLMs in parallel'), clearly distinguishing it from single-model siblings like ask_model. It also articulates the decision-making purpose (which model to use) with data, making the tool's function immediately obvious.

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 states 'useful before committing a long job to an expensive model,' giving a concrete usage context. It does not explicitly name alternative tools or state when not to use it, so it's clear but lacks 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?

With no meaningful annotation hints (all false), the description carries the burden of explaining behavior. It discloses key traits: async returns a job_id immediately, takes 2-5 minutes, and requires polling check_job. It also mentions the external hosting at AI NetCafé. Lacking are details about failure modes or side effects, but the core behavior is well covered.

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 reasonably concise, with the main verb front-loaded and essential details (duration, async behavior, when to use) packed into four sentences. The '29k stars' detail is somewhat extraneous, but the hosting note is mildly relevant for setting expectations about an external service.

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 an async tool that kicks off a long-running job, the description covers the essential context: what it does, how long it takes, that it returns a job_id, which sibling to poll, and when to choose it. The presence of an output schema means the description doesn't need to detail the report structure, and the description wisely focuses on the job submission flow.

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 both parameters already described in detail (depth enum explanations, topic guidance to phrase as a question). The tool description adds no extra parameter semantics beyond what the schema provides, so 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 'Start an autonomous web research task' and elaborates on the mechanism (plans sub-questions, searches, reads, writes report with citations). It distinguishes itself from siblings by noting 'this is real research, not a single model call' and explicitly referencing 'poll check_job' as the sibling to use for retrieving results.

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 on when to use this tool: 'Use this when you need sourced, current information rather than what a model already knows.' It implies the alternative (ask_model) by contrasting with a single model call, and the async nature is made clear with the polling instruction.

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 readOnly, idempotent, and non-destructive behavior. The description adds meaningful context by specifying what details are included (purpose, usage, benchmarks, source, URL), which goes beyond the structured hints and clarifies the tool's informational payload.

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, front-loaded sentence that lists the key return contents without redundancy. Every phrase earns its place, and it is appropriately sized for a simple retrieval tool.

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 low complexity (one parameter, output schema present, safe annotations), the description thoroughly explains what the tool provides. It includes the type of details and the source of the slug, making it fully adequate for a single-app getter.

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 schema already documents the slug parameter with 'Application slug, from list_apps.' The description adds no additional parameter-specific detail beyond that, so it meets the baseline but does not exceed it.

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 returns full details of one hosted application, enumerating specific content types (function, usage, benchmarks, source repo, URL). The word 'one' distinguishes it from sibling list_apps, making the resource and scope 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?

Usage is implied: use this tool when you need comprehensive details about a specific application, especially versus list_apps for listing all apps. However, it does not explicitly mention alternatives or exclusions, leaving the agent to rely on context.

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=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful context about apps normally requiring local setup but running pre-configured here, yet it does not disclose additional behavioral traits such as pagination, response format, or edge cases. The output schema covers return structure, so some burden is lifted, but the description offers limited added behavioral insight.

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, each adding value: purpose is front-loaded, then context about the apps, then explicit usage guidance. No redundancy or 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 list tool with one optional parameter, a rich output schema, and strong annotations, this description is fully complete. It provides an overview, examples of when to use it, and clear exclusions, which is more than sufficient for an agent to decide when and how to invoke it.

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 parameter 'category' with a clear description. The tool description does not elaborate on parameter usage beyond schema, so it hits the baseline of 3 as per the high schema coverage rule.

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, using a specific verb ('List') and resource. It also distinguishes itself from direct task tools like translate_pdf and deep_research by explicitly naming them as alternatives, making the 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 when-to-use guidance with examples ('translating a PDF...', 'generating a PowerPoint...') and explicit when-not-to-use instructions, naming four specific sibling tools to call directly instead. This is model guidance.

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 this as a safe read-only, idempotent operation. The description adds context about the output scope ('every model callable') and the specific pricing data included, going beyond the annotation's safety profile by describing what the response will contain.

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 sentence that front-loads the action and scope, with no redundant phrases. Every word contributes to the purpose and output, making it highly efficient and well-structured.

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 parameterless list tool with an output schema, the description covers the essential aspects: what is listed, from where, and what pricing information is included. The output schema handles return value details, so this is sufficient context for an agent to select and invoke the 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 tool has zero parameters, so the schema provides 100% coverage. The description adds value by explaining that the output includes input/output pricing per million tokens, which helps the agent understand the return format without needing parameter details. Baseline for 0 params is 4.

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 the specific verb 'List' and identifies the resource as 'every model callable through AI NetCafé', clearly distinguishing this from sibling tools like ask_model or compare_models by focusing on a comprehensive listing with pricing. This is a specific verb+resource+scope statement.

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 states the use case ('so you can pick by cost as well as capability') and implies the tool is for broad model selection. However, it does not explicitly mention when not to use it or name alternatives such as compare_models, providing clear context but no 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?

With no helpful annotations (all false), the description carries the burden of explaining behavior. It discloses the async job pattern, the need to poll via check_job, the output type (.pptx), and the expected duration. It also mentions it is powered by Presenton, which adds provenance. It doesn't cover error cases or rate limits, but the core behavioral traits are well 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 three sentences, well-structured, and front-loaded with the primary purpose. Each sentence adds distinct information: core function, async workflow, and time estimate. 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?

Given the asynchronous nature and the existence of an output schema, the description is quite complete. It explains the job_id/check_job flow, the real file output, and the time frame. It doesn't mention failure handling or prerequisites, but the essential context for using this tool is present. A slightly higher score would require more operational details like retries or error states.

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 provides descriptions for all 4 parameters (100% coverage), so the baseline is 3. The description adds 'topic or outline' which essentially mirrors the schema's 'topic' parameter description. It does not add new meaning for slides, language, or instructions beyond what the schema states, so no bonus beyond baseline.

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 converts a topic or outline into a downloadable .pptx file, using a specific verb and resource. It also distinguishes itself by noting 'not a link into someone's web editor', which differentiates it from potentially similar tools or web-based generators.

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 guidance on the asynchronous workflow: it returns a job_id and tells the user to poll check_job for the download URL. It also sets a time expectation of 1-3 minutes. While it doesn't name alternative tools, the reference to check_job and the 'not a link' phrase imply usage context well.

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?

The description discloses the asynchronous job-based behavior, return format (job_id, poll check_job), expected latency (20-60 seconds), and output types (translated-only and bilingual). This goes beyond the annotations, which are all false, providing the agent with essential behavioral expectations without contradictions.

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 four sentences, each serving a distinct purpose: core function, distinguishing feature, output/process, and technical context. It is front-loaded with the main action and avoids any irrelevant or redundant details, making it highly concise.

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 description fully covers the tool's operation, including the asynchronous workflow, time expectations, and how to obtain results via check_job. Since an output schema exists, detailed return values are not needed here. It could mention error handling, but for a standard translation tool, it is sufficiently 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?

The input schema fully documents all three parameters with descriptions and defaults, so the description adds limited extra value. It does provide a performance note for page counts, but essentially relies on the schema for parameter semantics. Baseline of 3 is appropriate given 100% schema coverage.

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 translates a PDF from a URL while preserving layout, and explicitly differentiates it from ordinary translators that flatten documents. It also specifies the async nature (returns job_id) and how to retrieve results, making the 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 clear context for when to use the tool (academic PDFs with formulas/figures/two-column layout) and gives performance guidance for page-range choices (e.g., 'all' is slow and expensive). However, it does not explicitly mention when not to use it or compare to alternative translation tools, though the sibling context (check_job) is naturally implied.

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