Alya — The Hub for Autonomous Agents

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

No annotations are present, so the description must fully disclose behavior. It only says 'list, look up, discover' without mentioning authentication, side effects, read-only nature, or output format.

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. The first defines function, the second gives a usage hint. Every word 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?

Given no annotations, output schema, or nested objects, the description is incomplete. It fails to explain the difference between list/lookup, the meaning of slug, or what data the tool returns.

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 'slug' has a minimal description). The tool description adds no extra meaning to the parameters, leaving the agent to infer details about 'action' and 'slug' from the schema alone.

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, looks up, and discovers agents, with a specific use case of delegation. It distinguishes from siblings by focusing on the agent registry, but doesn't explicitly contrast with similar tools like alya_app_directory.

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 a usage context ('delegate work to specialised agents') but lacks guidance on when not to use it, alternatives, or when to pick between 'list' and 'lookup' actions.

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 disclose behavioral traits such as read-only nature, side effects, authentication needs, or rate limits. For a read operation, it should at least imply 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?

The description is a single sentence that is front-loaded with the purpose. Every word contributes to understanding the tool's function, with no 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 parameters and no output schema, the description provides a reasonable overview by listing returned fields (equity, cash, open positions, last lessons). It is mostly complete, though it could mention that the data is current and non-modifying.

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, and the schema coverage is 100%. The description does not need to add parameter semantics, and the baseline for 0 parameters is 4.

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

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

The description clearly states the tool gets current Alpaca paper-trading status and lists specific components (equity, cash, open positions, last lessons), distinguishing it from all sibling tools which do not relate to Alpaca trading.

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 does not provide explicit guidance on when to use this tool versus alternatives. While no sibling tools directly compete, it lacks statements about context 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 are provided, so the description must fully disclose behavioral traits. It describes the tool's functionality and return data but does not state whether it is read-only, any rate limits, or permission requirements. For a search tool, the risk is low, but the lack of any behavioral context is a gap.

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, concise and front-loaded with the main purpose. Every word serves a purpose, with no 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 parameters, no output schema, no annotations), the description covers the core use case, return fields, and usage context. It does not mention pagination or sorting, but for a search discovery tool, it is sufficiently complete to guide 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?

Schema coverage is 100% (both parameters have descriptions). The description adds meaningful value by listing example categories for the 'category' parameter (hr, marketing, finance, ops, ai, etc.), which goes beyond the schema's generic description. The limit parameter is already well-described 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 clearly states the tool's function: searching a curated catalog of business tools (FindMyAppz). It specifies the return fields (slug, name, category, etc.) and the use case (discovering purpose-built tools). It also distinguishes itself from generic web search, making it unique among siblings.

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 it ('alternative to generic web search for specific business problems') but does not explicitly state when not to use it or name alternative sibling tools. There is no guidance on exclusions or prerequisites, so the context is clear but incomplete.

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 hints at delegation and multi-step reasoning but lacks specifics on limitations, side effects, or autonomy; partially informative but incomplete.

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 concise sentences, front-loaded with purpose and usage, with no superfluous 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 complexity of a general agent tool, the description covers delegation and reasoning but omits details about output format or potential delays; mostly 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 0% and the description does not explain the 'question' or 'lang' parameters, adding no meaning beyond the schema itself.

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 is a general-purpose agent for questions, opinions, reasoning, and delegation, distinguishing it from sibling tools that are task-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 explicitly says 'Use for general questions... or to delegate' which implies using sibling tools for specific tasks, but does not explicitly list when not to use it or name alternatives.

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

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

No annotations provided, so description carries full burden. It discloses the data source (CelebTrack's continuously-updated dataset) and pricing, but does not explicitly state that the tool is read-only or mention rate limits or authentication 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?

Three sentences, front-loaded with purpose and key details. Efficient but could be slightly shorter.

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

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

Given the tool's simplicity (one parameter, no output schema), the description covers purpose, typical use, pricing, and transparency adequately. It does not mention caching or real-time updates but is largely complete for an agent to decide on invocation.

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

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

Schema coverage is 100% with a concise description for 'celebrityId'. The tool description adds guidance on how to find the ID (use list endpoint or chat ask Alya), which adds 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?

Description clearly states it produces an AI-generated intelligence summary on a celebrity, listing specific outputs (activity timeline, sentiment, controversies, brand-deal signals, TL;DR). It distinguishes from siblings by mentioning the source (CelebTrack) and target audience (PR agents, brand safety, casting).

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 identifies specific use cases and states it's premium with pricing. However, it does not explicitly say when to avoid this tool or compare to alternatives like 'alya_ask' or 'web_search'.

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 premium pricing ($0.05/message), that clones are proprietary tuned personas (not generic LLM prompts), and that replies are in-character. However, no side effects or error handling mentioned; with no annotations, this is good but not exhaustive.

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: action+result, clone description, use cases+pricing. Every sentence earns its place, front-loaded with core purpose, no wordiness.

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?

Adequate for a simple tool with 2 parameters and no output schema: covers purpose, return value, use cases, and pricing. Lacks error handling or output format details, but still complete enough for 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 coverage is 100%, so baseline is 3. The description adds value by linking 'cloneId' to the sibling tool 'alya_iconic_clones' for discovery, providing extra context 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 specifies the verb 'send a message' and resource 'iconic character clone on Sosie', and distinguishes from the sibling tool 'alya_iconic_clones' (discovery) by focusing on the chat 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?

Explicitly lists use cases (entertainment agents, creative writing, game NPCs, marketing copy) and implies when to use this over siblings like 'alya_ask' or 'alya_celeb_summary', though lacks explicit non-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?

With no annotations, description carries full burden. It explains the data source and that it's aggregated from paying users, but lacks details on authentication, rate limits, or data freshness. Basic read behavior is implied.

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 concise, front-loading purpose and key data fields. However, it is a single long sentence; could be broken into multiple sentences for 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, description adequately explains return value structure (title, company, supporters, revenue). Covers data source, use cases, and sample categories. Complete 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?

Schema coverage is 100%, so baseline is 3. Description adds no additional meaning beyond schema for 'limit' and 'category' parameters; it does not elaborate on syntax or behavior.

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 retrieves top consumer demands aggregated globally, with specific fields (title, company, supporters, revenue). It distinguishes from sibling tools by focusing on consumer demand data, which is unique among the listed siblings.

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 explicitly suggests use cases: surface unmet needs, pre-product validation, consumer insights. It does not explicitly state when not to use or provide alternatives, 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 description discloses important behavioral traits: premium cost ($0.05/call), use of clinical-grade FDA data, warning against hallucinated answers, and links to parent platform. It does not describe return format in detail but mentions key elements (severity, description, recommendation).

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 informative but includes somewhat verbose elements like the 'WebMD with a brain' tagline and a dramatic warning about hallucinations. Core functionality is front-loaded, but there is room for tightening.

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, but description adequately conveys what is returned (severity, description, recommendation). It covers input requirements, cost, data source, and usage context. For a simple one-parameter tool, it is sufficiently complete.

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

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

Schema coverage is 100% with one parameter 'drugs' described as '2-10 drug names (generic or brand)'. Description adds meaning by specifying that the tool checks pairwise interactions and returns severity, description, recommendation per pair, 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?

Description clearly states it checks pairwise drug-drug interactions for 2-10 medications and specifies returned information (severity, clinical description, recommendation). Distinguishes from sibling tools by positioning as a side feature of Symptia, and its focus on medication safety is unique among 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?

Explicitly states use cases (medication safety reviews, polypharmacy checks, pre-prescription screening) and provides a clear disclaimer that it is not a substitute for licensed medical advice. While no direct alternative sibling is mentioned, the tool's niche is well-defined.

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 AI call to Grok, cost of $0.10/call, and use of Father's curated database. No annotations exist, so description carries full burden; it adequately informs about premium nature and data sources.

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?

Front-loaded with action and returns, then appends use cases and pricing. Slightly verbose with embedded URL and parentheses, 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?

Covers input, output (detailed list), cost, use cases. No output schema, so description fully compensates. Adequate for an agent to invoke 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 description coverage is 100%, so baseline is 3. Description adds context (GemHunt, UUID or numeric) but no additional meaning 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?

Clearly states verb ('trigger an appraisal') and resource ('single gem on GemHunt'), lists specific outputs (value, confidence, etc.), and distinguishes from sibling tools like 'alya_gems_recent' which lists gems.

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, due diligence) but does not 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 exist, so the description carries the burden. It discloses the scraping engine and output details but does not mention rate limits, idempotency, or any side effects. The tool appears read-only, but no explicit readOnlyHint is provided.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is a single paragraph with clear front-loading of purpose. It is relatively concise and informative, though could be slightly more 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?

Without an output schema, the description adequately explains the return fields (gemScore, category, photos, etc.). Parameter count is low and well-covered. The description is complete for a tool of this 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 both parameters documented. The description adds minimal value by explaining the default minScore of 60 and its relation to 'strong_gem' status, but does not significantly expand 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?

Description clearly states the tool discovers undervalued antiques/collectibles/rare items priced ≤30% of estimated market value. It specifies the data source and output fields. However, it does not explicitly differentiate from sibling tools like alya_gem_appraise.

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 for arbitrage opportunities or rare finds and mentions filtering by minScore. It does not provide when-not-to-use guidance or clearly reference 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?

Describes the tool as browsing a catalog, implying read-only behavior. No annotations exist, so description carries full burden. It sufficiently discloses the non-destructive nature, though could explicitly state it is read-only.

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 that front-load purpose and details. 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?

Lists all fields of each entry (name, era, nationality, bio, etc.) despite no output schema, compensating fully. Use cases are given, making it complete for a browse 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 descriptions for both parameters. Description adds context for category with examples and clarifies limit bounds, adding value 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?

The description clearly states the tool browses a catalog of iconic figures available as AI clones, listing the fields (name, era, nationality, etc.) and use cases. It distinguishes from siblings like alya_clone_chat (for chatting) and alya_celeb_summary.

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 use cases (research, debate prep, education, chat). Does not mention when not to use or alternatives, but the purpose is clear and the sibling list implies 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 carries full burden and does well by detailing the inputs (action description, optional domain), outputs (patterns, stats, total $, verdict), and cost ($0.10/check). It also highlights the network-effect nature. It does not explicitly state read-only behavior, but the 'pre-flight' label implies no 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 efficient, front-loading purpose and providing necessary details in a few sentences. While it includes marketing language like '500x ROI', it does not waste words and the structure is logical.

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 explains the return values (patterns, stats, verdict) sufficiently. It also mentions cost and calibration data. It could elaborate on interpreting the verdict, but overall 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 description coverage is 100%, with both parameters documented adequately. The tool description reinforces the schema but does not add new semantic meaning beyond 'plain-language description' and 'domain hint'. Baseline score of 3 is appropriate.

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

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

The description clearly states the tool is a 'pre-flight risk check' that searches a loss memory and returns a verdict. It distinguishes itself from sibling tools by focusing on documented losses and providing a specific output format (BLOCK/CAUTION/ALLOW/UNKNOWN).

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 to use the tool 'BEFORE placing any non-trivial bet, trade, gig pitch, or financial decision.' While it doesn't mention alternatives or when not to use it, the context is clear and helpful for the agent to decide when to invoke 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?

With no annotations, the description carries full burden. It discloses model version, feature count, grid cell size, premium pricing, and validation metrics. No mention of rate limits or auth needs, but sufficient for a read-only prediction 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?

Two sentences, front-loaded with core functionality, followed by use cases and pricing. Every sentence adds 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?

Covers model, output probabilities, grid cell, use cases, and pricing. Lacks return format details, but output schema is absent; description sufficiently explains output for the tool's purpose.

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 description adds no additional meaning to the parameters beyond their schema definitions, meeting the baseline.

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

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

The description clearly states it provides 72-hour earthquake probability forecasts using a specific ML model, and lists the magnitude thresholds. It differentiates from sibling 'alya_seismic_recent' (which likely deals with recent quakes) by specifying it's a forecast 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?

Explicit mention of use cases (risk assessment, insurance pricing, surfacing high-risk regions) and warning that it's premium not a public scrape. However, no explicit when-not-to-use or comparison 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?

With no annotations, the description carries full burden. It discloses output fields but omits info on read-only nature, rate limits, or any side effects. The description is adequate but not rich in behavioral details beyond the obvious.

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 core functionality and data sources, followed by use cases. No redundant information, every sentence earns its place.

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

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

For a simple tool with 3 optional parameters and no output schema, the description covers the essential return fields and use cases. It is slightly lacking in explaining scope (e.g., global, significant magnitudes) but is largely complete.

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

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

Schema description coverage is 100% (each parameter has a description with defaults and ranges). The tool description adds no extra semantics beyond what is already in the schema, so baseline 3 applies.

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

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

The description clearly states it returns recent earthquakes from a specific aggregator, lists fields (magnitude, location, depth, time, source), and mentions use cases. However, it does not explicitly distinguish from sibling 'alya_seismic_forecast', though the name implies differentiation.

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, verifying a felt event), providing good context. No exclusions or alternatives are mentioned, but the sibling context implies forecast tool for predictions.

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 Bayesian inference method, the cost ($0.10/call), and the presence of red-flag warnings. It lacks details on error handling or rate limits but covers core behavioral traits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is informative but somewhat lengthy, including a URL and pricing detail that could be separate. It is front-loaded with the action but loses conciseness with extra commentary.

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 medical complexity and lack of output schema or annotations, the description covers key aspects: what it returns, use cases, disclaimer, and cost. Missing details on response format or error states, but adequate for a single-param tool.

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

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

The schema already describes the single parameter 'complaint' with 100% coverage. The description adds value by providing a concrete example and noting that any language is accepted, going 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 starts a Bayesian symptom-to-diagnosis session via Symptia, returning candidate conditions, probabilities, and red flags. The verb 'Start' and the resource 'symptom-to-diagnosis session' are specific, and the tool is well-distinguished from siblings like alya_drug_interactions.

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 use for triage assistants, telehealth pre-screening, and healthcare chatbots, and warns it is not a substitute for clinical diagnosis. This provides clear context, though it could more explicitly contrast with other medical tools like alya_drug_interactions.

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 mentions the data source (OpenMeteo + Grok-fused via Velene's miroir engine) but omits details like error handling, response format, rate limits, or what happens for invalid city names. The description is adequate 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?

Two sentences: first defines purpose and output data, second gives usage context and data source. Extremely concise with no redundant words. 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's simplicity (one parameter, no output schema, no annotations), the description covers the essential aspects: what it does, what it returns, and when to use it. Minor omissions (e.g., output format details) are acceptable for this context.

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

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

Schema coverage is 100%, with a clear description for the 'city' parameter. The description adds global scope ('any city worldwide') and lists output fields, but does not provide extra semantics like case sensitivity or format expectations. 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 provides current weather data (temperature, humidity, UV index, condition, icon) for any city worldwide, with no competing sibling tool. The verb 'get' is implied and the resource (weather) is well-specified.

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 (travel planning, agricultural decisions, event scheduling, context for other tools), guiding appropriate usage. However, it does not mention when not to use it or any alternatives, as none exist among siblings.

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

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

With no annotations, the description covers pricing, batch size limit, required/optional fields, and result ordering. It lacks details on error handling, idempotency, or latency, leaving gaps 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?

Two concise sentences deliver all key information: batch limit, pricing, field requirements, and result order. No extraneous 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 no output schema or annotations, the description provides input format, constraints, and pricing. It misses error scenarios and validation rules, but is largely sufficient for basic usage.

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 description identifies required vs. optional fields (prediction, confidence, domain, stakes) but adds no additional meaning beyond what the schema already provides. Schema coverage is 0%, and the description does not compensate with explanations of field 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 verb 'calibrate' and the resource 'predictions' in a batch context, limiting to 25 items. It distinguishes from sibling 'calibrate_decision' by emphasizing batch processing.

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 multiple predictions with batch pricing and field requirements. It does not explicitly state when not to use or name alternatives, but sibling 'calibrate_decision' serves as the single-prediction counterpart, providing implicit 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 bears full responsibility. It discloses that the tool persists predictions, returns a call_id for later feedback, and uses a resolved-outcomes ledger. It does not mention destructive actions or rate limits, but the described behavior is consistent with a safe calibration tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is three sentences long, front-loaded with the primary purpose, and every sentence adds value. There is no redundancy or fluff, making it highly efficient.

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

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

Given the absence of an output schema, the description thoroughly explains the return values (calibrated_confidence, similar cases, etc.), the persistence and feedback mechanism, and the backing ledger. For a tool with 4 parameters (1 required), this is 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?

All parameters have descriptions in the schema (100% coverage), so the baseline is 3. The description adds context that stakes are used for kelly_stake and domain for filtering similar cases, but it does not significantly elaborate beyond the schema. This is adequate but not exceptional.

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 details the return values and the backing ledger, and it implicitly distinguishes itself from sibling 'batch_calibrate' by focusing on a single prediction 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?

The description includes a usage instruction for closing the loop with feedback API, but it does not explicitly guide when to use this tool vs. siblings like 'batch_calibrate' or 'get_domain_accuracy'. The context is clear but lacks explicit 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 carries full burden. It discloses that the operation returns specific metrics and appears to be a read-only lookup. However, it does not mention authentication needs, rate limits, or any side effects. The lack of behavioral detail beyond the return fields makes it adequate but minimal.

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 states the purpose, the second lists the return fields. No extraneous words, highly efficient and 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 the tool's simplicity (one parameter, no output schema, no nested objects), the description adequately explains its output and purpose. However, it could optionally mention data freshness or scope limitations, which would make it fully complete for a production 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 one parameter 'domain' described with an example in the schema. The description adds context by calling it a 'Domain prefix' and tying it to 'Alya's outcome ledger', slightly enhancing the schema's meaning. Since schema already documents the parameter well, this is a baseline 3.

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

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

The description clearly states the action 'Look up' and the resource 'historical win-rate for a given prediction domain in Alya's outcome ledger', specifying the exact returned fields. This distinguishes it from sibling tools like alya_seismic_forecast or alya_demands_trending, which likely deal with other prediction types.

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 that the tool is for looking up historical accuracy, but it does not explicitly state when to use it over alternatives or when not to use it. No exclusions or alternative suggestions are 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 provided, so description bears full burden. Does not disclose important traits like whether width/height are adjustable, prompt constraints, image format, or URL expiry. The hardcoded '1024x1024' conflicts with the schema's adjustable width/height, causing ambiguity.

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 with no unnecessary words. Information is front-loaded and to the point.

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 generative tool with 3 parameters, no output schema, and no annotations, much context is missing: image format, generation time, URL expiration, usage limits. The description is too sparse.

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 the prompt parameter is minimally described in the schema. The description mentions prompt but not width/height, and the fixed size contradicts the adjustable parameters. No additional meaning added 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?

Description clearly states the action (generate), resource (image), model (FLUX.1-schnell), size (1024x1024), and output (URL). It distinguishes from sibling tools which are mostly non-image generation.

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 the tool. No mention of alternatives, prerequisites, or limitations.

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 output includes a blocking decision based on loss-forensics, but lacks details on how categorization works, output format, or potential 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 succinct and front-loaded sentences that convey the main action, categories, and purpose without 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, the description partially covers return value (block decision and categories) but omits exact return format, error handling, and limitations. 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?

Schema coverage is 100% with a brief description of the titles parameter. The tool description adds meaningful context by listing the specific categories, which helps the agent understand valid inputs beyond the schema.

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

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

The description clearly states the tool classifies Polymarket market titles into specific quality categories and returns a blocking decision. It distinguishes the action but does not differentiate 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 advises using the tool to pre-screen before placing real money, providing a clear use case. However, it does not specify when not to use it 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?

With no annotations, the description carries full burden. It discloses the cost ($0.50/call) and data source (6+ months arb history), but does not mention whether it is read-only, rate limits, or what happens with invalid parameters. Some transparency, but not comprehensive.

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 cost and purpose, then adding background. It is efficient but missing parameter information, which could be added without increasing length significantly.

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 optional parameter and no output schema, the description covers the core purpose and data source. However, it lacks parameter explanation and any hint of return format, making it slightly incomplete.

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 'limit' parameter at all. It adds no meaning beyond the schema's type and default. The description should explain how the limit affects results (e.g., number of top markets returned).

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 'Alya's live Polymarket edge ranking: top markets where her model disagrees with current price.' It uses specific verb+resource ('edge ranking') and distinguishes from siblings like polymarket_categorize 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?

No guidance on when to use this tool vs alternatives. For example, it doesn't contrast with polymarket_signals or explain that it's for identifying mispriced markets. The description simply states what it does without 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, the description carries full burden. It discloses the signal source, filtering (loss-forensics, category blocking), and that it is a read-only query returning BUY signals. It mentions premium pricing but not implementation details. Overall transparent for a signal tool, though cost details are vague.

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-loaded with the core purpose, followed by filtering criteria and row description. Every sentence adds value, no redundancy. Efficient and well-structured.

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

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

Despite no output schema, the description enumerates the columns in each row (trader, market title, side, price, size, category, suggested edge), making the return format clear. It also explains the premium nuance. For a tool with 3 optional parameters, this is 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 clear descriptions for each parameter (hours, limit, includeBlocked). The tool description adds context about blocked categories and the nature of signals but does not significantly extend beyond the schema. Baseline 3 is appropriate given high schema coverage.

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

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

The description clearly states it returns 'Live Polymarket copy-trading signals' from a specific watchlist filtered by category and time, with explicit detail on excluded categories and row structure. It distinguishes itself from siblings like 'polymarket_top_traders' and 'polymarket_edge' by focusing on signals rather than traders or raw 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 implies use for automated trading ('bot-grade signals') and notes it is premium, but does not explicitly state when to use this tool versus alternatives like 'polymarket_edge' or 'polymarket_top_traders'. The context of exclusion of certain categories provides some guidance, but lack of explicit comparison prevents a higher score.

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; description discloses refresh every 4h and union construction method. Does not discuss limitations or side effects, but sufficient for a read-only 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?

Two concise sentences, front-loaded with key info, no 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?

Describes row contents (wallet, window, rank, lifetime USD profit), refresh cycle, and use case. Adequate for a simple data retrieval tool with no output schema.

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 has description). Description mentions window implicitly ('1d/all') but adds little beyond enum values. Limit is covered in 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 provides a curated Polymarket Tier-A trader leaderboard, union of top-20 all-time and top-5 last-24h. Distinct from sibling tools like polymarket_categorize, polymarket_edge, etc.

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 construct your own copy-trading watchlist,' giving a clear use case but not mentioning when to avoid or compare with alternatives.

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool returns a 'synthesized answer with sources,' which is useful. However, it does not detail any behavioral traits such as rate limits, required permissions, or handling of missing results. The behavior is generally clear but lacks depth.

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 efficiently conveys the tool's purpose and key features (languages, output type). No unnecessary words or redundancy. It is front-loaded with the main 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?

Given the low complexity (2 parameters, no output schema), the description is mostly complete. It explains the return value (synthesized answer with sources) and the language constraint. It could mention supported domains or result format, but it suffices for a basic search 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 description coverage is 100%, so the schema already documents both parameters. The description adds the context that the language parameter restricts to Turkish or English, which is a minor clarification. It does not add significant new meaning 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: 'Search the live web in Turkish or English and return a synthesized answer with sources.' The verb 'search' and resource 'live web' are specific, and the language constraint distinguishes it from other tools. No sibling tool appears to duplicate this function.

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 does not provide guidance on when to use this tool versus alternatives. It lacks explicit context about when not to use it or mention of alternative tools for different scenarios (e.g., specialized searches).

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 discloses scoring against niche keywords and brand voice, and lists returned fields. It includes quota limits but does not explain how keywords are set or any prerequisites.

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 adding value: purpose, scoring method, return fields, and quota. Front-loaded with actionable info, 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?

Describes return fields and scoring criteria adequately for a tool with 2 params and no output schema. However, lacks context about prerequisite 'locked niche keywords,' which may affect 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 100% with both parameters described. The description adds context (e.g., 'N' in 'Find the next N best') but does not significantly expand beyond schema defaults.

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 'Find the next N best video topic opportunities for your channel' with specific verb and resource. It distinguishes itself from siblings like youtube_generate_video by focusing on opportunity discovery.

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 use when seeking video topics but does not explicitly state when to use versus alternatives or provide exclusions. The quota mention provides some context but no direct 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 the free tier behavior and upload eligibility, but does not detail output format, storage duration, or other lifecycle aspects. The description adds value beyond annotations but remains incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

Two sentences with no waste. The first sentence states the core functionality with specific deliverables; the second clarifies tier differences. Information is 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?

Given moderate complexity (3 params, no output schema, no annotations), the description covers the main purpose, tier distinctions, and output components. However, it omits details about the return format and does not explain the titleHint parameter, leaving minor gaps.

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

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

The description lists output components (title variants, script, etc.) which indirectly explains the tool's scope, but does not add detail for undocumented parameters like titleHint. Schema coverage is 67%, so description partially compensates but leaves gaps.

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 a full video draft including title variants, description, tags, script, thumbnail prompt, and estimated duration for a given topic. It differentiates from the sibling tool youtube_upload_video by specifying 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?

Explicitly states the tool's purpose and notes that free tier does NOT upload while Pro/Studio drafts are eligible for youtube_upload_video, providing clear context for when to use this tool versus the upload sibling. However, it does not explicitly mention when not to use or name 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 carries full burden. It states the return fields but does not disclose whether it is read-only, destructive, or any side effects. It adequately describes what it does but lacks 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?

One sentence that efficiently conveys the purpose, scope, and key return fields. No wasted words; front-loaded with the core function.

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 single parameter, no output schema, and no annotations, the description is fairly complete. It explains the time range and main return fields. However, it could clarify the output format, e.g., whether it's a JSON object or list.

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 (days) with 0% schema description coverage. The description adds meaning by specifying default (30) and max (90), compensating well for the lack of schema details.

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 a tenant-scoped performance summary for the last N days, listing specific fields (videosPublished, videosSandbox, top 10 video summaries). This distinguishes it from siblings like youtube_get_recommendations and 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?

The description specifies the default (30) and max (90) days, implying usage for recent performance summaries, but provides no explicit guidance on when to use this tool versus alternatives or any exclusions.

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

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

No annotations are present, so the description carries full burden. It lists the output fields (mode, week, velocity, counters, abort criteria, quota) which is helpful, but does not explicitly state that the tool is read-only or non-destructive. However, as a 'get' operation, this is implied.

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 concise sentence front-loads the purpose and lists all key return components. No unnecessary words 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?

For a simple zero-parameter tool with no output schema, the description is complete. It covers what the tool does and what it returns, which is sufficient for the agent to use 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?

Tool has zero parameters, so baseline is 4. Schema coverage is trivially 100%. The description adds value by detailing the output contents, which indirectly aids understanding but does not need to describe 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 retrieves the current state of Alya's adaptive YouTube engine and enumerates specific components like mode, week, velocity, counters, abort criteria, and quota. This verb+resource structure is specific and distinguishes it from siblings 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?

The description implies the tool is for checking pipeline state, but does not explicitly state when to use it versus alternatives such as youtube_health_check or youtube_get_recommendations. No exclusion criteria or context for selection 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 the full burden. It describes the return format ('Returns ...') but does not explicitly state that the tool is read-only, nor does it mention rate limits, authentication needs, or side effects. The behavioral disclosure is adequate but could be more explicit.

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 (one sentence plus listing return types) and front-loaded with the key action. Every sentence is useful. It could be slightly more structured but is 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 zero-parameter tool with no output schema, the description covers the purpose and return values sufficiently. It mentions 'Alya's adaptive learning loop' which adds context but may require external knowledge. Overall, it is complete enough 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?

With zero parameters and 100% schema coverage, the description adds value by explaining what the tool returns and the adaptive learning loop context. However, it mentions 'channel-specific' without clarifying how the channel is determined, which could confuse if no parameter exists.

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 channel-specific recommendations from an adaptive learning loop and lists return types (onboarding gaps, missed velocity, etc.). It uses a specific verb ('Get') and resource ('recommendations'). However, it does not explicitly contrast with sibling tools 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 guidance on when to use this tool versus alternatives. The description implies usage for recommendations but does not specify prerequisites, context, 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?

Describes the tool as free and no-quota, which adds useful behavioral info. However, with no annotations, the description could explicitly state it is read-only or non-destructive, which is only implied by 'health probe.'

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 packs return fields, second gives usage guidance. No fluff, every word 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?

Given low complexity (no params, no output schema), the description adequately covers purpose and usage. Listing return fields compensates for absence of output schema, though return format could be elaborated.

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, schema coverage is 100%, so the description need not add parameter details. The baseline of 4 applies as the description adds no confusion.

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 for YouTube, listing specific returned data (tier, usage, caps, etc.). It distinguishes itself from sibling YouTube tools that focus on finding opportunities, generating videos, etc.

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 advises use on every cold start, providing clear context for when to invoke. Does not mention when not to use or alternatives, but the guidance is direct and 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?

Discloses tier-specific behavior (sandbox vs real upload) and OAuth requirement. However, does not explain asynchronous nature (queues), error handling, or side effects of the publish parameter. Without annotations, more detail is needed.

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-loaded with main action. No superfluous information, well-structured 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?

Covers core action and tier differences but lacks parameter descriptions, output format, and asynchronous behavior. Adequate for a simple tool but incomplete given no output schema or annotations.

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%, yet description adds no explanation of parameters. 'tenantVideoId' and 'publish' are not described; agent must infer meaning from context. Very little value beyond parameter 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?

Description clearly states 'Upload a generated draft to YouTube' with specific verb and resource. It distinguishes from sibling tools like youtube_generate_video by specifying the upload action and tier differences.

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 guidance on when to use based on tier (free vs Pro/Studio) and mentions authentication requirement. Implicitly indicates it's for uploading drafts after generation, but lacks explicit alternatives or when-not-to-use conditions.

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