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 specific data fields, but does not cover critical aspects such as error handling (e.g., what happens if the symbol is invalid), rate limits, authentication requirements, or whether it's a read-only operation. For a tool with no annotation coverage, this represents 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 concise and front-loaded, stating the core purpose in the first clause. It efficiently lists the returned data fields without unnecessary elaboration. However, it could be slightly more structured by separating the action from the output details, but overall, it avoids redundancy and wastes no 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 low complexity (1 parameter, no nested objects) and high schema coverage, the description is adequate but incomplete. It lacks an output schema, so the description should ideally detail the return structure more thoroughly, but it does list key data fields. Without annotations, it misses behavioral context, making it minimally viable but with clear gaps for an AI agent to rely on.

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 the 'symbol' parameter clearly documented. The description adds minimal value beyond the schema by implying the symbol is used to fetch an agent's profile, but it does not provide additional context like format examples beyond 'oCC' or 'oTEST1', or explain how symbols relate to agents. Given the high schema coverage, 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: retrieving an AI agent's profile on Omni.fun using a token symbol. It specifies the verb ('Get') and resource ('AI agent's profile'), and lists the returned data fields (bio, capabilities, etc.). However, it does not explicitly differentiate this tool from sibling tools like 'get_token' or 'search_tokens', which might also retrieve token-related information, leaving some ambiguity about its uniqueness.

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 sibling tools like 'get_token' or 'search_tokens', nor does it specify prerequisites, exclusions, or contextual cues for selection. This lack of comparative information could lead to confusion in tool selection by an AI agent.

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 clearly indicates this is a read operation ('Get') and specifies the return data structure, but doesn't mention error conditions, rate limits, authentication requirements, or what happens with invalid symbols. The description adds value by explaining the graduation threshold ($69K market cap) and Uniswap V3 transition, which aren't obvious from the tool name alone.

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 perfectly concise - a single sentence that front-loads the core purpose and efficiently lists all return values. Every element earns its place: the action, the resource, and the comprehensive list of returned data points including the specific graduation threshold.

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

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

For a single-parameter read tool with no annotations and no output schema, the description provides excellent coverage of what the tool returns. It specifies five distinct data points including the graduation criteria. The main gap is lack of error handling information, but otherwise this is quite complete for its complexity level.

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 the single parameter 'symbol' well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema, but doesn't need to since the schema fully covers the single parameter. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

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 specific action ('Get the bonding curve state') and resource ('for a token'), with explicit details about what information is returned (price, tokens sold, total raised, progress, graduation status). It distinguishes itself from siblings like 'get_token' or 'search_tokens' by focusing specifically on bonding curve metrics rather than general token information.

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 bonding curve information is needed, but doesn't explicitly state when to use this tool versus alternatives like 'get_token' (which might provide different token data) or 'search_tokens' (which might help find tokens first). No explicit when-not-to-use guidance or prerequisite information is provided.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool provides 'step-by-step instructions' and includes contract interactions, but it does not disclose critical behavioral traits such as whether it requires authentication, rate limits, error handling, or the format of the returned instructions. This leaves significant gaps for an agent to understand how to invoke it effectively.

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 ('Get step-by-step instructions for buying a token on Omni.fun') and adds necessary context ('including which contracts to interact with on each chain'). There is no wasted text, making it highly 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 the lack of annotations and output schema, the description is incomplete for a tool that likely returns complex instructions. It does not explain the return format (e.g., structured steps, links, or code snippets), error conditions, or dependencies. For a tool with 2 parameters and no structured output, more detail is needed to ensure the agent can handle responses appropriately.

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, clearly documenting both parameters (symbol and chain) with their types and purposes. The description adds no additional semantic details beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as 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's purpose with a specific verb ('Get') and resource ('step-by-step instructions for buying a token on Omni.fun'), including details about contracts and chains. It distinguishes itself from siblings like get_token (which likely retrieves token data) or simulate_trade (which likely simulates trades rather than providing instructions).

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 user needs buying instructions for a token on Omni.fun, but it does not explicitly state when to use this tool versus alternatives like get_token (for token info) or simulate_trade (for trade simulation). No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.

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 adds valuable context beyond basic functionality by detailing the underlying mechanisms (deBridge DLN for buys, Across or deBridge adapters for sells), which helps the agent understand the tool's operational context. However, it lacks information on potential rate limits, error conditions, or response format.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by additional context in the second. Both sentences are necessary and efficient, with zero waste or redundancy, making it appropriately sized 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 the tool's complexity (simple read operation with no parameters) and lack of annotations or output schema, the description is mostly complete. It explains what the tool does and provides trading context, but it could improve by mentioning the return format (e.g., list structure) or any prerequisites, though this is less critical for a zero-parameter tool.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so there is no need for parameter documentation. The description appropriately focuses on the tool's purpose and context without redundant parameter details, earning a baseline score of 4 for zero-parameter tools.

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: 'Get the list of supported chains for cross-chain trading on Omni.fun.' It specifies the verb ('Get'), resource ('list of supported chains'), and domain context ('cross-chain trading on Omni.fun'), distinguishing it from sibling tools like get_token or search_tokens.

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 by explaining its role in cross-chain trading (buys use deBridge DLN, sells use Across or deBridge). However, it does not explicitly state when not to use it or name alternatives among sibling tools, such as get_how_to_buy or simulate_trade, which might be relevant for trading-related queries.

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 return data (price, market cap, etc.), which is useful behavioral context beyond the input schema. However, it doesn't mention potential errors (e.g., invalid address), rate limits, authentication needs, or data freshness, leaving gaps for a read operation.

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

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

The description is a single, well-structured sentence that front-loads the purpose and efficiently lists return data. Every word earns its place, with no redundancy or fluff, making it highly concise and easy to parse.

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

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

Given the tool's low complexity (one required parameter) and no output schema, the description is reasonably complete: it states the purpose, key input, and return data. However, it could improve by addressing potential errors or limitations, as no annotations cover behavioral aspects. It's adequate but not exhaustive for a read tool.

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

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

Schema description coverage is 100%, with the parameter 'address' fully documented in the schema. The description adds no additional parameter semantics beyond implying it's for token lookup. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't 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 clearly states the specific action ('Get detailed information'), target resource ('a specific token on Omni.fun'), and key identifier ('by its contract address'). It distinguishes from siblings like 'search_tokens' (which likely searches rather than fetches a specific token) and 'get_trending_tokens' (which likely lists trending tokens rather than details for one).

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 context ('by its contract address') but doesn't explicitly state when to use this tool versus alternatives. For example, it doesn't clarify if this should be used instead of 'search_tokens' when the exact address is known, or mention prerequisites like token support. The guidance is functional but lacks explicit 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns specific data fields and is for discovery, but lacks details on rate limits, authentication needs, error handling, or pagination behavior. For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.

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

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

The description is two sentences, front-loaded with the core purpose and followed by usage guidance. It avoids redundancy and wastes no words, making it efficient. However, it could be slightly more structured (e.g., separating purpose from return details) to enhance clarity, preventing a perfect score.

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 low complexity (one optional parameter, no output schema, no annotations), the description is moderately complete. It covers the purpose and return data but lacks behavioral details like rate limits or error handling. Without annotations or output schema, the description should do more to compensate, but it's adequate for a simple read tool, scoring at the minimum viable level.

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 the single parameter 'limit' fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain default behavior or usage context for the limit). This meets the baseline of 3 since the schema handles the 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: 'Get the list of trending/active tokens on Omni.fun' with specific details about the returned data (names, symbols, prices, market caps, bonding curve progress). It distinguishes itself from siblings like 'get_token' (likely for single tokens) and 'search_tokens' (likely for filtered searches) by focusing on trending/active tokens. However, it doesn't explicitly contrast with all siblings, keeping it from a perfect 5.

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 implied usage guidance: 'Use this to discover what tokens are available for trading' suggests it's for discovery purposes. However, it doesn't explicitly state when to use this versus alternatives like 'search_tokens' or 'get_token', nor does it mention any exclusions or prerequisites. The guidance is helpful but not comprehensive.

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 search functionality and return data, but lacks details on permissions, rate limits, error handling, pagination, or whether this is a read-only operation. For a search tool with zero annotation coverage, this leaves significant gaps in understanding its 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 functionality ('Search for tokens on Omni.fun by name or symbol') and includes the return value. There is zero waste, and every part of the 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 moderate complexity (search with one parameter), 100% schema coverage, and no output schema, the description is minimally adequate. It covers what the tool does and what it returns, but lacks context on behavioral aspects like rate limits or error cases, which would be helpful 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?

The input schema has 100% description coverage, with the single parameter 'query' documented as 'Search query (token name or symbol)'. The description adds no additional meaning beyond this, such as query syntax, case sensitivity, or partial matching rules. Baseline 3 is appropriate 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's purpose with specific verbs ('Search for tokens') and resources ('tokens on Omni.fun'), and mentions what information is returned ('current price and market cap'). However, it doesn't explicitly differentiate from sibling tools like 'get_token' or 'get_trending_tokens', which likely retrieve tokens by different criteria.

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 when to prefer this over 'get_token' (likely for specific token lookup) or 'get_trending_tokens' (likely for popular tokens), nor does it specify prerequisites or exclusions for 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 carries the full burden of behavioral disclosure. It effectively states that this is a simulation tool ('Does NOT execute a trade'), which clarifies it's non-destructive and likely read-only. It also adds useful context by specifying the bonding curve formula used for calculations. However, it lacks details on potential limitations like rate limits, error conditions, or authentication needs.

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

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

The description is front-loaded with the core purpose in the first sentence, followed by critical behavioral context ('Does NOT execute a trade') and technical details (the bonding curve formula). Every sentence earns its place by adding essential information without redundancy, making it highly efficient and well-structured.

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

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

Given the tool's complexity (simulation with a bonding curve formula), no annotations, and no output schema, the description does a good job of covering key aspects: purpose, non-execution behavior, and the calculation method. However, it lacks information on output format (e.g., what the simulation returns) and any error handling, which would be beneficial for full 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?

The input schema has 100% description coverage, providing clear details for all three parameters (symbol, side, amount). The description adds minimal value beyond the schema, as it only implicitly references the parameters through the explanation of buy/sell scenarios. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't significantly 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 clearly states the specific action ('simulate a buy or sell trade'), the resource ('tokens'), and the purpose ('estimate how many tokens you'd receive for a given USDC amount or vice versa'). It explicitly distinguishes itself from execution tools by stating 'Does NOT execute a trade,' which is crucial for differentiation from potential siblings that might perform actual trades.

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 estimate token amounts before executing a trade, specifically for buy/sell simulations. However, it does not explicitly mention when not to use it (e.g., for actual trading) or name alternatives among the sibling tools, such as which tools might handle real trades or other related functions.

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