Alya — The Hub for Autonomous Agents

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 that the tool is read-only, safe, or any auth requirements. The description focuses on purpose, not behavior.

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, but contains slight redundancy ('list, look up, and discover'). It is front-loaded with the core function, 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?

Given no output schema, the description does not explain return values or how to use the results for delegation. It leaves gaps in understanding for an AI 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?

The input schema has 50% description coverage, but the description adds no extra meaning beyond what the schema already provides. It references 'list' and 'look up' which match the action enum, but does not clarify slug format or usage.

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 and looks up agents in the Alya Hub catalog, with the added context of delegating work to specialized agents. This differentiates it from sibling tools which have unrelated purposes.

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 suggests using the tool to delegate work, but does not provide explicit guidance on when not to use it or alternatives. For a straightforward catalog tool, this is adequate but lacks depth.

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 partially discloses behavior by listing return fields. However, it does not mention authentication requirements, call frequency, or whether the data is live or cached, which are relevant for a trading status 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, efficient sentence that front-loads the purpose and enumerates the return fields. 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 parameters and no output schema, the description fully captures the tool's functionality by listing the data it provides. It is adequate for a simple status 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?

The tool has zero parameters, so the schema coverage is 100%. The description adds no parameter info because none is needed, earning a baseline of 4.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool gets the current Alpaca paper-trading status and lists specific data fields (equity, cash, open positions, last lessons). It uses a specific verb and resource, distinguishing it from unrelated siblings like image_gen 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 provides no guidance on when to use this tool versus alternatives or any exclusions. Siblings are not functionally similar, but no explicit context is given.

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 carry the burden. It implies a read-only search but does not explicitly state it is non-destructive or discuss any side effects, rate limits, or authentication needs. Adequate but lacks explicit behavioral details.

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 describes what the tool does and output fields, second gives usage context. No unnecessary words, well-structured, and front-loaded with key 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?

Given the lack of output schema, the description adequately summarizes the output fields. It could mention pagination or default behavior for the limit parameter, but overall provides sufficient context for a simple search tool. Distinguishes well from sibling tools.

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%, as both parameters have descriptions in the input schema. The tool description adds marginal value by listing example categories, but does not significantly enhance understanding beyond what the schema already provides.

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 curated catalog of business/AI/HR/marketing tools, specifying the fields returned. It distinguishes itself from generic web search, making the purpose unmistakable.

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 says 'Use to discover purpose-built tools' and 'alternative to generic web search when looking for a tool that solves a specific business problem', providing clear when-to-use and when-not-to-use 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 are provided, so the description must fully disclose behavior. It mentions delegation to internal tools, which is key, but does not explain potential side effects, cost, response format, or what happens if the agent cannot answer. The transparency is moderate.

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 tool's identity and core use cases. Every sentence provides value without 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?

For a general-purpose agent tool with no output schema, the description lacks details on return values, limitations, or behavior in edge cases. It provides a functional overview but is not fully comprehensive given 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?

The input schema has 0% parameter description coverage, so the description should compensate. However, it does not mention or elaborate on any parameters. The schema itself is clear (question and lang), but the description adds no 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 states 'Ask Alya - the operator agent' which provides a verb-resource pair, and the use cases (general questions, opinions, reasoning) distinguish it from sibling tools that are task-specific (e.g., web_search, image_gen). However, the verb 'ask' is somewhat vague compared to more specific actions.

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 when to use: for general questions, opinions, multi-step reasoning, or delegation. It implies that for specific tasks like web search or image generation, sibling tools should be used instead, but does not explicitly state when not to use or provide 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?

With no annotations, the description fully bears transparency. It explains it is AI synthesis over CelebTrack's dataset (not a generic scrape), mentions cost ($0.05/summary), and describes return content. Lacks rate limits or authentication needs, but is adequate for a read-only summary 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 concise (~75 words) and front-loaded with purpose. It could be slightly more structured (e.g., bullet points for return items), but remains efficient and easy to parse.

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 or annotations, the description covers purpose, returns, use cases, and premium note. It does not mention error handling or data freshness, but is largely complete for its 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 a clear description for the single parameter celebrityId. The tool description adds no extra semantic detail beyond what the schema provides, 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 generates an AI celebrity summary from CelebTrack, listing specific outputs (timeline, sentiment, controversies, brand signals, TL;DR). It distinguishes from siblings by focusing on celebrity intelligence, with no overlap with sibling tools like web_search or polymarket_*.

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 target users (PR agents, brand-safety screeners, etc.) and use cases. It does not mention when NOT to use or compare to alternatives, but the context is clear and sufficient for an agent to decide.

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 key behavioral traits: the clone is a tuned persona (not generic LLM), mentions premium pricing ($0.05/message), and indicates the clone has style/voice/lore. This adds value beyond the schema, though it lacks details on rate limits or authentication.

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 very concise at four sentences, with the most critical functionality stated first. Every sentence adds value; there is no redundant or extraneous 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?

Given no output schema, the description minimally states the return value ('in-character reply'). While sufficient for a simple chat tool, it could be strengthened by mentioning the response format (e.g., text only) or error conditions.

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 adds little beyond what the schema already provides. The description notes cloneId can be discovered via a sibling tool and message has a 1-2000 char limit, but this is already 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?

Description explicitly states the verb ('Send a message') and the resource ('iconic character clone') and specifies the return value ('Returns the clone's in-character reply'). It distinguishes this tool from sibling 'alya_iconic_clones' which is for discovery, not interaction.

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 use cases ('entertainment agents, creative writing assistants...') which implies appropriate contexts. However, it does not explicitly state when not to use this tool or mention alternatives besides the sibling discovery tool.

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 is the sole source of behavioral info. It indicates the tool returns aggregated data (read-like operation) but does not disclose rate limits, data freshness, or the read-only nature. 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?

The description is two sentences that are front-loaded with the tool's core purpose and followed by use cases. Every word is purposeful; 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 2 parameters and no output schema, the description sufficiently explains the output structure and use cases. Missing context includes whether results are sorted (e.g., by popularity) and pagination details. Good but not exhaustive.

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 covers 100% of parameters with descriptions. The description adds no extra meaning beyond what the schema provides, such as specifying the format of category or limit behavior. 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 that the tool aggregates top consumer demands globally from AskFor, listing specific fields (title, target company, supporter count, total revenue) and use cases (surface market needs, validation signals). This distinguishes it from sibling tools like alya_app_directory or alya_weather_now.

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 surface unmet market needs, pre-product validation, or generate consumer insights, providing clear context. However, it does not mention when not to use or suggest alternative 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 has the full burden. It explains the output but does not disclose if the tool is read-only, error handling for invalid drugs, or any rate limits. The behavior is implied but not fully transparent.

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, background, usage context. No wasted words, front-loaded with key 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?

Without an output schema, the description explains the return structure (severity, description, recommendation per pair). It also includes a disclaimer. It could mention whether results are returned as a list but is otherwise 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?

Input schema has 100% coverage and describes the array and drug names. The description adds '2-10 medications' and 'generic or brand', which mostly repeats the schema. So the value added is minimal.

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 checks pairwise drug-drug interactions for 2-10 medications and specifies the returned fields (severity, clinical description, recommendation). This distinguishes it from sibling tools like alya_weather_now or polymarket_categorize.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

It provides explicit use cases: medication safety reviews, polypharmacy checks, pre-prescription screening. It also includes a disclaimer about not substituting medical advice. However, it does not explicitly state when not to use or compare to 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. Description mentions premium cost, Grok AI call, and curated database, but does not specify if the operation is read-only, if it modifies state, authentication needs, or error behavior.

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 well-structured with action first, then outputs, then use cases, and cost. Some redundancy could be trimmed, but overall 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 no output schema, the description compensates with detailed output list. However, it lacks error handling or limitation info. Sufficient for a simple single-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 100% with a clear description of gemId. The tool description adds context about the platform but does not provide additional semantic detail beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Trigger a Grok-AI gemological appraisal') and the resource ('a single gem on GemHunt'), with a detailed list of outputs. However, it does not explicitly differentiate from sibling tools like 'alya_gems_recent'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

Provides explicit use cases (collectibles agents, jewelry e-commerce, insurance estimation, pre-purchase due diligence) and premium cost details. Lacks when-not-to-use guidance or alternative tool mentions.

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 the full burden. It explains that the tool searches a scraped database and returns items with specific attributes, implying a read-only operation. It does not mention any side effects, destructive actions, or authentication needs, but the description is transparent about its data source and intended usage.

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-loading the core purpose and then providing essential details (data source, returned fields, parameter usage). Every sentence earns its place without redundancy or 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?

Given the tool's simplicity (2 optional params, no output schema), the description comprehensively covers inputs (limit, minScore), outputs (gemScore, category, photos, asking price, value range), and the intended use case. It does not require additional context like rate limits or authentication, as those are not implied by the tool's nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the parameters with defaults and valid ranges. The description adds semantic value by explaining that `minScore` of 60 indicates 'strong_gem' status and contextualizes the threshold. This goes beyond what the schema provides, improving the agent's understanding of parameter significance.

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 defines the tool as discovering undervalued antiques, collectibles, and rare items priced ≤30% of market value. It specifies the data source (GemHunt's scraped engine) and includes key returned fields (gemScore, category, etc.). The purpose is distinct from sibling tools, which focus on other domains (e.g., earthquakes, weather, stocks).

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 the tool to 'find arbitrage opportunities or rare finds' and mentions filtering by minScore for 'strong_gem' status. However, it does not provide explicit when-not-to-use guidance or alternative tools, though the sibling set is diverse and no direct alternative exists.

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 describes the tool as a browse operation (likely read-only) but does not disclose behavioral traits like authentication needs, rate limits, or destructive potential. The description is adequate but lacks depth for full 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 two sentences: the first defines the tool's function, the second lists fields and use cases. It is front-loaded and concise with 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?

No output schema exists, but the description enumerates the returned fields (name, era, nationality, bio, personality summary, knownFor, qualityScore, category), which covers what an agent needs to understand the output. Limited only by missing pagination or sorting details, which are not critical for a simple list 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?

Both parameters ('limit' and 'category') have full schema coverage. The description adds context about the catalog content but does not enhance parameter semantics beyond the schema's own descriptions. 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 browses a catalog of iconic figures as AI clones, listing the fields included. It is specific and distinguishes from siblings, none of which are similar clone-figures 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?

The description provides explicit use cases: 'research, debate prep, education, or chat use cases.' It does not specify when not to use or name alternatives, but the sibling list shows no direct competitors, making guidance 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?

With no annotations, the description carries full burden. It details what the tool returns (top patterns, stats, verdict) and notes the cost ($0.10/check) and network effect. It implies a read-only operation, but could be more explicit about side effects or authorization needs.

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 three sentences that are front-loaded with the core purpose. Each sentence adds value: purpose, usage, and additional context (cost, network effect). No redundant or filler content.

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 and lack of output schema, the description is remarkably complete. It covers what the tool does, what it returns, when to use it, cost implications, and even the network effect. It provides sufficient information for an agent to invoke it 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 100%. The description adds context beyond the schema by explaining the purpose of each parameter (e.g., 'Optional domain hint to narrow the search') and provides examples for actionDescription. This enhances understanding beyond the schema definitions.

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 as a 'pre-flight risk check against Alya's loss memory' that returns similar past patterns, stats, and a verdict. It uses specific verbs and resources, distinguishing it from sibling tools like polymarket_edge 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?

Provides explicit usage guidance: 'Use BEFORE placing any non-trivial bet, trade, gig pitch, or financial decision.' While it doesn't explicitly state when not to use or name alternatives, the context is clear and the instruction is actionable.

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 full burden. It discloses the model version, feature count, grid resolution, and magnitude thresholds. However, it omits limitations such as accuracy metrics, data freshness, or whether the forecast is updated in real-time. The predictive nature is implied but not thoroughly explained.

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: the first provides core details (model, timeframe, grid, thresholds) and the second suggests use cases. Every word adds value; no redundancy or filler.

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 simple parameters and no output schema. The description explains the output format (1° grid cell with probabilities for three magnitudes). While it lacks details on interpreting probabilities or pagination, the information is sufficient for a well-informed agent to use the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage for both parameters ('limit' and 'minProb55'). The description adds context by mentioning magnitude thresholds (M5.5+, M6.0+, M7.0+), but it does not elaborate on parameter usage beyond what the schema already provides.

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 72-hour earthquake probability forecasts using a specific model (SeismoAI's LightGBM+XGBoost). It specifies the resource (earthquake probabilities) and the action (forecast). This distinguishes it from siblings like 'alya_seismic_recent' which likely provides recent earthquake data.

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 explicit use cases: 'risk assessment, insurance pricing, or to surface high-risk regions before events happen.' However, it does not provide guidance on when not to use the tool or mention alternative tools for related tasks (e.g., 'alya_seismic_recent' for historical data).

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 does not disclose behavioral traits such as idempotency, rate limits, or behavior with no results. The description only states what it returns.

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 front-loaded purpose and no extraneous information. Every word 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?

For a simple query tool with no output schema, the description adequately covers what the tool does and typical use cases. However, it lacks details on return structure or edge cases.

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 parameters are fully documented in the schema. The description adds no extra meaning 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 it returns recent earthquake data from a specific aggregator (USGS+EMSC+GFZ) with specified fields. The name 'alya_seismic_recent' is distinct from sibling 'alya_seismic_forecast', so purpose is well-defined.

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 appropriate use cases (monitoring, news, risk assessment) but does not explicitly exclude scenarios or mention alternatives like 'alya_seismic_forecast' for forecast data.

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 Bayesian inference, output structure, and premium cost. Lacks details on data privacy but is informative.

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?

Well-structured with front-loaded purpose, then details and usage. Slightly lengthy but every sentence adds value; minimal waste.

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 complexity (medical tool) and no output schema, description is complete: return format, use cases, caveats, and cost are all 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?

Only one parameter with 100% schema coverage. Description adds value by specifying 'in their own words (any language)' and providing an example, enhancing schema guidance.

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 a Bayesian symptom-to-diagnosis session via Symptia, returning ranked conditions, follow-up questions, and red flags. It distinguishes from sibling tools by its medical reasoning focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

Explicitly states when to use (triage, telehealth, healthcare chatbots) and when not to use (not a substitute for licensed diagnosis). Implicitly distinguishes from non-medical 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?

Discloses data source ('Velene's miroir engine (OpenMeteo + Grok-fused)') and what data is returned. With no annotations, it carries the burden; it mentions fusion but lacks details on potential limitations like data freshness or rate limits. Still helpful.

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 with no wasted words. The main output is front-loaded in the first sentence, followed by usage guidance and data source.

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 simplicity (1 param, no output schema), the description covers purpose, usage, and output fields. Could specify return format but sufficient for a 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?

Schema coverage is 100% for the single parameter 'city,' which already provides examples. The description adds 'for any city worldwide' but no further parameter semantics 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 explicitly states 'Current weather (temperature °C, humidity %, UV index, condition, icon) for any city worldwide,' using a specific verb and resource. It clearly distinguishes from sibling tools like alya_seismic_forecast and 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?

Provides explicit use cases: 'travel planning, agricultural decisions, event scheduling, or as context for other tools.' While no explicit exclusions or alternatives are given, the context is clear given no other weather tools exist.

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?

Despite no annotations, the description discloses key behaviors: flat pricing ($0.005 per call), batch limit of 25, required/optional fields, and output order. It adds value beyond the schema.

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 pack all essential info: purpose, limit, pricing, and parameter guidance. Front-loaded and efficient with zero waste.

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 clearly states the return type (array of calibration results matching input order), and covers input requirements fully.

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 0% schema description coverage, the description fully compensates by listing that each item must include 'prediction' and optionally 'confidence', 'domain', 'stakes', plus the maxItems 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 'Calibrate up to 25 predictions in a single MCP call', using a specific verb (calibrate) and resource (predictions), and distinguishes from sibling 'calibrate_decision' by emphasizing batch and size limit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

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

It provides context on when to use (batch calibration) and mentions cost and structure, but does not explicitly state when not to use or compare with alternatives beyond the sibling naming.

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 persistence of the prediction, returning call_id, and the ability to provide feedback. It also describes outputs like devil's-advocate counter-arguments. This provides good behavioral context beyond basic functionality.

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 well-structured sentences: purpose and returns, backing ledger, and side effect/feedback loop. No wasted words, front-loaded with core 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?

Despite no output schema, the description enumerates all returned items and explains the feedback mechanism. It covers persistence and usage context adequately for a 4-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 100%, and the description adds some context (e.g., 'Used to filter similar cases' for domain) but largely echoes the schema descriptions. 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 verb 'Calibrate' and the resource 'prediction's confidence against historical outcomes'. It lists specific return values, distinguishing it from siblings like batch_calibrate.

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 a single prediction calibration but does not explicitly differentiate from sibling tools like batch_calibrate or get_domain_accuracy. It mentions a feedback loop but lacks when-to-use or when-not-to-use 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 must bear burden. It lists returned fields but does not disclose any additional behavioral traits such as permission requirements, rate limits, or side effects. Adequate but not exceptional.

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 front-loaded with purpose and return value. No wasted words; every part is essential.

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 1-parameter tool with no output schema, the description is adequate: explains purpose and return fields. Could add caveats about calculation or data freshness, but not strictly necessary.

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 covers 100% of parameters with descriptions. Description adds context by naming the source (Alya's outcome ledger) and providing examples, which helps the agent understand the domain parameter better.

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 that the tool looks up historical win-rate for a given prediction domain, specifying the resource and the returned fields. Distinguishes itself from siblings which cover different 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?

Implies usage when needing a domain's historical win-rate, but no explicit when-not-to-use or alternatives. Since no sibling tool overlaps directly, the lack of guidance is less critical but still 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 are provided, so the description carries the full burden. It only states it generates an image and returns a URL, but omits important behavioral traits like rate limits, authentication requirements, or any side effects (e.g., costs, content policies). The model mention adds some value, but overall insufficient.

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, front-loaded with the main action, and contains only two sentences. Every word adds value—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?

Given the low schema coverage (33%) and no output schema, the description should provide more context about parameter usage, model behavior, and output format. It only covers the basic purpose and return type, leaving gaps for a complete understanding.

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 only 33% (only prompt has a basic description). The description mentions '1024x1024' but does not explain the width and height parameters beyond that, nor does it add detail to the prompt parameter. The default dimensions are noted, but parameter semantics are largely missing.

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's function: generate a 1024x1024 image from a text prompt using FLUX.1-schnell, and that it returns a URL. The verb 'Generate' plus the resource 'image' and model specification makes the purpose very clear. Sibling tools are unrelated, so no differentiation needed.

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 no guidance on when to use this tool, such as prerequisites, limitations, or alternatives. While siblings are unrelated, the lack of any usage context reduces its helpfulness.

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 the full burden. It reveals key behavioral traits: classification into categories and a blocking decision based on '$548 loss-forensics.' This goes beyond a simple 'classify' and adds specific financial context, though it could mention rate limits or 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?

The description is two sentences long, front-loads the purpose and categories, and includes a clear usage directive. Every word adds value; no filler or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no output schema, the description explains that it returns whether Alya would block the market, but it could be more explicit about also returning the category assignments. However, for a simple single-parameter tool, it provides sufficient context for an AI agent to understand the tool's role and output.

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 covers the parameter 'titles' with a description of '1..50 market titles to categorize.' The description adds meaning by indicating what will be done with the titles (classification into categories, blocking decision) and the outcome (blocking flag), which complements the schema well.

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 classifies Polymarket market titles into specific categories (e.g., wc_future, tail_safe_no) and returns whether Alya would block the market. This verb+resource combination is specific and distinguishes it from sibling tools like polymarket_edge or polymarket_signals, which likely serve different purposes.

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 tells when to use this tool: 'Use this to pre-screen any market before placing real money.' It provides clear context but does not mention when not to use it or suggest 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?

No annotations provided, so the description carries full burden. It discloses the tool returns a ranking based on disagreement, which is a read operation. However, it does not explain the meaning of 'edge' or any potential behavioral side effects. It is minimally transparent but not misleading.

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 immediately states the action and result. It is front-loaded and contains no extraneous information. Every word is necessary.

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 annotations, no output schema, and a simple parameter, the description provides basic context but leaves gaps. It does not explain the output format, how the ranking is computed, or what 'edge' means. It is complete enough for a simple tool but lacks depth for full understanding.

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 one parameter 'limit' with no description, and the description does not mention it or clarify its purpose. The default value of 5 hints at a count, but the description adds no value beyond the schema. With 0% schema description coverage, the description should compensate, but it fails to.

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 Alya's edge ranking on Polymarket, specifically the top markets where Alya's model disagrees with current price. It uses a specific verb ('Get') and identifies the resource ('edge ranking'). It distinguishes itself 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 implies the tool is for finding markets with disagreement, but it does not provide explicit guidance on when to use it versus alternatives, nor does it mention when not to use it. It lacks usage context such as prerequisites or typical scenarios.

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, the description bears full burden. It discloses the source (Tier-A watchlist, loss-forensics), default exclusions, and output fields. It does not mention safety, but the tool is clearly a read/signal retrieval, not destructive.

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 packed with information in two sentences, front-loaded with the main purpose. It could be slightly more concise but is efficient for the complexity.

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 lists output fields (trader, market title, side, etc.) and explains filtering logic (blocked categories, lookback). This covers expected return structure for a signal 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 parameter descriptions. The description adds context by explaining the 'includeBlocked' default and linking hours to lookback. It reinforces the parameter meanings without being redundant.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns 'Live Polymarket copy-trading signals' from a specific watchlist and quality filter, and lists the output fields. It distinguishes itself from siblings like polymarket_categorize and polymarket_edge by focusing on 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 explains default behavior (blocked categories excluded) and parameters (hours, limit, includeBlocked). However, it lacks explicit guidance on when to use this tool versus alternatives, though the purpose implies it's for signals.

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 burden of transparency. It discloses the refresh cadence (every 4h), the composition of the leaderboard (union of top-20 all-time and top-5 last-24h), and the output fields. For a read-only tool, this is adequate but could mention safety or idempotency.

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 purpose, and every word earns its place. It is efficient and easy to parse.

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 and moderate complexity, the description covers the purpose, refresh rate, output fields, and use case. It could elaborate on the return format (e.g., JSON array) or sorting, but it is sufficiently complete for an agent to understand the tool's behavior.

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% (limit has a description, window does not). The description adds context for the window parameter by mentioning 'window (1d/all)' in the output, but does not explain the 'both' option or provide additional semantics beyond the enum. The limit parameter's schema description is clear, so the description adds marginal 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 it provides a curated leaderboard of top Polymarket traders, combining top-20 all-time and top-5 last-24h profit, refreshed every 4h. This specific verb-resource combination and the mention of 'copy-trading watchlist' distinguishes it from sibling Polymarket 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?

The description explicitly recommends using it to construct a copy-trading watchlist, providing a clear use case. However, it does not explicitly state when not to use it or mention alternatives among siblings, which would improve 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?

With no annotations, the description carries the full burden. It discloses that the tool synthesizes answers with sources, but does not mention read-only nature, rate limits, or authentication needs, which are relevant for behavioral clarity.

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, front-loading the action and output. Every word is necessary and no redundant information is present.

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 low complexity, no output schema, and minimal parameters, the description provides sufficient context: it states the search scope (live web), language options, and output type (synthesized answer with sources). A small improvement would specify the return format (e.g., text with citations), but current version is 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 coverage is 100% with good descriptions for both parameters. The description adds that search is in Turkish or English, aligning with the enum, but does not provide additional parameter-level details beyond what the schema offers.

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 verb 'search', the resource 'live web', and the output 'synthesized answer with sources'. It also specifies language support (Turkish or English), distinguishing it from sibling tools like alya_ask or 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 implies usage for web searching but does not explicitly state when to use this tool versus alternatives like alya_ask, nor does it provide exclusions or prerequisites.

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 covers return fields and quota limits, but does not disclose whether the tool modifies data, requires authentication, or any side effects. It adequately describes the scoring behavior but lacks mutability info.

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 plus quota: no wasted words, front-loaded with purpose and output, 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?

Covers purpose, return fields, quotas, but omits prerequisites (e.g., channel setup, locked keywords configuration), error handling, and synchronicity. Adequate but not exhaustive for a no-schema no-annotation 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%, so the description adds no new info beyond the schema descriptions for 'count' and 'locale'. Baseline score 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 finds video topic opportunities, specifies the returned fields, and distinguishes it from siblings like 'youtube_upload_video' and 'youtube_get_performance' by focusing on topic discovery and 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?

The description implies it is for channel strategy with 'locked niche keywords and brand voice', but does not explicitly state when to use this tool over alternatives like 'youtube_get_recommendations' or provide conditions for not using 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 are provided, so the description bears full burden. It discloses the draft generation, sandbox preview, and upload eligibility, but lacks details on side effects, rate limits, or persistence of drafts.

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, front-loading the tool's purpose and key distinctions.

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 the list of generated components, sandbox preview, and tier eligibility, but lacks details on output format or error handling.

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 67% description coverage, but the description adds no extra meaning beyond the schema. The undocumented titleHint parameter remains unexplained.

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 it generates a full draft video including title variants, description, tags, script, thumbnail prompt, and estimated duration for a given topic, clearly distinguishing it from the upload 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?

Provides clear context on when to use (for draft generation) and when to use the upload tool (Pro/Studio drafts eligible), but does not explicitly exclude other use cases.

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 disclose behavior fully. Only states it returns three fields. No mention of authentication, rate limits, side effects, or data freshness. For a read operation, more detail on scope and limitations is expected.

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 establishes purpose and time range, second lists return fields. No redundant or extraneous 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 simple tool with one parameter and no output schema, the description covers key aspects but omits details like structure of video summaries, tenant scope, and pagination. Adequate but not fully informative.

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 one parameter 'days' with no description. Description adds 'last N days (default 30, max 90)', providing meaning and constraints (max 90) beyond the schema's default.

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 verb 'Get' and the resource 'tenant-scoped performance summary'. Specifies time range, default, and max, plus lists return fields. Differentiates from sibling tools like youtube_find_opportunities and youtube_get_pipeline_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?

Implies usage for obtaining performance summaries at tenant level. No explicit when-not or alternatives, but the description is sufficient for basic understanding.

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 should disclose behavioral traits. It lists what data is returned (mode, week, velocity, counters, etc.) but does not mention whether the operation is fast, requires auth, or is cached. It adequately describes the output but lacks depth on how the data is obtained or its freshness.

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?

A single sentence of 18 words that front-loads 'Get the current state' and succinctly lists the returned fields. Every word adds value; 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 no output schema, the description enumerates the key fields of the pipeline status (mode, week, velocity, counters, abort criteria, API quota). This is sufficiently complete for a simple status retrieval, though it omits details like data types or potential error conditions.

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 schema coverage is effectively 100%. According to the rubric, 0 params gives a baseline of 4. The description correctly implies no inputs are needed.

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 explicitly names the resource 'current state of Alya's adaptive YouTube engine', listing concrete fields like mode, week, and counters. This clearly distinguishes it from sibling tools like youtube_health_check 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 is provided on when to use this tool versus alternatives such as youtube_health_check or youtube_get_performance. The description lacks explicit context or exclusion criteria.

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, and description does not disclose behavioral traits (e.g., read-only, auth requirements, side effects) beyond listing return types.

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, front-loaded with core action, 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 zero-parameter tool, description adequately covers return types, but assumes understanding of 'Alya's adaptive learning loop' and lacks context on prerequisites.

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 description need not add parameter meaning; schema coverage is 100% (vacuously). Baseline score of 4 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?

Description clearly states the tool 'get prioritized, channel-specific recommendations' with specific return types, distinguishing it from siblings like 'youtube_find_opportunities'.

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 explicit guidance on when to use this tool versus alternatives; sibling tools exist (e.g., youtube_find_opportunities) but no differentiation 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?

Without annotations, the description states it's free (no cost) and no-quota (no rate limit), which are key behavioral traits for an agent. Also lists return data, implying read-only 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?

Two concise sentences with no redundancy. Each sentence provides essential value: first states purpose and cost/quota, second details outputs and usage timing.

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?

Adequately covers all necessary information for a simple health check tool: what it returns and when to use it. No output schema needed given 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 in schema, so description doesn't need parameter info. Baseline 4 applies as schema coverage is 100% (empty).

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 identifies as a health probe that returns specific fields like tier, usage, caps, and status. Distinguishes from sibling youtube tools which focus on content creation or analytics.

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 'Use this from your agent on every cold start', providing clear when-to-use guidance. Also highlights it's free and quota-free, encouraging frequent 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?

With no annotations, the description carries the burden. It reveals free tier returns sandbox URL, Pro queues to own account requiring OAuth. However, it lacks details on error handling, idempotency, or whether the upload is immediate or async.

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 fluff, front-loaded with the primary action. 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 tool has 2 parameters and no output schema, the description lacks parameter explanations and output details. It does not cover behavioral aspects like side effects (creating YouTube video) or prerequisites beyond OAuth.

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% and the description does not mention the parameters 'publish' or 'tenantVideoId'. No meaning is added beyond the schema fields. The agent has no explanation of what these parameters do.

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 'Upload a generated draft to YouTube' with a clear verb and resource. It distinguishes between free tier (sandbox) and Pro/Studio (real upload), differentiating it from sibling tools like youtube_generate_video.

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: requires a generated draft, and explains tier differences (free vs Pro/Studio) with OAuth requirement. Does not explicitly state alternatives or when not to use, 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.