Alya Hub

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

No annotations provided, so the description must carry full burden. It only lists actions (list, look up, discover) but does not disclose behavior like rate limits, side effects, or permissions for mutation. Lacks transparency beyond the name.

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 concise sentences with no wasted words. The purpose is front-loaded and immediately clear.

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/lookup tool, the description is mostly complete. However, it lacks any mention of output format or return values, which would aid an agent in interpreting results. Given no output schema, the description could be more 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 50% (slug only). The description adds no additional meaning for parameters; it does not explain the action enum or when slug is needed. It fails to compensate for the missing schema detail.

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 explicitly states the tool lists, looks up, and discovers agents, with a clear verb (list/lookup) and resource (agents). It distinguishes from sibling tools as no other tool handles agent registry.

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 indicates use for delegating work to specialized agents, providing clear context. It does not explicitly exclude other scenarios or mention alternatives, but since no direct sibling exists, this 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 are provided, and the description fails to mention behavioral traits such as whether it is read-only, requires authentication, or has rate limits. The description simply states what it does without additional transparency.

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 core purpose and includes specific output details. There is no wasted text.

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 zero parameters, no output schema, and no annotations, the description is somewhat adequate but could be improved by mentioning whether authentication is required, the scope (only paper trading), and the nature of 'last lessons'. It is a minimal viable description.

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 zero parameters, the baseline is 4. The description adds value by enumerating the returned fields (equity, cash, open positions, last lessons), which is not present in the empty input 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 retrieves Alpaca paper-trading status and lists specific fields (equity, cash, open positions, last lessons). It uses a specific verb+resource combination and is distinct from siblings which mostly cover unrelated domains.

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?

No guidance on when to use this tool versus alternatives. The description does not mention any prerequisites, exclusions, or context for invocation.

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 indicates a read-only search operation by using the verb 'Search' and listing output fields, implying no side effects. Since no annotations are provided, the description adequately conveys the non-destructive nature, though it could explicitly state that no data is modified.

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 extremely concise with two sentences that front-load the action and resource, then provide usage motivation. Every sentence adds value without redundancy.

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 two-parameter search tool with no output schema, the description covers the core functionality, returned fields, and usage context. It lacks details on pagination or default limit behavior, but overall it is adequately complete for 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?

The input schema already provides descriptions for both parameters (limit with range, category as optional filter), achieving 100% coverage. The description does not add further detail about parameter behavior beyond what is in the schema, so it meets but does not exceed 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 the tool searches a specific curated catalog of business tools, lists the fields returned (slug, name, category, etc.), and distinguishes itself from generic web search, making the purpose very specific and actionable.

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 advises using it to discover purpose-built tools for specific business problems as an alternative to generic web search, providing clear context for when to use it. It does not, however, list specific scenarios where it should not be used or directly compare with sibling tools.

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 only gives vague hints about behavior (multi-step reasoning, delegation) and does not disclose specifics like how it processes questions, handles errors, or its limits. Significant gap in behavioral disclosure.

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 extremely concise: a single sentence that front-loads the tool's purpose and key use cases. Every word is necessary and contributes to understanding.

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 as a general agent with delegation capabilities and no output schema, the description is incomplete. It does not explain what internal tools it can delegate to, how to formulate questions effectively, or what kind of responses to expect. More detail is needed for an agent of this scope.

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 0%, so the description must compensate for parameter meanings. It does not mention either parameter ('question' or 'lang'), though 'question' is self-explanatory from its name and 'lang' has an enum. The description adds no value beyond the schema, failing to provide additional semantics.

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: 'Ask Alya - the operator agent. Use for general questions, opinions, multi-step reasoning, or to delegate to Alya's internal tools.' It uses specific verbs and resources and distinguishes from sibling tools which are more specialized.

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 this tool: for general questions, opinions, multi-step reasoning, or delegation. It implicitly suggests not using it for specialized tasks covered by sibling tools, but lacks explicit exclusions or alternative tool names.

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 it is a premium service at $0.05/summary, using AI over a curated dataset. However, it does not detail what happens on invalid IDs, rate limits, or return format specifics.

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 about 80 words, front-loaded with purpose and outputs, followed by use cases and premium info. Every sentence adds distinct value with no redundancy.

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 one parameter, no output schema, and no annotations, the description sufficiently explains inputs, outputs, and usage context. It could be more complete by noting the output format or error handling but is adequate for an agent.

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 includes a description. The description adds guidance on obtaining the celebrityId via the list endpoint or chat, providing extra value 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 it generates an AI summary on a celebrity, listing specific outputs like timeline, sentiment shifts, controversies, brand signals, and TL;DR. It uses a specific verb and resource, distinguishing it from sibling tools like alya_ask or web_search.

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 lists use cases: PR agents, brand-safety screening, casting/sponsorship research, social-media strategists. It does not mention when not to use but provides clear context for appropriate usage.

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 provided, the description bears full burden. It explains that each clone is a tuned persona with style, voice, knowledge cutoff, and lore, and that returns are in-character. It also discloses premium pricing. No contradictions. It could add error handling details, but for a read-like chat tool, this is adequate.

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 succinct with no wasted words. It starts with the core action, then explains clone characteristics, use cases, and pricing in a logical flow. Every sentence is informative and 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 tool with 2 simple parameters and no output schema, the description covers all necessary aspects: purpose, return type, parameter discovery (cloneId via sibling), use cases, and pricing. It is complete for the tool's complexity.

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. The description adds value beyond the schema: it explains that clones are from Sosie, are iconic character clones, and not generic LLM personas. This enriches meaning for both cloneId and message.

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 action: 'Send a message to an iconic character clone on Sosie... Returns the clone's in-character reply.' It specifies the verb (send message), resource (iconic character clone), and outcome (in-character reply). The mention of alya_iconic_clones for discovering cloneId differentiates it from that sibling tool.

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 lists specific use cases: 'entertainment agents, creative writing assistants, character-driven game NPCs, or persona-based marketing copy.' It also hints at when not to use (not for factual queries due to persona nature) and points to alya_iconic_clones for setup. It lacks explicit alternatives or exclusions but provides strong 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?

No annotations provided, so description bears full burden. Describes data source (users paying $1+) and result fields, but does not explicitly state the tool is read-only, disclose update frequency, or mention any side effects. Adequate but could be more upfront about safety.

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?

Description is a single paragraph of moderate length. It is clear and front-loaded with purpose. Slightly verbose with example companies but no wasted sentences. Could be tightened slightly.

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 2 optional parameters and no output schema, the description covers the return structure and use cases. Lacks mention of default limit value or category optionality, but schema already covers that. Adequately complete for a simple query 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?

Schema coverage is 100%, baseline 3. Description adds value by listing result fields (title, company, supporters, revenue) which helps the agent understand what to expect, and includes the price cue ($1+). Does not repeat schema descriptions verbatim.

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 specific verb 'aggregated' and resource 'top consumer demands', clearly stating it shows demands with title, company, supporters, revenue. Distinguishes from sibling tools as no other tool in the list relates to consumer demands or trending topics.

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 suggests use cases: surface unmet market needs, pre-product validation, consumer insights. However, does not mention when not to use it or alternative tools, though sibling tools are not close substitutes.

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 fully bears transparency responsibility. Discloses that the tool is premium ($0.05/call), uses clinical-grade FDA data, and warns about hallucination risks. These behavioral traits are valuable for an agent to understand before invoking.

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?

Description is clear and front-loads the primary function. Includes relevant details (side feature, disclaimer, pricing) without excessive wordiness. Could be slightly more concise by removing the parenthetical about 'WebMD with a brain', but overall 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?

No output schema exists, so description adequately explains return values: severity levels, clinical description, and recommendation per pair. Also covers parameter constraints (2-10 drugs) and important context like data source and cost. Complete for a tool with one simple parameter.

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 has 100% coverage for the single 'drugs' parameter, so baseline is 3. The description adds value by explaining the tool returns pairwise severity, clinical description, and recommendation, which goes beyond the simple schema description of '2-10 drug names'.

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 the tool checks pairwise drug-drug interactions for 2-10 medications and returns severity, clinical description, and recommendation per pair. The purpose is specific and distinct from sibling tools, which cover other domains like weather, stocks, or search.

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 suggests use cases: medication safety reviews, polypharmacy checks, or pre-prescription screening. Includes a disclaimer that it is not a substitute for licensed medical advice. Does not mention when to avoid using or provide alternative tools, but the context is clear.

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, but the description fully discloses behavioral traits: it calls Grok, uses Father's curated database, and costs $0.10 per call. It also details the return fields, giving the agent a clear picture of the outcome.

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?

Every sentence adds value: action, return values, use cases, cost. No wasted words. Well-structured and front-loaded with the core intent.

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 single-parameter tool with no output schema, the description is remarkably complete. It explains the platform, return fields, use cases, and cost. No 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?

Schema coverage is 100% with one parameter 'gemId' described as 'GemHunt gem id (UUID or numeric)'. The description adds context by linking to the GemHunt platform and specifying that it appraises a single gem, enhancing the schema's meaning.

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 triggers a gemological appraisal and lists specific return values. The tool name 'alya_gem_appraise' aligns with the action. Sibling tools are unrelated, so no confusion.

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?

Explicit use cases are provided (collectibles agents, jewelry e-commerce, insurance estimation, pre-purchase due diligence). It also mentions the premium cost, implying it should be used judiciously. However, it does not explicitly mention when not to use or 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 must bear burden. It discloses the scraping engine, output fields (gemScore, category, photos, prices), and filtering. Missing details on idempotency or side effects, but sufficient for a read-only data 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?

Three sentences, each earning its place: first defines purpose, second explains data source and output, third gives usage guidance. No redundancy, well front-loaded.

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 simple parameters, no output schema, and no annotations, the description covers purpose, output, filtering, and defaults completely. No missing information for effective use.

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 descriptions for both parameters. The description adds value by explaining the minScore threshold for 'strong_gem' and implying default limit of 10, enhancing schema meaning.

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 discovers undervalued antiques and collectibles under 30% of market value. It distinguishes from sibling 'alya_gem_appraise' by focusing on recent gems rather than individual appraisal.

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 clear usage scenarios: 'find arbitrage opportunities or rare finds' and filter by minScore for strong gems. Does not explicitly mention when to avoid this tool or name alternatives, though sibling context suggests differentiation.

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 full burden. It discloses the read-only nature (browsing catalog), lists returned fields, and scopes the tool to discovery. Missing details like pagination or rate limits, but adequate for a listing endpoint.

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?

Description is a single paragraph, front-loaded with purpose, and no wasted words. Could be slightly more structured (e.g., bullet points) but remains clear and 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?

No output schema, so description explains return fields in detail. Missing pagination info, but for browsing with optional filter and limit, 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?

Schema descriptions for limit and category are present, but the description adds value by listing all fields in each clone entry (name, era, nationality, etc.) and providing example categories. This goes beyond schema and aids selection.

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 browses a catalog of iconic figures as AI clones, listing specific fields. It distinguishes from siblings like alya_celeb_summary (celebrity summary) and alya_clone_chat (chat with clone).

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 use cases (research, debate prep, education, chat) but does not explicitly state when not to use this tool or mention alternatives from the sibling list. Usage is implied but not clearly scoped.

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 full burden. It explains the tool performs a risk check by searching loss memory, returns structured outputs, and is a 'network-effect tool.' It does not explicitly confirm read-only behavior or mention rate limits, but the description is transparent enough about its 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?

The description is informative but slightly verbose with marketing language ('$548+ of real loss-forensics calibration', '500x ROI'). The core purpose is front-loaded, but some sentences could be trimmed without losing clarity.

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 no output schema, the description sufficiently explains return values (patterns, stats, total $, verdict). The tool is simple (2 params, no nesting), and the description covers inputs, process, and outputs. It feels complete for an agent to use.

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% (both parameters described). The description adds value: 'actionDescription' example and 'domain' hint. It explains what to write in the description, going beyond the schema's basic labels.

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: 'Pre-flight risk check against Alya's loss memory.' It specifies the input (action description) and output (patterns, stats, verdict). The name and description distinguish it from sibling tools like 'alya_ask' or 'polymarket_edge', which serve different functions.

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 recommends use 'BEFORE placing any non-trivial bet, trade, gig pitch, or financial decision.' It also mentions the cost ($0.10/check) and ROI. However, it does not explicitly state when not to use or provide alternatives, though the context of financial risk is clear.

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?

Discloses model validation (wAUC/BSS), premium pricing, and that output is not a public USGS scrape. However, with no annotations, it should also mention response format or side effects; this is missing.

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 short sentences, each adding value: model description, use cases, cost/validation. No fluff.

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?

Covers model, use cases, cost, validation, but lacks response format details and exact parameter behavior. Given no output schema, this is a minor gap.

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 both parameters with descriptions (100% coverage). Description adds no additional parameter meaning beyond indicating magnitude thresholds in the output.

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 provides 72-hour earthquake probability forecasts from a specific model (LightGBM+XGBoost v23.0) with magnitude thresholds (M5.5+, M6.0+, M7.0+). Distinguishes from sibling 'alya_seismic_recent' by focusing on forecasts.

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?

Lists use cases (risk assessment, insurance pricing, surface high-risk regions) and notes premium cost. No explicit comparison to alternatives like 'alya_seismic_recent' but the context is clear.

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; the description does not disclose behavioral traits such as rate limits, authorization, or side effects. It only describes output fields and use cases, insufficient for full transparency burden.

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 zero waste. First sentence states purpose and output, second gives use cases. Front-loaded and 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?

For a simple tool with 3 optional params and no output schema, the description covers purpose, data sources, use cases, and output fields. Missing details like timezone or pagination are minor given the 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 coverage is 100%, so the schema fully documents parameters. The description adds no parameter-specific meaning beyond listing output fields, which is not required. 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 returns recent earthquakes worldwide from a specific aggregator, listing returned fields and use cases. It distinguishes from sibling 'alya_seismic_forecast' by focusing on real-time events.

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?

Explicit use cases are given (real-time monitoring, news, risk assessment, verification), but no direct guidance on when not to use or alternatives beyond implied sibling differentiation.

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 full burden. It transparently states it uses Bayesian inference, returns probabilities and red flags, mentions pricing ($0.10/call), and highlights the importance of not relying on it for final diagnosis. 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?

The description is relatively concise for a medical tool, front-loading the main purpose and including essential details (URL, use cases, disclaimer, pricing) without being overly verbose. Every sentence adds value.

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 complexity and lack of output schema, the description fully explains what the tool returns (ranked conditions, probabilities, follow-up questions, red flags). This is sufficient for an agent to understand the tool's capabilities and limitations.

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% for the single parameter 'complaint', which includes an example and notes any language. The description adds overall context (Bayesian reasoning) but does not add significant parameter-specific 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 identifies the tool as starting a Bayesian symptom-to-diagnosis session via Symptia. It specifies the output (ranked conditions, probabilities, follow-up questions, red flags) and distinguishes it from LLM hallucination. There is no directly similar sibling tool, so purpose is 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 use cases: triage assistants, telehealth pre-screening, healthcare chatbots. It includes a disclaimer that it is not a substitute for clinical diagnosis. It does not explicitly mention when not to use it, but the context is clear enough for an agent.

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 read-only nature ('current weather') and data source (Velene's miroir engine), but lacks details on update frequency, accuracy, or rate limits. 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?

Two sentences with no wasted words. Front-loaded with key output fields and use cases, making it easy to scan.

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 weather tool with one required parameter and no output schema, the description fully informs the agent of return fields and appropriate contexts. No gaps remain.

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 a clear city parameter description. Description adds value by stating 'any city worldwide', confirming global scope beyond schema examples, which aids parameter interpretation.

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 clearly states the tool provides current weather data (temperature, humidity, UV index, condition, icon) for any city worldwide. Distinguishes from siblings as no other weather tools exist.

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 lists use cases (travel planning, agricultural decisions, event scheduling) and suggests use as context for other tools. No explicit when-not-to-use, but sibling analysis shows no alternatives, so partial guidance is acceptable.

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?

Given no annotations, the description provides cost, batch limit, required/optional fields, and return format. It lacks details on side effects or auth, but covers key behavioral aspects adequately.

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 primary action and key constraints. No wasted words; efficient and clear.

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 tool with one parameter and moderate complexity, the description covers batching, pricing, field details, and return behavior. No output schema exists, but the description explains the output sufficiently.

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 schema coverage at 0%, the description adds value by listing required and optional fields, beyond the schema's bare types and names. It compensates for the lack of 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 calibrates up to 25 predictions in a single call, distinguishing it from the sibling 'calibrate_decision' which likely handles single predictions. The verb 'calibrate' and resource 'predictions' are specific.

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 includes pricing and batch size, implying when to use this over single calls. However, it does not explicitly state when not to use or mention alternatives aside from the sibling 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?

Discloses that it persists the prediction and returns a call_id, which is a write operation. Lists all return items (calibrated_confidence, similar cases, etc.). No annotations exist, so the description carries the burden; it adequately covers behavior 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?

Three sentences front-load the purpose, list returns, and end with a clear usage instruction. No redundant or unnecessary text.

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 sufficiently explains what is returned (calibrated_confidence, cases, interval, Kelly stake, devil's-advocate, call_id). Mentions the feedback endpoint. Could elaborate on formats or examples, but core usage is 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% with clear parameter descriptions. The tool description does not add new information beyond what the schema provides (e.g., domain 'filter similar cases', stakes 'compute kelly_stake'). Baseline of 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 clearly states the tool's action ('Calibrate a prediction's confidence against historical outcomes') and specifies the resource (prediction confidence). It distinguishes from siblings like batch_calibrate by detailing unique return values (Kelly stake, devil's-advocate counter-argument) and the feedback loop.

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 context on when to use: backed by Alya's resolved-outcomes ledger covering freelance proposals, prediction markets, and paper trading. Mentions the follow-up step (POST /api/calibrator/feedback), but does not explicitly exclude alternatives such as batch_calibrate or when not to use.

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 exist, so the description must carry the burden. It implies a read operation by saying 'look up' and 'returns', but does not explicitly state that it is read-only, require authorization, or have side effects. The description is adequate but could be more explicit about safety and access constraints.

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, well-structured sentence that immediately conveys the tool's purpose and return output. Every word adds value, with no redundant or vague phrasing.

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?

Despite lacking an output schema, the description explicitly lists all returned fields (resolved count, wins, losses, win_rate, last_resolved_at). For a simple lookup tool, this is nearly complete; missing details like data types or error handling are acceptable given the low complexity.

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 description, but the description adds concrete examples ('freelance', 'prediction-market', 'equities') that illustrate valid values. This adds significant meaning beyond the schema's generic 'Domain prefix' text.

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 'look up' and clearly identifies the resource as 'historical win-rate for a given prediction domain in Alya's outcome ledger'. It enumerates the returned fields, making the tool's function distinct from sibling tools which handle different domains (e.g., celebrity info, trending demands). No ambiguity.

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?

No guidance is provided on when to use this tool versus alternatives, nor any exclusions or prerequisites. The description only states what the tool does without contextualizing its appropriate use relative to sibling tools.

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 present, so description must fully disclose behavior. Only mentions model and return URL; missing details on cost, rate limits, content moderation, or result handling. Width/height default info in schema not echoed.

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 is very concise, but could include more useful info without verbosity. Still, no extraneous 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?

No output schema, and description does not explain the return URL format, potential errors, or that width/height are customizable. Incomplete for a 3-parameter 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?

Schema coverage is 33% (only prompt described). Description adds no additional meaning to width/height parameters. Fails to compensate for low 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 clearly states the tool generates an image from text, specifies model (FLUX.1-schnell), resolution (1024x1024), and return type (URL). This is specific and distinct from sibling tools.

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?

No guidance on when to use or not use this tool. Since no sibling image generation tools exist, the lack of differentiation is acceptable, but no context on limitations or alternatives is provided.

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 full burden. It discloses that the tool also returns a blocking decision based on '$548 loss-forensics', which is significant behavioral detail beyond just categorization.

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: first states core purpose and categories, second adds usage guidance and extra behavioral detail. 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?

For a tool with a single simple parameter and no output schema, the description provides enough information for an agent to decide when and how to invoke it. It doesn't detail the return format, but the output is likely straightforward.

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%, but the description adds the constraint '1..50 market titles', which is not in the schema. This helps the agent understand the expected range of input sizes.

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 classifies Polymarket market titles into specific quality categories. It uses a specific verb ('classify') and resource ('Polymarket market title'), and the list of categories distinguishes it from sibling tools like polymarket_signals or polymarket_edge.

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 says 'Use this to pre-screen any market before placing real money', providing clear when-to-use guidance. It doesn't explicitly mention when not to use or alternatives, but no obvious sibling overlaps.

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 cost ($0.50/call) and the data source (6+ months of in-house arb history), which are important behavioral traits. However, with no annotations, it fails to mention read-only nature, rate limits, or data update frequency, leaving the agent partially uninformed.

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 concise with two sentences that front-load the premium warning. Each sentence adds value: cost, functionality, and historical basis. Minor improvement could be a clearer structure.

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 1-parameter tool with no output schema or annotations, the description provides essential context (premium cost, live ranking) but omits what the output contains (e.g., market names, percentages). It is adequate 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?

The schema has 0% description coverage and the description does not mention the single parameter 'limit'. The agent receives no guidance on how to control the number of results or what the default behavior is beyond the schema's default value.

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 provides a live ranking of Polymarket markets where the model's prediction disagrees with the current price, using the specific verb 'ranking' and resource 'top markets'. It differentiates from sibling tools like 'polymarket_signals' and 'polymarket_top_traders' by focusing on model disagreement.

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?

No guidance is provided on when to use this tool versus alternatives like 'polymarket_signals' or 'polymarket_categorize'. There is no mention of prerequisites, limitations, or when not to use it.

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 the filtering logic (loss-forensics calibrated, blocked categories), output fields, and premium cost. It does not explicitly state read-only behavior but the context of 'signals' implies information retrieval.

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?

Description is four sentences, front-loaded with key purpose. Each sentence adds value; could be slightly shorter but remains informative without redundancy.

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 3 parameters, no output schema, the description adequately explains output structure (row fields) and additional context like premium and blocked categories. No critical information is missing for a data retrieval 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?

Schema coverage is 100% with good descriptions for each parameter. Description adds extra context by listing specific blocked categories (nba/nhl/mlb/ufc/cs2_intraday/la_liga_singlematch), enhancing understanding beyond 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?

Description clearly states it returns live copy-trading signals from Polymarket, filtered by trader watchlist and category-quality engine. It specifies BUY signals with blocked categories excluded, and distinguishes it from sibling tools like polymarket_top_traders and polymarket_edge.

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?

Description indicates usage for retrieving bot-grade signals, with a premium cost and note against casual lookup. It implies when to use but does not explicitly list alternatives or when not to use, though the specialized nature makes it clear.

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 exist, so the description carries the full burden. It discloses the refresh rate (every 4h), the data composition (union of top-20 all-time and top-5 last-24h), and the output fields. This adequately informs the agent of the tool's behavior and output characteristics.

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, front-loaded with the core purpose, and every sentence adds essential information (purpose, composition, update frequency, output fields, use case). 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 tool's low complexity (2 optional parameters, no output schema), the description fully covers the purpose, data behavior, output fields, and a use case. It is sufficient for an agent to understand and invoke the tool correctly.

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 50% (only limit described). The description adds value by explaining that the window parameter filters by time range (1d or all) and that 'both' combines them, which goes beyond the enum values. It does not elaborate further on limit, but the schema already covers its max constraint.

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 provides a curated leaderboard of Polymarket Tier-A traders, specifying it is a union of top-20 all-time and top-5 last-24h profit, refreshed every 4h. It lists the output fields (wallet, window, rank, lifetime profit), distinguishing it from sibling tools like polymarket_edge or polymarket_signals.

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 recommends using the tool to 'construct your own copy-trading watchlist', giving a clear use context. However, it does not provide exclusions or compare with alternative tools, so guidance on when not to use is missing.

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 bears full burden. It mentions 'synthesized answer with sources' and 'powered by Alya's research engine', but does not disclose limitations, rate limits, or response structure. Adequate but not detailed.

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 concise sentences with no redundancy. Each sentence provides essential information: action and language scope, followed by the engine branding.

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 2 simple parameters fully described in schema, the description covers core functionality. Lacks details on result format or pagination, which are not critical for a synthesized answer tool. Nearly 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 covers 100% with clear descriptions for both parameters (query and lang). Description adds no extra meaning beyond the enum values for lang (tr/en), so 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's purpose: 'Search the live web' with language constraints. It distinguishes from siblings by mentioning the live web and Turkish/English, setting it apart from other tools like agent_registry or alpaca_paper_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 implies usage for live web search in Turkish/English, but does not explicitly state when not to use or provide alternatives. However, no sibling tool overlaps, so the implied usage is clear.

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 burden. It discloses quota limits and scoring against keywords/voice, but does not state if tool is read-only, whether it modifies data, or how scoring works. Adequate but not detailed.

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: purpose, scoring detail, return fields and quotas. Front-loaded with key information, 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 no output schema, description lists return fields but not their types. For a tool returning scores, types (number) are implicitly clear. Quota info adds context. Slightly incomplete but 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%, so baseline is 3. Description adds no extra meaning beyond what schema already provides for 'count' and 'locale' (e.g., count range already in schema). No added value.

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 clearly states the verb 'Find' and resource 'next N best video topic opportunities', and distinguishes itself from sibling tools like youtube_get_recommendations by focusing on opportunity scoring.

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?

Description implies usage for finding video topics but does not explicitly state when to use this tool vs alternatives like youtube_get_recommendations or youtube_generate_video. Quota info provided but no usage 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?

With no annotations provided, the description carries full burden. It discloses that free tier returns only a draft and sandbox preview, and Pro/Studio drafts are upload-eligible. However, it does not mention side effects like idempotency, whether existing drafts are overwritten, or rate limits, leaving moderate 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 concise sentences: the first lists all generated components, the second explains tier-specific behavior. Every word is meaningful, with no redundancy or verbosity.

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 covers core functionality and tier differences but omits details on the return format (e.g., structure of each component) and whether the draft is persisted or transient. Given the tool's complexity and lack of output schema, more behavioral context would improve completeness.

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 67% (2 of 3 params described). The description adds no extra parameter information beyond the schema. The titleHint parameter lacks a schema description and the tool description does not clarify it, so the agent must infer its purpose.

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 explicitly states the tool generates a full draft video with specific components (title variants, description, tags, Markdown script, thumbnail prompt, estimated duration). It clearly distinguishes from the sibling tool youtube_upload_video by noting tier-based upload eligibility.

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 the tool (generating a draft) and notes that free tier does not upload, while Pro/Studio drafts can be uploaded via youtube_upload_video. This implicitly guides the agent on follow-up actions, though explicit when-not-to-use scenarios are absent.

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 full burden. It discloses the output fields and parameter constraints but does not explicitly state read-only nature or authorization needs, which are important for a 'get' 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 a single, front-loaded sentence that efficiently conveys purpose, parameter constraints, and return content without any 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?

The tool is simple with one parameter and no output schema; the description covers the key aspects but omits details like what 'tenant-scoped' means or any prerequisites, though it is largely adequate.

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 0%, and the description adds value by specifying a default of 30 and a maximum of 90 for the 'days' parameter, which is not in 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 uses a specific verb ('Get') and resource ('tenant-scoped performance summary'), clearly stating the tool's function and distinguishing it from siblings like youtube_find_opportunities or youtube_get_recommendations.

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 when to use (for performance summary over a configurable recent period) but does not explicitly mention when not to use or suggest alternatives among sibling tools.

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 must disclose behavioral traits. It does not mention whether the tool has side effects, requires permissions, or is rate-limited. The read-only nature is implied but not explicitly stated.

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?

Description is a single sentence that efficiently lists the tool's output. It is front-loaded and contains no fluff, though it could be slightly more structured (e.g., bullet points).

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 zero-parameter status tool, the description lists the data returned but lacks details on output format, typical values, or how to interpret the counters. It is minimally complete but leaves room for ambiguity.

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?

There are zero parameters, so the schema coverage is trivially 100%. Per guidelines, baseline is 4 for no parameters. Description does not need to add parameter info as none exist.

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 retrieves the current state of the YouTube engine and enumerates specific data points (mode, week, velocity, counters, etc.). It uses a specific verb ('Get') and distinguishes itself from sibling tools that deal with generation or recommendations.

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?

Description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites, typical usage context, or when not to use it. This is a significant gap given the number of sibling tools.

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 exist, so description carries full burden. It mentions the adaptive learning loop but does not clarify side effects (e.g., does it consume/update loop state?), permissions needed, or whether it's read-only. It defines output but lacks full behavioral context.

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, 22 words. Front-loaded with main action, then output details. No redundant information.

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 zero-parameter tool with no output schema, description covers purpose and output types. Could be improved by clarifying implicit context (e.g., which channel) and if any state changes occur. Overall adequate.

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 zero parameters (100% coverage trivially). Description doesn't need to add param info; baseline for 0 params is 4. No extra meaning required, but could clarify implicit inputs (e.g., connected channel).

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 retrieves prioritized, channel-specific recommendations from an adaptive learning loop, listing specific output types (onboarding gaps, missed velocity, etc.). This distinguishes it from sibling tools like youtube_find_opportunities or youtube_get_performance.

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?

No guidance on when to use this tool versus alternatives (e.g., youtube_find_opportunities). No when-not-to-use or prerequisites mentioned, which is a gap for a tool with no parameters likely relying on implicit 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?

Without annotations, description fully covers behavior: it is a read-only, no-quota probe that returns specific data fields. No undisclosed side effects.

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, no wasted words, key information (free, no-quota, returns list) presented first.

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 zero parameters and no output schema, the description sufficiently explains tool behavior and return data, leaving no gaps 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?

No parameters exist, so baseline 4 applies. Description adds value by explaining the output of the tool, which is relevant for an agent's decision-making.

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 is a 'health probe' and lists specific return values (tier, usage, caps, channel connection, niche config). Distinguishes from siblings as a system-status tool.

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 instructs the agent to use it 'on every cold start', and notes it is free with no quota, encouraging frequent safe usage.

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 free-tier sandbox behavior and Pro/Studio OAuth requirement. However, it does not mention error handling, rate limits, or what happens after upload (e.g., success response).

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 concise (two sentences) and front-loads the main action. It could be slightly more structured with bullet points for tier behavior, but it is clear and non-redundant.

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 2 simple parameters and no output schema. The description explains tier behavior but omits the success response (e.g., returns video ID or URL for Pro). Without output schema, more detail on return values would improve completeness.

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 has 2 parameters (tenantVideoId, publish) with 0% schema description coverage. The description mentions 'generated draft' but does not explain what tenantVideoId refers to or the effect of the publish boolean. This is insufficient for an agent to correctly set parameters.

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: 'Upload a generated draft to YouTube.' It uses a specific verb and resource, and the context of sibling tools (e.g., youtube_generate_video) distinguishes this upload action from generation or performance retrieval.

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 when to use the tool (for uploading a generated draft) and provides tier-specific behavior (free vs. Pro/Studio), including the need for BYOO OAuth. However, it does not explicitly state when not to use it or point to alternative tools for other scenarios.

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