memeoracle

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 mentions output includes 'top tokens by volume with momentum scores' but fails to detail critical behaviors such as data sources, update frequency, rate limits, authentication needs, or error handling. This leaves significant gaps for an agent to understand operational constraints.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by a practical use-case example. It uses two concise sentences without redundancy, though the second sentence could be more tightly integrated to avoid slight fragmentation.

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 cryptocurrency data analysis, no annotations, and no output schema, the description is insufficient. It omits details on return format (e.g., structure of token data, momentum score ranges), error conditions, and how 'hot' is defined, making it incomplete for reliable agent operation.

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

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

Schema description coverage is 100%, so the schema already documents both parameters ('chain' and 'limit') with descriptions and defaults. The description adds minimal value beyond the schema by implying the tool scans chains for tokens, but does not provide additional context like what 'momentum scores' entail or how chains are validated.

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: to identify 'hot' tokens on a specific blockchain by volume with momentum scores, using specific verbs like 'scan' and resources like 'tokens' and 'chain'. It distinguishes from siblings by focusing on current volume-based trends rather than new launches, memes, or security checks. However, it doesn't explicitly differentiate from tools like 'trending_memes' or 'whale_watch' beyond the examples given.

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 scouting tokens on popular chains like Solana, Base, Ethereum, and BSC, suggesting it's best for real-time trend analysis. However, it lacks explicit guidance on when to use this tool versus alternatives such as 'new_launches' for recent tokens or 'trending_memes' for meme-specific trends, and does not mention exclusions or prerequisites.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions checking server health and API connectivity, implying a read-only diagnostic operation, but lacks details on permissions, rate limits, response format, or any side effects. This is a significant gap for a tool with zero annotation coverage.

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

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

The description is a single, efficient sentence that lists key components (server health, API connectivity, supported chains, tool list, pricing) without unnecessary words. It's appropriately sized for a no-parameter tool, though it could be slightly more structured for clarity.

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

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

Given the tool's complexity (simple diagnostic with 0 parameters) and lack of annotations and output schema, the description is moderately complete. It covers what the tool checks but misses behavioral details like response format or usage context. It's adequate but has clear gaps in transparency and guidelines.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so the schema already fully documents the lack of inputs. The description doesn't need to add parameter information, and it appropriately doesn't mention any, earning a baseline score of 4 for this dimension.

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: checking server health, API connectivity for specific services (DexScreener, CoinGecko, SerpAPI), supported chains, tool list, and pricing. It uses specific verbs like 'check' and lists concrete resources, though it doesn't explicitly differentiate from sibling tools like 'chain_radar' or 'token_scan'.

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. The description lists what it does but doesn't indicate scenarios for its use, prerequisites, or comparisons with sibling tools like 'rug_check' or 'whale_watch'.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It describes the scoring logic and output format (0-100 score with ratings), which adds value. However, it lacks critical details like whether this is a read-only operation, data sources, rate limits, or error handling, leaving significant gaps for a tool with computational output.

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

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

The description is a single, efficient sentence that front-loads the core function and output. Every phrase adds value: the composite metrics, scoring range, and rating scale. It could be slightly more structured but avoids redundancy.

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

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

Given no annotations and no output schema, the description partially compensates by explaining the scoring logic and output format. However, for a tool with computational complexity and three parameters, it should ideally cover more behavioral aspects like data freshness or interpretation guidelines to be fully complete.

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

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

Schema description coverage is 100%, so parameters are documented in the schema. The description adds no parameter-specific information beyond what the schema provides (chain, query, address). Baseline 3 is appropriate as the schema handles parameter documentation 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?

The description clearly states the tool performs 'composite momentum scoring' based on specific metrics (volume surges, price acceleration, buy pressure, boost activity) and outputs a score with ratings. It distinguishes from siblings by focusing on momentum scoring rather than health checks, new launches, or other functions. However, it doesn't explicitly name the resource being scored (tokens), which slightly reduces specificity.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'viral_score' or 'trending_memes'. It doesn't mention prerequisites, exclusions, or comparative contexts. Usage is implied through the scoring focus but not explicitly stated.

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 of behavioral disclosure. It mentions the tool provides 'profiles, descriptions, and social links,' which hints at read-only behavior, but doesn't explicitly state whether it's a read operation, if it requires authentication, or any rate limits. For a tool with no annotations, this is a significant gap in transparency about its operational characteristics.

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

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

The description is concise and front-loaded, with two sentences that efficiently convey the core purpose and a key usage hint. There's no wasted language, and it avoids redundancy. However, it could be slightly more structured by explicitly separating purpose from guidelines.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose and a bit of context but lacks details on behavioral traits, output format, or sibling differentiation. Without annotations or an output schema, more completeness would be beneficial, but it's not entirely inadequate.

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, with clear documentation for both parameters ('chain' and 'limit'). The description doesn't add any parameter-specific information beyond what the schema provides, such as example chains or usage tips. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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: 'Discover the freshest token launches with profiles, descriptions, and social links.' It specifies the verb ('Discover') and resource ('freshest token launches'), and includes additional context about what information is provided. However, it doesn't explicitly differentiate from sibling tools like 'trending_memes' or 'chain_radar' which might also involve tokens or launches.

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 implied usage context with 'Catch tokens in their first minutes/hours,' suggesting it's for monitoring new launches. However, it offers no explicit guidance on when to use this tool versus alternatives like 'token_scan' or 'trending_memes,' nor does it mention any prerequisites or exclusions. This leaves the agent with minimal direction 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 the full burden of behavioral disclosure. It mentions the tool returns a risk score 0-100 with detailed flags, which gives some output context, but lacks critical details such as data sources, rate limits, authentication needs, error handling, or whether the analysis is real-time or cached. For a risk assessment tool with zero annotation coverage, this is a significant gap in 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 front-loaded with the core purpose and efficiently lists analysis dimensions in a single sentence, followed by output details. It avoids redundancy and wastes no words, though it could be slightly more structured by separating usage context from behavioral traits. Overall, it is appropriately sized for its complexity.

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

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

Given the tool's complexity (risk assessment with multiple analysis dimensions), no annotations, and no output schema, the description is moderately complete. It covers the purpose and output format but lacks details on behavioral aspects like data freshness, reliability, or error cases. Without annotations or output schema, more context on operational constraints would improve completeness for safe agent 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%, with all three parameters (chain, query, address) documented in the schema. The description does not add any meaning beyond what the schema provides, such as explaining parameter relationships (e.g., using address vs. query) or format specifics. Baseline 3 is appropriate since the schema handles parameter documentation 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?

The description clearly states the tool's purpose with specific verbs ('Risk assessment for any token') and resources ('token'), listing key analysis dimensions like liquidity depth, age, volume patterns, buy/sell ratio, and FDV analysis. It distinguishes itself from siblings by focusing on comprehensive risk scoring rather than monitoring (e.g., chain_radar, whale_watch) or trend analysis (e.g., trending_memes, viral_score).

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or comparisons to sibling tools like health_check or token_scan, which might overlap in functionality. Usage is implied only through the purpose statement, leaving the agent to infer context without explicit direction.

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 lists output metrics but doesn't describe how the scan works (e.g., real-time vs. cached data, rate limits, authentication needs, or error handling). This leaves gaps in understanding the tool's operational behavior.

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

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

The description is appropriately sized and front-loaded, starting with the core action ('Deep scan any token') followed by metrics and search methods. It avoids redundancy, though it could be slightly more structured (e.g., separating input guidance from output 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?

Given the complexity of scanning tokens across multiple metrics and chains, with no annotations and no output schema, the description is moderately complete. It covers what the tool does and basic inputs, but lacks details on output format, error cases, or integration with sibling tools, leaving room for improvement.

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 all three parameters (chain, query, address). The description adds marginal value by mentioning search methods ('name, symbol, or contract address'), but doesn't provide additional syntax, format details, or usage examples beyond what the schema specifies.

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 with specific verbs ('Deep scan') and resources ('any token'), listing comprehensive metrics like price, liquidity, volume, etc. It distinguishes from siblings by focusing on token scanning rather than chain monitoring or trend analysis, though it doesn't 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?

The description implies usage through the phrase 'Search by name, symbol, or contract address,' suggesting when to use this tool. However, it lacks explicit guidance on when to choose this over siblings like chain_radar or trending_memes, and no exclusions or prerequisites 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 of behavioral disclosure. It mentions data sources (DexScreener + CoinGecko) and filtering capability, but lacks details on rate limits, authentication needs, response format, or whether results are real-time/cached. This leaves gaps in understanding operational behavior.

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

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

The description is a single, efficient sentence that front-loads the core purpose and includes essential details (sources, filtering). Every word earns its place with no redundancy or unnecessary elaboration.

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

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

Given no annotations and no output schema, the description adequately covers the tool's purpose and basic usage. However, for a data-fetching tool with potential complexity (e.g., ranking criteria, data freshness), it lacks details on return values, error handling, or data update frequency, leaving room for improvement.

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 fully documents both parameters (chain and limit). The description adds no additional parameter semantics beyond what's in the schema, such as explaining what 'hottest' or 'most-boosted' means in relation to these parameters. Baseline 3 is appropriate when 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?

The description clearly states the verb 'Get' and specifies the resource 'hottest trending and most-boosted memecoins' from specific sources (DexScreener + CoinGecko). It distinguishes itself from siblings like 'new_launches' (which might focus on recent tokens) and 'viral_score' (which might measure virality metrics) by emphasizing trending/boosted status.

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

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

The description provides clear context for when to use this tool: to find trending memecoins with filtering by chain. However, it does not explicitly state when NOT to use it or name alternatives among siblings (e.g., 'new_launches' for newly launched tokens instead of trending ones), which prevents a perfect score.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the data sources (Google Trends + DexScreener) and the categorical output, but doesn't describe what the analysis entails, whether it requires specific permissions, rate limits, or how the categorization is determined. For a tool with no annotation coverage, this leaves significant behavioral 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 a single, efficient sentence that front-loads the purpose ('viral potential analysis'), specifies data sources, and clearly states the output format. Every element earns its place with zero wasted words, making it easy for an agent to parse quickly.

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 viral analysis with multiple data sources and no output schema, the description is minimally adequate. It covers the purpose and output categories but lacks details on the analysis methodology, error handling, or example outputs. With no annotations and no output schema, more context would be helpful for an agent to use this tool 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?

Schema description coverage is 100%, with all three parameters clearly documented in the schema itself. The description adds no parameter-specific information beyond what the schema provides, such as how 'chain', 'query', and 'address' interact in the analysis. This meets the baseline score of 3 when the 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?

The description clearly states the tool performs 'viral potential analysis' using Google Trends and DexScreener data, with a specific outcome of categorizing tokens into five distinct states (DEAD, QUIET, BUZZING, VIRAL, MEGA_VIRAL). It distinguishes itself from siblings by focusing on viral potential assessment rather than health checks, scans, or trend monitoring. However, it doesn't explicitly contrast with similar tools like 'momentum_score' or 'trending_memes'.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or comparisons with sibling tools like 'momentum_score' or 'trending_memes' that might offer overlapping functionality. The agent must infer usage from the purpose alone without explicit direction.

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 of behavioral disclosure. It mentions 'smart money signals' and 'who is pumping money into tokens', which implies read-only data retrieval about market activity, but it doesn't specify data sources, update frequency, accuracy, or any limitations (e.g., rate limits, authentication needs). For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.

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

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

The description is concise with two sentences that efficiently state the purpose and key features ('whale activity' and 'CTO detection'). It's front-loaded with the main idea and avoids unnecessary details. However, it could be slightly more structured by explicitly separating the two signal types for clarity.

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

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

Given the complexity of financial/market data tools, no annotations, and no output schema, the description is incomplete. It lacks details on what the output includes (e.g., token names, metrics, timestamps), how signals are calculated, data freshness, or any disclaimers about reliability. This makes it inadequate for an agent to fully understand the tool's context and 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?

The input schema has 100% description coverage, with clear documentation for both parameters ('chain' and 'limit'). The description doesn't add any meaningful semantics beyond what the schema provides—it doesn't explain how these parameters affect the signals (e.g., how 'chain' filtering works) or provide usage examples. Given the high schema coverage, a baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 the tool provides 'smart money signals' including 'top boosted tokens (whale activity)' and 'community takeovers (CTO detection)', which gives a general sense of purpose. However, it's somewhat vague about what specific data or metrics are returned, and while it mentions 'whale activity' and 'CTO detection', it doesn't clearly differentiate this tool from potential siblings like 'trending_memes' or 'viral_score' that might also track token popularity.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for when it's most useful (e.g., market analysis, investment decisions), or how it differs from sibling tools like 'trending_memes' or 'viral_score'. The user is left to infer usage based on the general purpose alone.

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