x402-alpha

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

With no annotations provided, the description must fully disclose behavioral traits. It only reveals that payment is consumed on execution, including timeouts. However, it does not mention whether the tool is read-only, idempotent, or any side effects. The response format is vaguely described as 'unified AI synthesis.' More detail on what gets modified or what the return structure is would improve 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 very concise: two sentences. The first sentence front-loads the main purpose, and the second adds critical cost information. Every word earns its place; 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?

The tool has no output schema, so the description should clarify what the output looks like. It says 'unified AI synthesis' but does not specify format (text, JSON, etc.). It does cover cost and timeout behavior, which is important. Given the complexity of a paid tool, more detail on the return value would make it 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%, so the schema already documents each parameter (chain, symbol, address). The description adds no extra semantic meaning beyond what is in the schema, such as that address enables on-chain analysis, which is already stated. The bundling aspect is implicit but not tied to specific parameters.

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

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

The description clearly states the tool's purpose: it bundles market data, sentiment, on-chain transfers, and risk research into a unified AI synthesis. It also mentions the cost and payment consumption, making the scope unambiguous. This distinguishes it from sibling tools like alpha_news or alpha_onchain, which focus on individual aspects.

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 obtaining a comprehensive token brief in one call, but it does not explicitly state when to use it over alternatives (e.g., alpha_sentiment for sentiment only). There is no guidance on when not to use it or what prerequisites are needed. The mention of cost and timeout consumption provides some context but not comparative 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?

Discloses cost ($0.03 USDC) and billing behavior (consumed on execution including timeouts). Lacking annotations, this is helpful but does not cover other behavioral aspects like side effects, rate limits, or error handling.

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 efficiently convey purpose and cost. No wasted words; front-loaded with the primary function. Ideal conciseness.

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?

Missing details about return format, pagination, or how to interpret results. With no output schema, description should provide more context about what the agent can expect. Adequate but 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 already describes all 4 parameters with 100% coverage. Description adds no additional nuance beyond listing categories in prose, which overlaps with schema. 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?

Description clearly states it provides 'upcoming crypto events' and lists specific types (token unlocks, protocol upgrades, governance votes, launches). It distinguishes from sibling tools like alpha_news or alpha_token by focusing on events, but lacks an explicit verb like 'list' or 'get'.

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. Does not mention situations where another alpha tool would be more appropriate, nor does it specify prerequisites or contexts.

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. Discloses cost and timeout behavior, which helps with financial expectations. But does not cover data sources, latency, error handling, or output format. Adequate 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?

Two sentences, zero wasted words. First sentence states purpose clearly. Second sentence adds critical cost/timeout info. Perfectly 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 no output schema and 3 parameters, description is incomplete. Does not explain return format, pagination, or sorting. For a comparison tool, more detail on output structure would help. However, cost and timeout are covered. Middle ground.

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 schema already describes parameters. Description adds minimal extra meaning by listing example focus areas (price, volume, sentiment, fundamentals). No additional validation or usage details beyond schema. 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?

Description uses specific verb 'compare' and identifies resources ('tokens') and dimensions ('price action, volume, sentiment, fundamentals'). Clearly distinguishes from sibling tools like alpha_token (single token) and alpha_stats (statistics) by emphasizing side-by-side comparison.

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?

Mentions cost ($0.05 USDC) and payment consumed on execution including timeouts, which is useful. However, no explicit guidance on when to use vs siblings or when not to use. The context of 'comparison' implies usage, but lacks direct alternatives or 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 provided, so the description carries the full burden. It discloses multi-source usage, cost, timeout, and that payment is consumed on execution including timeouts. Could mention output format or error handling but is sufficiently transparent for a single-parameter 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?

Single sentence with no wasted words. Key elements (multi-source, cost, timeout, payment consumption) are front-loaded and clearly communicated.

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 (multi-source, cost, timeout, single param), the description covers the essential behavioral aspects. Missing details on output format or timeout behavior (e.g., what happens on timeout) but adequate 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?

Schema description coverage is 100% with example queries. The tool description does not add additional parameter information beyond the schema, so baseline score of 3 is appropriate.

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

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

Clearly states it is a deep multi-source research tool listing specific sources (Exa, Firecrawl, Claude, tweets), cost, and timeout. Distinguishes from sibling tools like alpha_brief or alpha_search by emphasizing depth and multiple sources.

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: it is for deep research, costs $0.10 USDC, and may take up to 60s. Does not explicitly state when not to use it or name alternatives among siblings, but the depth/time/cost imply it is for thorough investigations.

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 adds value by disclosing the $0.05 USDC cost and that payment is consumed even on timeouts. It does not mention rate limits or data freshness but provides critical behavioral context 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 concise sentences front-load purpose and critical cost information. No wasted words.

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

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

Given the tool's simplicity (1 optional param, no output schema) and many siblings, the description lacks details on return format or what a 'pulse' includes. It is adequate but not 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%, and the tool description adds no extra meaning to the 'theme' parameter beyond what the schema already states. 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?

Description clearly states it's a 'Macro economic pulse' aggregating data from FRED, prediction markets, Twitter, and Grok. This verb+resource combination is specific and distinguishes it from sibling tools like alpha_news or alpha_prediction.

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

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

Description implies usage for macro economic overviews but does not explicitly state when to use this tool versus alternatives like alpha_news or alpha_sentiment. No exclusions or alternative tool names are mentioned.

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 a $0.05 USDC payment is consumed on execution (including timeouts), which is critical behavioral info. It also hints at checks (liquidity, rug flags, social velocity), though it could further detail what exactly happens (e.g., data sources, rate limits).

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

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

The description is two sentences, with the first sentence conveying the core purpose and the second disclosing payment and timeout behavior. Every word is purposeful, and the structure is front-loaded with the most critical 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?

The tool has 3 optional parameters and no output schema. The description covers the functionality and payment, but does not specify the output format or how results are structured (e.g., keys, metrics). For a tool with no output schema, additional detail on return values would improve completeness.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds no new info about parameters beyond what the schema already provides (chain, symbol, address). The phrase 'per-token degen risk read' implies using symbol or address, but this is not explicit.

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 covers 'top meme tokens by volume with momentum' or a 'per-token degen risk read.' This distinguishes it from sibling tools like alpha_token (general token info) and alpha_trending (trending tokens). However, the verb is implied rather than explicit, missing a 'get' or 'return' statement.

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

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

The description outlines two use cases (market-wide screener and per-token risk read) but provides no guidance on when to use this tool versus alternatives like alpha_token or alpha_onchain. No exclusion criteria or context for sibling comparison is given, leaving the agent to infer usage.

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

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It only notes the payment and timeout behavior, omitting details such as side effects, rate limits, idempotency, or what happens on invalid input. The lack of output schema means return format is also not described.

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

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

The description is two sentences long with no fluff. It front-loads the core purpose and then adds a cost note. It could be slightly more structured (e.g., separating purpose from cost), but it is concise.

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

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

Given that the tool has three optional parameters and no output schema, the description falls short. It does not explain the return format, error handling, pagination, or other aspects essential for an agent to use it effectively.

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 three parameters are documented in the input schema with descriptions (100% coverage). The tool description adds no further semantic value beyond listing example narrative slugs, so it meets the baseline for 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 'Detect and track active market narratives' and lists specific examples like AI tokens, RWA, L2, memecoins, DePIN. This verb+resource combination distinguishes it from sibling tools like alpha_memecoin or alpha_trending.

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 mentions a cost of $0.05 USDC and that payment is consumed on execution including timeouts, but it does not provide explicit guidance on when to use this tool versus alternatives like alpha_search or alpha_sentiment.

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 a $0.02 USDC cost consumed on execution including timeouts, which is a critical behavioral trait. However, it does not mention other behaviors like rate limits or output format, but the cost and timeout transparency is valuable.

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 purpose and sources, second explains payment and timeout behavior. No fluff, but the first sentence is dense with source names. Efficient overall.

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

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

The description covers the main functionality and cost but lacks details about the output format or what 'AI synthesis' produces. Given no output schema, this is a gap. Adequate but not complete.

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

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

The input schema has 100% description coverage (query, token, category are described clearly). The description adds no extra meaning beyond what the schema already 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 it provides AI-filtered crypto news from specific sources like CoinTelegraph, Decrypt, CoinDesk, Blockworks, and X/Twitter. This distinguishes it from sibling tools like alpha_trending or alpha_sentiment, which focus on other aspects of crypto analysis.

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

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

The description gives context (sources and cost) but does not explicitly state when to use this tool versus alternatives. No exclusions or when-not-to-use guidance are provided, though the source specificity implies its domain.

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 cost ($0.05 USDC) and that payment is consumed on execution including timeouts, which is useful. However, it does not state whether the operation is read-only, mutates state, or has rate limits.

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

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

The description is very concise with only two sentences, no filler, and front-loads the core purpose. Every sentence adds value.

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

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

Given the complexity (4 parameters, no output schema), the description covers the purpose and some behavioral info (cost) but lacks details about return format, pagination, or whether results are alerts or data points. It feels adequate but could be more complete.

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

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

Schema coverage is 100% so baseline is 3. The description adds no extra meaning beyond the schema parameter descriptions; it does not explain relationships between parameters, provide examples, or clarify usage patterns.

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 it provides 'on-chain activity intelligence' listing specific use cases like whale movements and large transfers. It clearly identifies the resource (on-chain data) but lacks a verb (e.g., 'retrieve') and does not distinguish from sibling tools like alpha_trending or alpha_stats.

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 instructions on when to use this tool versus alternatives. The description does not mention exclusions, prerequisites, or compare with any of the 18 sibling tools, leaving the agent without guidance for tool selection.

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 cost ($0.05 USDC) and that payment is consumed on execution including timeouts, which is helpful. However, it does not mention read-only nature, rate limits, or response format, leaving gaps in behavioral 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 with no wasted words. The first sentence captures the core functionality, and the second sentence adds essential cost behavior. Information is front-loaded effectively.

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 funding rates across two exchanges with squeeze-risk synthesis and no output schema, the description lacks details about return format, pagination, or how results are structured. It covers cost and core purpose but leaves some completeness gaps.

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

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

Schema coverage is 100%, and the description adds value by stating the supported exchanges (Hyperliquid, Gate) and that omitting symbol gives market-wide extremes. This provides practical 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 states the tool provides perpetual futures funding rates across Hyperliquid and Gate, with crowded-trade extremes and squeeze-risk synthesis. It also specifies the cost ($0.05 USDC) and payment consumption. The verb 'funding rates' and resource specification distinguish it from sibling alpha_* 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 implies usage when funding rates or squeeze-risk synthesis is needed, but it does not provide explicit guidance on when to use this tool over alternatives, such as alpha_deep or alpha_risk. No exclusions or comparative context are 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It details the cost policy including consumption on timeouts, which is valuable. However, it does not mention other behavioral aspects such as rate limits, authentication requirements, or data freshness, leaving gaps.

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

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

The description is two concise sentences: the first lists core features, and the second states the cost and execution policy. No extraneous information, and critical details are 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 lack of an output schema, the description adequately lists the main return components (holdings, diversification, risk, rebalancing). However, it omits details on response format, error handling, or processing time, which would be helpful for a paid 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 input schema has 100% coverage, fully documenting both parameters (wallet, chain). The description does not add any additional parameter-level meaning beyond what is already in the schema, resulting in a baseline score.

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 'Wallet portfolio analysis' and lists specific outputs (token holdings, diversification score, risk exposure, AI rebalancing suggestions). It effectively distinguishes the tool from sibling tools like alpha_brief, alpha_compare, and alpha_token, which cover different aspects of crypto analysis.

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?

While the description implies use for wallet portfolio analysis, it does not explicitly state when to use this tool over alternatives or when not to use it. The cost detail ($0.05 USDC) provides a practical constraint, but no direct comparison with sibling tools 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?

Discloses cost ($0.03 USDC) and that payment is consumed even on timeouts, which is important behavioral information. With no annotations, this is good but could include more details like error handling or data 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?

Extremely concise: two sentences, no filler. Front-loaded with purpose, followed by cost. 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?

Given no output schema, the description should hint at return format. It does not. Schema covers inputs fully, but output structure is missing, leaving agents to infer. Could be improved.

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 no extra meaning beyond the schema, so baseline score of 3 applies.

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

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

The description clearly states it provides prediction market intelligence from multiple sources (Polymarket, Kalshi, Twitter, Grok), distinguishing it from sibling tools like alpha_sentiment or alpha_news. The mention of cost adds clarity.

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. It implies use for prediction market queries but does not specify when not to use or suggest sibling tools for other needs.

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 payment cost ($0.062 USDC) and that payment is consumed even on timeouts, which is important behavioral context. However, it does not mention what happens if required parameters are missing (auto-detect fails?), rate limits, or other failure modes.

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

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

The description is two sentences with no wasted words. The first sentence front-loads the purpose and key features, the second adds essential cost and execution behavior. 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?

Given no output schema, the description should clarify what the tool returns (format, structure) but does not. It covers purpose, features, and cost but omits return value details and error handling. Adequate but with clear 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?

Input schema coverage is 100%, with each parameter described (chain, symbol, address). The description adds no further meaning to these parameters beyond what the schema provides, so a 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's purpose: token safety assessment. It lists specific aspects like audit status, rug pull signals, liquidity depth, holder concentration, honeypot detection, and social corroboration, providing a precise verb-resource pair ('assess token safety'). This distinguishes it from sibling tools focused on other areas (e.g., alpha_news, alpha_sentiment).

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 implicitly indicates use for token risk evaluation but provides no explicit guidance on when to choose this tool over siblings (e.g., alpha_deep for deeper analysis, alpha_token for general info). It does not specify prerequisites 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?

The description discloses the cost ($0.03 USDC) and that payment is consumed even on timeout, which are critical behavioral traits. However, no mention of success format, error behavior, or rate limits. With no annotations, the description partially covers transparency but lacks completeness.

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. The first explains what the tool does, and the second covers critical cost information. No unnecessary words, and front-loaded with purpose.

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 no annotations, the description should compensate by explaining return format. It mentions synthesis but not what is returned (text, URLs, etc.). The cost warning adds value, but overall completeness is average for a simple tool with one parameter.

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

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

The description does not add parameter-specific details beyond the input schema. Schema coverage is 100% (one param with description), so the baseline is 3. The tool name and description imply the query is for crypto, but no examples or format guidance are given.

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 neural web search combined with Twitter and AI synthesis using Exa and Grok. It distinguishes itself from sibling tools (e.g., alpha_memecoin) which are more specialized, making the general search purpose evident.

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 when-to-use or when-not-to-use guidance is provided. However, the name 'alpha_search' and the context of specialized sibling tools imply it is for general crypto intelligence queries. Lack of explicit alternatives or exclusions keeps this at implied usage.

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

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

Despite no annotations, the description discloses key behavioral traits: cost ($0.062 USDC), payment consumed on execution including timeouts (non-refundable), and a volume limit (up to 99 tweets). It could mention whether the tool is read-only or modifies data, but the cost and limit transparency is strong.

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, dense sentence that front-loads the core functionality. It efficiently conveys sources, metrics, scoring, and cost, but the length could be slightly reduced for readability.

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

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

Given one parameter, no output schema, and a straightforward purpose, the description provides a complete picture: what the tool does, its inputs, and its cost. Missing details like return format are minor given the 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 100% schema coverage, the baseline is 3. The description adds minimal extra meaning beyond the schema's 'Token or topic to analyze'. It does not specify format constraints or examples, but the schema alone is sufficient.

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: cross-source sentiment analysis on X and Reddit with engagement metrics, AI bull/bear scoring, and corroboration check. It differentiates from siblings like alpha_brief and alpha_compare by focusing specifically on sentiment.

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 sentiment analysis on a token/topic but doesn't explicitly state when to use vs alternatives, nor does it provide exclusion criteria or prerequisites. The mention of payment consumption is helpful but not a direct usage guideline.

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 only states it gets stats and is free, but does not confirm read-only nature, rate limits on the tool, or any side effects. This is insufficient for a tool with no annotations.

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

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

The description is two sentences with no unnecessary words. It is front-loaded with the action and resource, 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?

With no output schema, the description should fully explain the return values. It lists three example stats but is vague on format, units, or structure. Also lacks details on authentication requirements or potential errors. Adequate for a simple tool but not 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?

There are no parameters, and schema description coverage is 100% (trivially). The description adds meaning by listing the output fields (uptime, memory, rate limits), which helps the agent understand what data is 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 verb 'Get', the resource 'gateway stats', and specifies the scope 'uptime, memory, rate limits'. It is distinct from sibling tools which cover different functionalities like brief, calendar, compare, 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?

The description only mentions 'Free — no payment required' but provides no guidance on when to use this tool vs alternatives. There is no context about intended use cases 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?

With no annotations, description carries full burden and discloses key behaviors: prepaid HMAC-signed deliveries, limit of 5 deliveries, $0.05 USDC cost, payment consumed on execution. Missing details like handling of never-triggering conditions or webhook failures.

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

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

Two sentences with no wasted words. Front-loaded with core purpose and immediately followed by critical operational details.

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?

Description adequately covers purpose, mechanism, and cost. However, missing return value (e.g., subscription ID or confirmation) reduces completeness for agent invocation without 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 covers all 4 parameters with descriptions (100% coverage). Description adds marginal value by tying parameters to the payment model and delivery count, but does not significantly enhance parameter 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?

Description clearly states 'Create a notify-on-condition webhook subscription' with specific verb and resource, and distinguishes from sibling tools (e.g., alpha_brief, alpha_search) that serve different analytical 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?

Description provides clear context for when to use: for price/funding condition triggers. It does not explicitly mention alternatives or when not to use, but sibling context makes differentiation easy.

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. Discloses that payment is consumed on execution including timeouts, which is a key behavioral trait. However, it does not disclose return format, rate limits, or any authorization needs. The cost and timeout info add value but are 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?

Description is a few sentences long, front-loaded with the main purpose and key details (cost, Twitter included). No wasted words. Could be slightly more structured, but it is concise and readable.

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 a single parameter and no output schema, the description reasonably covers what the tool does, what data it returns (price, volume, holders, engagement, analysis), and cost behavior. It does not explain return schema, but since none is provided, this is acceptable. The description is fairly complete 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% with one required parameter 'symbol'. The schema description already explains 'Token symbol (e.g., SOL, WIF, PEPE)'. The description adds no additional meaning beyond the schema. Baseline 3 is appropriate since schema does the heavy lifting.

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

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

Clearly states it provides token intelligence with specific data points (price, volume, holders, X/Twitter engagement, analysis). The verb is implicit but the resource and scope are well-defined. However, it does not explicitly distinguish from similar alpha_* tools in the sibling list, leaving the agent to infer 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?

No explicit guidance on when or when not to use this tool. The mention of a $0.03 cost implies it is for paid detailed analysis versus free alternatives, but no alternatives are named or contrasted. Sibling tools exist (e.g., alpha_brief, alpha_deep) but description does not help the agent decide between them.

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 payment details ($0.03 USDC, consumed on execution including timeouts) and data sources (Twitter, AI-analyzed). This adds value beyond annotations (none provided) by informing the agent of cost and data nature.

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, efficiently front-loading key information (purpose, cost, inclusions). 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?

Covers purpose, data contents, and cost behavior. Lacks output format or field details but is acceptable given no output schema and simple list nature. Could mention if results are limited or sorted.

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, schema coverage is 100%, so baseline is 4. The description adds meaning by describing the output content (trending tokens, engagement data, narratives), enhancing understanding beyond the empty 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 'AI-analyzed trending tokens with X/Twitter engagement data and market narratives,' indicating specific verb (analyzed/trending) and resource (tokens). It is clear but does not explicitly differentiate from sibling tools.

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

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

No guidance on when to use this tool versus siblings like alpha_search or alpha_news. The description mentions cost but lacks context for selection.

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