ai-netcafe

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

No annotations are provided, so the description must disclose all behavioral traits. It mentions exact cost return and default model, but does not discuss rate limits, error handling, or authentication requirements.

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 containing only essential information: what it does, what it returns, and when to use it. 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?

While there is no output schema, the description hints at the return format (answer + cost). For a simple inference tool, this is sufficient. Could mention error responses or streaming options, but not critical.

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?

Input schema has 100% parameter description coverage, which already provides clear meaning. The description adds value by noting the model defaults to a cheap capable model and that list_models can be used to find available IDs.

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 one LLM and returns the answer with cost, distinguishing it from siblings like compare_models (comparison) and list_models (listing).

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 concrete use cases: getting a second opinion from a different model or using a cheaper model for bulk subtasks. Lacks explicit when-not-to-use or alternatives but offers clear 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?

No annotations are provided, so the description carries the full burden. It implies read-only behavior by saying 'Get', but does not explicitly state non-destructiveness or other side effects. Adequate but not thorough.

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?

One concise sentence with no wasted words, effectively communicating the tool's purpose and usage.

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 no output schema, the description does not explain what the status or result looks like, leaving some ambiguity. Adequate for a simple tool but not fully 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?

Schema description coverage is 100% with a clear parameter description. The tool description adds no additional meaning beyond the schema, meeting the 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 it retrieves the status or result of a job started by deep_research, distinguishing it from sibling tools like deep_research which starts the 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?

The description provides clear context on when to use (after starting a job) and includes polling instructions, but does not explicitly exclude other job types or mention 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?

No annotations provided, so description carries full burden. It discloses parallel execution, real cost and latency measurement, and side-by-side comparison. It does not mention rate limits or performance implications but provides key behavioral traits.

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 with no wasted words. First sentence states core action and output; second sentence adds real-world context. Highly efficient.

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 complexity (3 parameters, no output schema, no annotations), the description is complete: purpose, behavior, and usage scenario are covered. It enables an agent to decide and invoke correctly without 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?

With 100% schema coverage, baseline is 3. The description adds value by noting default models ('cheap/mid/strong spread'), which is not in the schema. Also clarifies the 'prompt' is sent to every model and 'system' is optional.

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 runs one prompt across multiple models in parallel, returning answers with cost and latency. The title and description differentiate it from sibling tools like ask_model (single model) and check_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 explains the tool is useful before committing a long job to an expensive model, answering 'which model should I use?' with data. It provides context for when to use but does not explicitly exclude alternative scenarios or name 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?

No annotations are provided, so the description carries the full burden. It discloses that the task is autonomous, takes 2-5 minutes, returns a job_id immediately, and requires polling check_job. It also mentions the underlying technology (gpt-researcher hosted at AI NetCafé). This gives good insight into the tool's behavior, though cost or rate limits are not mentioned.

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 paragraph, front-loaded with the core purpose, and every sentence adds value. It is concise with no unnecessary words, covering purpose, behavior, usage guidance, and technical details efficiently.

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 complexity (autonomous research) and lack of output schema, the description explains the asynchronous flow (returns job_id, poll check_job) and when to use it. It is complete enough for an agent to select and invoke correctly, though error handling or limits are not covered.

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 description adds significant meaning beyond the schema. For 'topic', it advises phrasing as a question, not a keyword. For 'depth', it explains the enum values with duration and output type ('quick = outline only (~1 min); standard = full cited report (~3 min)'). This is exemplary.

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 starts an autonomous web research task, details the process (planning sub-questions, searching, reading, writing a report), and distinguishes it from a single model call by emphasizing it's real research taking 2-5 minutes. The verb 'start' and resource 'autonomous web research task' are specific and 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 says 'Use this when you need sourced, current information rather than what a model already knows,' providing clear context for when to choose this tool over alternatives like ask_model. It does not explicitly state when not to use it or list all sibling alternatives, but the guidance is sufficient.

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?

No annotations provided. Description discloses read-only nature and what details are returned, but omits potential errors, authentication needs, or limits.

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?

Single front-loaded sentence enumerating contents. Slightly long but no wasted words; could be more concise by omitting specific examples.

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?

No output schema; description compensates by listing expected details. Parameter fully documented. Missing error handling or format expectations, but adequate for a simple get.

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 covers slug with description 'from list_apps'. Description adds context that slug comes from list_apps, aiding correct invocation.

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?

Clearly states verb 'Get details' and specific resource 'one hosted application'. Enumerates contents (what it does, how to use, benchmarks, repo, URL), distinguishing from sibling 'list_apps' which lists apps.

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?

Implies use after list_apps to get slug, but no explicit when-to-use or when-not-to-use. No mention of alternatives among siblings.

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 annotations, the description carries the burden. It discloses that apps are pre-configured and normally need local setup, adding context beyond the schema. No 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?

Two sentences with no waste: first clearly states purpose, second provides usage context and examples. Information is front-loaded and every sentence earns its place.

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 and no output schema, the description adequately covers purpose and usage. It could mention what fields are returned, but overall sufficient.

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 examples for category. The description repeats these examples without adding new meaning, so baseline score 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 apps hosted at AI NetCafé, with specific task examples (translating PDF, generating PPT, polishing paper) that distinguish it from siblings like ask_model 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 explicitly suggests using this tool to find a tool for a task, providing implicit guidance. However, it does not explicitly state when not to use it or compare with 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?

Describes the output (models with prices) and that it lists 'every model callable'. No annotations provided, so description is the sole source of behavioral info. It does not mention read-only nature, rate limits, or authentication, but is adequate for a simple listing operation.

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?

Single sentence that is front-loaded with the action and resource, includes the purpose (pick by cost). No unnecessary 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 has no parameters and no output schema, the description sufficiently explains what is returned (models with prices) and the use case. It is complete for its 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?

No parameters, so schema coverage is 100%. Baseline 4 applies as description does not need to add parameter semantics. The description adds value by indicating the output contains prices.

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?

Clearly states it lists all callable models with their input/output prices per million tokens. Distinguishes from sibling tools like ask_model or compare_models by focusing on listing all models with cost info.

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?

Implies use when selecting a model by cost, but does not explicitly mention when not to use or compare with siblings like compare_models or ask_model. Lacks explicit usage boundaries.

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 annotations, the description discloses async behavior (job_id, polling), typical duration, and open-source origin. This adds significant behavioral context beyond the schema, though error conditions or limitations are not 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 two sentences with no wasted words. It front-loads the core action ('Turn a topic or an outline into a real downloadable .pptx file'), then efficiently adds async workflow, timing, and provenance.

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 file generation tool, the description covers output format, job polling, and typical time. It references sibling check_job appropriately. Missing details on result validity or error handling, but adequate given the sibling tool's role.

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%, so baseline is 3. The description only mentions 'topic or outline', aligning with the topic parameter, but does not add new details for slides, language, or instructions beyond their schema descriptions.

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 generates a real .pptx file from a topic or outline, differentiating from web editor links. The verb 'Turn' and resource are specific, and sibling tools like check_job are mentioned appropriately.

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 instructs to poll check_job for the download URL and mentions typical generation time (1-3 minutes), providing clear usage context. However, it lacks explicit when-not-to-use guidance or alternatives beyond the sibling list.

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?

No annotations are provided, so the description carries the full burden. It discloses asynchronous behavior (returns job_id), typical latency, and the underlying technology (PDFMathTranslate hosted at AI NetCafé). It could mention error scenarios or limits (e.g., file size), but overall it gives sufficient behavioral insight for a translation tool.

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 delivering key information: purpose and differentiation in the first, workflow and timing in the second, and tech stack in the third. No wasted words; front-loaded with the primary action.

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?

There is no output schema, so the description must cover the return and workflow. It explains that the tool returns a job_id and instructs to poll check_job for download links, which is complete for an async tool. It also provides typical timing. Missing details like error codes or file size limits are not critical for this context.

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 description does not need to explain parameters. However, it adds context about typical time for a few pages (related to pages parameter) and mentions the default target language indirectly. This adds marginal value beyond the schema, earning a baseline score of 3.

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, including formulas and figures, distinguishing it from ordinary translators that flatten the document. The verb 'translate' and resource 'PDF from a URL' are specific, and the differentiation from alternatives is explicit.

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 explains the workflow: returns a job_id, poll check_job for download links. It mentions typical time (20-60 seconds). It does not explicitly list when not to use this tool or compare to sibling tools, but the context of PDF translation with layout preservation is clear, and the polling guidance is useful.

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