x402-alpha

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

With no annotations provided, the description carries full burden. It discloses that payment of $0.20 USDC is consumed on execution, including timeouts. This is critical behavioral info beyond the schema.

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

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

Two sentences: first clearly states purpose and bundled content, second specifies cost and execution behavior. No fluff, front-loaded with key 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, the description could explain the output format (e.g., text summary). However, it mentions 'unified AI synthesis' which implies a condensed result. For a brief tool, this is mostly sufficient, though explicit output structure would improve completeness.

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

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

Schema coverage is 100% (chain, symbol, address are described in schema). The description mentions bundled data types but does not link them to parameters, so it adds marginal meaning beyond what schema already provides.

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

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

The description uses specific verb 'bundling' and clearly states that the tool provides a unified token brief combining market data, sentiment, on-chain transfers, and risk research with AI synthesis. This distinguishes it from sibling tools like alpha_sentiment or alpha_onchain which are more specialized.

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

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

The description implies usage when a quick comprehensive token overview is needed, but does not explicitly state when to use this tool versus alternatives (e.g., alpha_deep for deeper analysis). No exclusions or when-not-to-use guidance 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?

Without annotations, the description reveals that payment is consumed on execution and includes timeouts, which is useful behavioral context. However, it does not disclose other traits like data freshness or 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 short and front-loaded with purpose and cost. It is efficient, though slightly unstructured; a bullet list could improve 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 no output schema, the description omits what the response contains (e.g., list of events, dates, details). For a paid tool with 4 optional parameters, more context is needed for effective use.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds no parameter-level meaning beyond the schema's descriptions, missing opportunities to explain parameter interactions or usage tips.

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 'Upcoming crypto events' and lists examples like token unlocks and protocol upgrades, making the tool's function evident. It does not explicitly differentiate from sibling tools, but the name and context suffice.

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

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

No guidance is provided on when to use this tool versus alternatives like alpha_news or alpha_token. The description only mentions cost, not usage context 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?

The description discloses the cost ($0.05 USDC) and that payment is consumed even on timeouts, but does not mention authentication, rate limits, or expected output format. Since no annotations exist, the description partially fulfills the transparency burden.

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

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

The description is two sentences with zero wasted words. The purpose is stated first, followed by cost and behavior. Extremely concise 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?

Given no output schema and no annotations, the description covers purpose and cost but omits details like whether addresses are required if tokens not used, or what the return format is. It adequately describes the core functionality but leaves some 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 input schema covers all three parameters with descriptions, so the description adds no additional semantic value beyond the schema. Baseline score of 3 is appropriate given 100% 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 'Side-by-side token comparison' and lists the comparison dimensions (price action, volume, sentiment, fundamentals). This differentiates it from sibling tools like alpha_token (single token analysis) or alpha_sentiment (sentiment only), though it does not explicitly name alternatives.

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

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

No guidance is provided on when to use this tool versus alternatives such as alpha_token or alpha_deep. The description only states what the tool does, not when it is appropriate or when to avoid 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 and discloses important behaviors: cost ($0.10), maximum duration (60s), and the fact that payment is consumed even on timeout. While it does not cover error handling or rate limits, the provided details are substantive and honest.

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 purpose, sources, cost, and timeout. Every element is necessary, no filler, and the most critical information is 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 complexity (multi-source, cost, timeout, no output schema), the description adequately covers key aspects. It could be more complete by mentioning the return format or constraints, but it sufficiently informs an agent about the tool's nature and important behaviors.

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

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

The schema has 100% description coverage for the single parameter, so the description adds no additional meaning beyond the schema's own description. The baseline score of 3 is appropriate, as the schema already provides adequate context for the 'query' parameter.

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

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

The description explicitly states it performs 'Deep multi-source research' using specific sources (Exa, Firecrawl, Claude, up to 99 tweets), differentiating it from sibling tools like alpha_brief or alpha_search. The verb 'research' combined with the resource scope clearly defines the tool's unique value.

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 comprehensive research but does not explicitly specify when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The context of 'deep' suggests it is for thorough investigations, but guidance is lacking.

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 reveals important behavioral traits: cost ($0.05 USDC), payment consumed on execution including timeouts, and data sources. However, it does not disclose return format or side effects.

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

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

Two sentences efficiently convey purpose, data sources, and cost. No redundant information; each 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 optional parameter, no output schema), the description covers purpose, sources, and cost. Missing output format details, but the tool likely returns a text summary.

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 single parameter 'theme' is fully described in the schema with examples (e.g., inflation, employment, rates) and a default behavior. The description adds no additional meaning beyond what the schema already provides.

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

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

The description clearly states it provides a macro economic pulse using specific sources (FRED, prediction markets, Twitter, Grok). It distinguishes itself from sibling tools like alpha_brief or alpha_news by focusing on macro indicators.

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 macro economic queries but does not explicitly state when to use this tool versus alternatives like alpha_narrative or alpha_sentiment. No exclusions or context-specific guidance 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?

Discloses payment consumption and timeout behavior, but no annotations are provided. Lacks details on read-only vs write, auth, or rate limits, but the payment info adds some 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?

Two sentences, no wasted words. Could be more structured but is efficient 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?

No output schema, and the description fails to explain what the tool returns or how to interpret results. Key details about screener mode and per-token output are implied but not explicit.

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%, parameters are self-explanatory. Description hints at per-token usage (symbol/address) but does not add significant meaning beyond the schema.

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

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

The description clearly identifies it as a memecoin vertical tool for either a market-wide screener or per-token risk read, but does not use a specific verb like 'get' or 'list' for the action.

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 cost and payment consumption info but no guidance on when to use this tool versus siblings like alpha_token or alpha_trending. No when-not or alternative tool references.

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

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

No annotations are provided, so the description carries the full burden. It discloses the cost and billing behavior (consumed on execution, including timeouts). However, it does not mention other behavioral traits such as rate limits, response format, 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?

The description is concise with two sentences: the first explains the purpose, and the second covers cost and billing. It is front-loaded and efficient, though the cost mention could be considered a detail for a separate line.

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

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

There is no output schema, and the description does not explain the return format or structure of detected narratives. It also lacks details on default behavior when no parameters are provided. Given the complexity of a market narratives tool, more completeness is needed.

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%, meaning the input schema already documents all three parameters. The description adds only high-level examples of narrative slugs, which adds minimal value beyond what the schema's description of the 'narrative' parameter already provides. 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 detects and tracks active market narratives, and provides a list of examples (AI tokens, RWA, L2, memecoins, DePIN). This distinguishes it from siblings like alpha_trending or alpha_search, which have different focuses.

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 $0.05 USDC cost and that payment is consumed on execution, including timeouts, which is useful but does not provide explicit guidance on when to use this tool versus alternatives. The narrative focus implies its use case, but no exclusions or alternatives are named.

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

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

No annotations provided, so description must disclose behavior. It reveals that payment is consumed even on timeout, indicating a cost and non-refundability. But it doesn't disclose rate limits, whether the operation is read-only, or what 'AI synthesis' entails. Some transparency, but gaps remain.

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 covers purpose and sources, second covers cost and failure behavior. No wasted words, front-loaded with key information.

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

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

With no output schema, the description should clarify return format. It mentions 'AI-filtered news' and 'AI synthesis' but doesn't specify output structure (e.g., list of articles, summaries). Also lacks prerequisites like account or authentication. 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 description coverage is 100%, and the schema already provides clear parameter descriptions. The tool's description adds context (sources listed) but does not significantly enhance parameter meaning beyond the schema. Baseline 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 provides 'AI-filtered crypto news from CoinTelegraph, Decrypt, CoinDesk, Blockworks + X/Twitter with AI synthesis'. The verb is implied (fetch), and the resource is specified. Sibling tools like alpha_brief, alpha_calendar suggest different news angles, but the description doesn't explicitly differentiate.

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 and payment consumed on execution, including timeouts, which guides usage expectations. However, no explicit when-to-use or when-not-to-use compared to sibling tools. Usage context is implied but not thorough.

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

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

No annotations provided, so description must fully disclose behavioral traits. Mentions cost and that payment is consumed on execution including timeouts, indicating a paid tool. But does not describe side effects, authorization needs, rate limits, or what happens on failure beyond timeout.

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 lists capabilities, second adds cost policy. Concise and front-loaded with main purpose. Could be slightly more structured but efficient.

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

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

No output schema, so description should explain return values. Does not cover what the agent receives (e.g., alerts, metrics). Missing details on error conditions, prerequisites, or how to interpret results. For a paid tool, more completeness is needed.

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% (all 4 parameters have descriptions). The tool description does not add additional meaning beyond the schema. Baseline 3 is appropriate since the schema already documents parameters adequately.

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

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

Description clearly states the tool provides on-chain activity intelligence including whale movements, large transfers, DEX volume anomalies, and smart money tracking. This specific verb+resource combination distinguishes it from sibling tools like alpha_brief, alpha_news, 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?

No explicit guidance on when to use this tool vs alternatives. Does not mention when to use on-chain intelligence over other alpha tools (e.g., sentiment, narrative). Only includes cost and payment details but no usage context.

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

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

Without annotations, the description adds value by disclosing the cost ($0.05 USDC) and that payment is consumed on execution including timeouts. However, it does not mention rate limits, authentication requirements, data freshness (live vs cached), or any potential side effects. The description is partially transparent but leaves 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 extremely concise—two sentences that efficiently convey purpose, scope, cost, and parameter hints. Every sentence is purposeful, and the most critical information is front-loaded. No extraneous words.

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

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

Given no output schema or annotations, the description covers the tool's purpose, parameter usage, cost, and timeout behavior. It lacks details on the return format or data structure, but the core functionality is adequately described for an agent to invoke the tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds meaning beyond the schema by clarifying that omitting symbol returns market-wide extremes and that exchange defaults to both. This enhances parameter understanding beyond the schema's basic descriptions.

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

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

The description clearly states the tool provides perpetual futures funding rates across Hyperliquid and Gate, with additional crowded-trade extremes and squeeze-risk synthesis. It uses a specific verb (funding rates) and resource (perpetual futures), and the unique name distinguishes it from sibling tools like alpha_stats or alpha_deep.

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 some guidance by explaining when to omit the symbol parameter (for market-wide extremes) and the exchange parameter defaults. However, it does not explicitly state when to use this tool versus alternatives among the 18 sibling tools, such as alpha_deep or alpha_onchain. Usage context is implied but no exclusions or comparisons 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?

Since no annotations are provided, the description carries full burden. It discloses a $0.05 USDC cost and that payment is consumed on execution including timeouts. However, it does not mention if the tool is read-only, any authentication requirements, or behavior on failure. The cost transparency is valuable 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 concise with two clear parts: purpose list and cost info. It is front-loaded with the core purpose. The only minor issue is that cost info could be better integrated, but overall efficient.

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

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

The description is minimal for a potentially complex tool. It lists outputs (holdings, diversification, risk, rebalancing) but does not describe output format, prerequisites, or how results are structured. No output schema exists, so description should compensate but fails to provide sufficient context for an agent to anticipate return values.

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

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

Schema coverage is 100% (both parameters described). The description adds no extra meaning beyond the schema's 'chain for lookup (auto-detect from address format if omitted)' and 'wallet address'. Baseline 3 applies since description does not enhance parameter understanding.

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

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

The description explicitly states 'Wallet portfolio analysis — token holdings, diversification score, risk exposure, AI rebalancing suggestions,' which clearly defines its purpose as a comprehensive portfolio analyzer. It is well-differentiated from sibling tools like alpha_risk, alpha_token, etc., which focus on specific 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?

No explicit guidance on when to use this tool versus alternatives. For example, it doesn't mention that alpha_risk might be used for standalone risk metrics or alpha_token for single token analysis. The description assumes the user knows its applicability.

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 on execution including timeouts, which is important behavioral context. However, with no annotations, it does not disclose whether the tool is read-only, destructive, or has other side effects beyond payment.

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 that efficiently communicate purpose, data sources, and cost. No redundant information; front-loaded with key 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?

For a tool with no annotations or output schema, the description provides the essentials but does not explain what the tool returns (e.g., a summary, data points, or scores). The cost information is helpful, but the output behavior is unclear.

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 query and category parameters described in the schema. The description adds no additional meaning beyond what the schema provides, resulting in 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 states it provides prediction market intelligence from multiple sources (Polymarket, Kalshi, Twitter, Grok), clearly indicating the domain and data types. It distinguishes itself from sibling tools like alpha_brief and alpha_deep by focusing on prediction markets.

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 like alpha_brief or alpha_deep. The description implies it's for prediction market queries but does not state when not to use it or provide comparison with other 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?

The description notes that payment is consumed on execution, including timeouts, which is a key behavioral trait. However, it does not clarify if the tool is read-only or if it has any side effects, and no annotations are provided to fill this 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 concise, consisting of two sentences that efficiently convey the tool's purpose and cost. It is front-loaded with the most important information, leaving no unnecessary words.

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

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

The description lists the assessment criteria, which helps infer output content despite no output schema. It includes cost behavior, but omits details like rate limits or authentication requirements. Overall, it is fairly complete for a simple tool with no required parameters.

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

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

The input schema covers 100% of parameters with descriptions, so the schema already provides clear meaning. The description adds no extra detail beyond what is in the schema, 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 defines the tool as a 'Token safety assessment' and enumerates specific aspects it covers (audit status, rug pull signals, liquidity depth, holder concentration, honeypot detection). This distinguishes it from sibling tools like alpha_brief or alpha_stats, which focus on different 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 usage for token risk evaluation but does not explicitly state when to choose this over alternatives. It mentions a cost of $0.05 but lacks guidance on prerequisites or situations where another tool, like alpha_deep, would be more appropriate.

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. Only discloses payment consumption on execution (including timeouts). Lacks details on rate limits, data freshness, synthesis behavior, 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, front-loaded with purpose. Concise but could be better structured with bullet points or sub-sections 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?

Lacks output schema or description of return value. For a complex tool combining multiple sources and AI, this is insufficient. Sibling tools suggest differentiation is needed.

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

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

Schema coverage is 100%, and the schema description for 'query' is clear ('Search query for crypto intelligence'). The description adds no extra parameter guidance 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 performs 'neural web search + Twitter + AI synthesis', which is specific. However, it does not explicitly differentiate from sibling tools like alpha_news or alpha_deep, which might also involve search and synthesis.

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. Mentions payment consumption but no context on ideal scenarios 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?

The description discloses critical behavioral details: payment consumption ($0.05 USDC) and that payment is consumed even on timeouts. It also notes a limit of 99 tweets. This is valuable transparency for an execution decision, though it could mention any required permissions or side effects.

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

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

Two concise sentences front-load the purpose and output, followed by critical cost/behavior info. 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?

The description fully covers what the tool returns (tweets, metrics, scoring) and its cost behavior. Despite lacking output schema, it provides enough detail for an agent to understand the output format. Minor gap: no mention of response structure, but acceptable 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?

The schema has 100% coverage for the single 'symbol' parameter. The description adds context by explaining the symbol is used to fetch tweets, but does not enrich the semantics beyond what the schema's description ('Token or topic to analyze') already conveys. Baseline 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 explicitly states the tool returns up to 99 tweets with engagement metrics and AI bull/bear scoring, clearly distinguishing it from sibling tools like alpha_brief or alpha_news which focus on other data 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 provides no guidance on when to use this tool versus siblings. It does not mention use cases, prerequisites, or alternatives, leaving the agent to infer from the tool name alone.

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

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

With no annotations, the description carries the burden. It explicitly states the tool is for getting stats (read-only) and is free. However, it lacks details on response format, caching, or rate limits of the API itself.

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, 16 words. The function is front-loaded with the verb and resource. 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?

For a simple stats tool with no parameters and no output schema, the description adequately states what it does and that it's free. It covers the core purpose but could mention that it's read-only more explicitly.

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

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

No parameters exist, so the input schema is empty. The description adds full value by enumerating the specific stats returned (uptime, memory, rate limits), which is the only relevant semantic information.

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' and the resource 'gateway stats' with specific examples (uptime, memory, rate limits). This distinguishes it cleanly from siblings like alpha_news or 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?

No explicit guidance on when to use or exclude this tool. The mention 'Free — no payment required' hints that some siblings might require payment, but it doesn't provide concrete alternative suggestions or usage 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?

With no annotations, the description carries full burden. It discloses that the tool is a write operation (creates a subscription), lists key behavioral traits (5 prepaid HMAC-signed deliveries, payment consumed on execution, $0.05 USDC cost). However, it does not mention what happens after deliveries are exhausted or whether subscriptions can be cancelled.

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 extraneous words. It front-loads the core purpose and then adds cost details. Every sentence provides essential information.

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

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

For a subscription creation tool with no output schema and no annotations, the description effectively covers purpose, behavior (prepaid, HMAC, payment), and parameter context. Minor gaps remain (e.g., lifecycle after 5 deliveries, error handling), but overall it is complete enough for an AI agent to understand the tool's use.

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

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

Schema description coverage is 100%, so the schema already details each parameter (type, symbol, webhook, threshold). The description adds context about triggers (price/funding) and mentions 'prepaid' but does not significantly enhance parameter semantics beyond the schema. 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 creates a notify-on-condition webhook subscription, specifying the resource (subscription) and action (create). It distinguishes from sibling tools that are primarily data retrieval (e.g., alpha_brief, alpha_stats) by focusing on proactive alerts.

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 setting up alerts on price/funding conditions but does not explicitly state when to use this tool versus alternatives. No exclusions or alternative suggestions are provided, though the context of payment and prepaid deliveries gives some 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 provided, the description carries the burden of transparency. It discloses that a payment of $0.03 USDC is consumed on execution including timeouts, which is helpful. However, it does not mention whether the tool is read-only or has any side effects, leaving some uncertainty.

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

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

The description is extremely concise, comprising two sentences that front-load the core purpose ('AI-synthesized token intelligence') and then add cost information. Every sentence contributes value without redundancy.

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

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

Given the tool has only one parameter and no output schema, the description adequately covers the data types provided, cost, and execution consumption. It lacks explicit mention of output format (e.g., JSON) but is sufficient for a simple tool.

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

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

The schema covers 100% of parameters and the description adds the example '(e.g., SOL, WIF, PEPE)', which is consistent but does not significantly expand meaning beyond the schema. No additional semantic depth is provided.

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

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

The description explicitly states it provides 'AI-synthesized token intelligence' with specific data points (price, volume, holders, engagement), clearly indicating the verb and resource. However, it does not differentiate from similar sibling tools like alpha_brief or alpha_deep, which may also provide token data.

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

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

No guidance is given on when to use this tool versus its siblings (e.g., alpha_brief for summary, alpha_deep for deeper analysis). The context of cost and execution is mentioned but does not help an agent decide between 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 provided, so description must carry behavioral burden. It discloses a payment cost ('$0.03 USDC', 'Payment is consumed on execution, including timeouts'), adding useful transparency. However, it does not state read-only nature or other side effects beyond cost.

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, efficient and front-loaded. The information is presented directly without fluff, but the structure could be improved (e.g., separating pricing from functionality).

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 and no parameters, the description is minimal. It provides high-level purpose but lacks details on return format or data structure, which would help an agent understand the output. The presence of many sibling tools increases the need for differentiation, which is missing.

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

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

No parameters exist, so schema coverage is 100%. The description does not need to explain parameters, and the baseline of 4 is appropriate given zero 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 it provides 'AI-analyzed trending tokens with X/Twitter engagement data and market narratives'. The verb and resource are specific, but it does not differentiate from siblings like alpha_sentiment or alpha_narrative, which may also involve social data or narratives.

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 like alpha_sentiment or alpha_stats. It does not mention prerequisites, context, or exclusions, leaving the agent to infer usage from the name alone.

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