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 reveals that activation 'Grants 100M KRW for simulated trading,' which is a useful behavioral trait (resource allocation). However, it lacks details on authentication needs, rate limits, error conditions, or whether this is a one-time or repeatable operation, leaving gaps for a mutation tool.

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

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

The description is highly concise and front-loaded: two sentences that directly state the action and a key benefit. Every sentence earns its place by providing essential information without redundancy or fluff.

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

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

Given the tool's complexity (a mutation with no parameters) and the absence of annotations and output schema, the description is partially complete. It explains the core action and a reward, but lacks details on return values, error handling, or side effects, which are important for an activation tool in a financial simulation context.

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

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

The tool has 0 parameters, and schema description coverage is 100%. The description adds no parameter information, which is appropriate since there are no parameters to document. This meets the baseline expectation for a parameterless tool, though it doesn't compensate for any schema gaps (none exist).

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: 'Activate your pex.bot account by registering this device.' It specifies the verb ('activate'), resource ('pex.bot account'), and action ('registering this device'). However, it doesn't explicitly differentiate from sibling tools like 'register' or 'join_autonomous', which might have overlapping activation-related functions.

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: it's for activating an account on a specific device, which suggests it should be used during initial setup or device registration. However, it provides no explicit guidance on when to use this tool versus alternatives like 'register' or 'join_autonomous', nor does it mention prerequisites 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool cancels orders but does not address critical aspects like permission requirements, whether cancellation is reversible, rate limits, or error conditions. This is a significant gap for a mutation tool.

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

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

The description is a single, efficient sentence that front-loads the core action ('cancel an open order') without unnecessary words. Every part of the sentence contributes directly to understanding the tool's 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 the tool's complexity (a mutation with no annotations or output schema), the description is incomplete. It lacks details on behavioral traits, error handling, and return values, which are essential for safe and effective use by an AI agent.

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

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

Schema description coverage is 100%, with the parameter 'order_id' fully documented as a UUID. The description adds no additional semantic context beyond what the schema provides, such as format examples or validation rules, so it meets the baseline for high schema coverage.

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

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

The description clearly states the specific action ('cancel') and resource ('an open order by its ID'), distinguishing it from siblings like 'place_order' or 'get_orderbook'. It precisely communicates what the tool does without being vague or tautological.

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 specifying 'open order', suggesting it should not be used for closed or non-existent orders. However, it does not explicitly state when to use alternatives (e.g., 'get_my_runs' to check order status) or provide clear exclusions, leaving some ambiguity.

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 states a read operation ('Get'), but doesn't mention permissions, rate limits, freshness of data, or what 'across all assets' entails (e.g., aggregated total vs. detailed breakdown). This leaves significant gaps 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 front-loads the core action ('Get your current account balance') and adds clarifying scope ('across all assets'). There is zero waste, making it appropriately sized and well-structured for its 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 the tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits like data format or usage context. Without annotations or output schema, it should do more to be fully complete, but it meets the basic threshold for a simple 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?

The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description adds no parameter information, but with 0 params, the baseline is 4 as it doesn't need to compensate for any gaps. It implies no inputs are required, which aligns with the schema.

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

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

The description clearly states the verb 'Get' and the resource 'current account balance across all assets', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_profile' or 'get_ticker', which might also retrieve financial information, so it's not 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 no guidance on when to use this tool versus alternatives like 'get_profile' (which might include balance) or 'get_markets' (which could show asset values). It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.

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 states it 'lists' markets, implying a read-only operation, but doesn't cover aspects like rate limits, authentication needs, pagination, or response format. For a 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 directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, 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 tool has no parameters, no annotations, and no output schema, the description is minimally complete for a simple listing tool. However, it lacks details on what 'symbol info' includes or how the data is structured, which could help the agent use the output effectively. It's adequate but has clear gaps in context.

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

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

The input schema has 0 parameters with 100% coverage, so there are no parameters to document. The description doesn't need to add parameter semantics, and it correctly avoids mentioning any. A baseline of 4 is appropriate as it doesn't mislead about parameters, though it could slightly elaborate on implicit filtering if any exists.

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

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

The description clearly states the verb ('List') and resource ('all available trading markets with their symbol info'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_ticker' or 'get_orderbook', which might also provide market-related information, so it doesn't reach the highest 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 doesn't mention when to prefer it over siblings like 'get_ticker' (which might show current prices) or 'get_orderbook' (which might show order depth), leaving the agent to infer usage based on tool names 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 provided, the description carries the full burden of behavioral disclosure. It describes what information is returned (status, performance, active runs) but lacks critical details: whether this is a read-only operation, if it requires authentication, rate limits, or error conditions. For a 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 two sentences that are front-loaded with the core purpose and efficiently list the key information returned. Every sentence adds value without redundancy. It could be slightly more structured but is appropriately sized for the tool's 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 has no parameters, no annotations, and no output schema, the description provides basic context about what the tool does and returns. However, it lacks details on behavioral aspects (e.g., authentication needs, response format) and doesn't fully compensate for the absence of structured data, making it minimally adequate but with clear gaps.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, which aligns with the schema. Baseline is 4 for zero parameters, as the description doesn't need to compensate for any gaps.

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

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

The description clearly states the tool's purpose: 'Get your Autonomous participation status' with specific details about what it shows (active runs, performance, status). It distinguishes from siblings like get_balance or get_profile by focusing on 'Autonomous participation' runs. However, it doesn't explicitly differentiate from potential similar tools not in the sibling list.

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 (like needing to be registered or having active runs), nor does it suggest when to use join_autonomous or activate instead. The context is implied but not explicit.

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 states the tool retrieves data ('Get'), implying a read-only operation, but doesn't mention potential rate limits, latency, or whether it requires authentication (unlike siblings like 'place_order' that likely do). This leaves gaps in understanding the tool's operational context.

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 without any wasted words. It directly addresses what the tool does, making it easy to parse and understand 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?

For a read-only data retrieval tool with no output schema and no annotations, the description is minimally adequate. It covers the basic purpose but lacks details on return format (e.g., structure of bid/ask levels), error conditions, or integration with sibling tools. Given the complexity of financial data and the presence of related tools, more context 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?

The input schema has 100% description coverage, clearly documenting both parameters ('symbol' and 'depth') with details like default values. The description adds no additional semantic meaning beyond what the schema provides, such as explaining how 'depth' affects performance or what 'bid/ask levels' entail. Baseline 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 action ('Get') and resource ('current orderbook (bid/ask levels) for a market'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_ticker' or 'get_markets', which might also provide market data but in different formats.

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 'get_ticker' for price data or 'get_markets' for market listings. It lacks any context about prerequisites, such as needing an active market session or authentication, which could be relevant given sibling tools like 'activate' or 'register'.

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

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

No annotations are provided, so the description carries full burden. It states a read operation ('Get'), implying it's likely safe and non-destructive, but doesn't disclose any behavioral traits such as authentication requirements, rate limits, error conditions, or what specific information is returned. This leaves significant gaps for an agent to understand how the tool behaves in practice.

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, clear sentence that front-loads the key action and resource. There's no wasted verbiage or redundancy, making it highly efficient and easy to parse. It earns its place by succinctly conveying the core 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 the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'profile information' entails (e.g., user details, settings, status), how the data is returned, or any operational constraints. For a tool with no structured metadata, this minimal description fails to provide enough context for reliable 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate here. Baseline is 4 for zero parameters, as the schema fully handles the lack of inputs without requiring description compensation.

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

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

The description clearly states the action ('Get') and resource ('your pex.bot account profile information'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_balance' or 'get_my_runs' beyond specifying 'profile information', which is somewhat generic. It avoids tautology since the name 'get_profile' alone doesn't specify 'account' or 'pex.bot'.

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, context for retrieving profile info, or comparisons to siblings like 'get_balance' or 'get_my_runs'. The agent must infer usage based on the name alone, which is insufficient for optimal 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. It implies a read operation ('Get') but doesn't disclose behavioral traits such as rate limits, authentication needs, error handling, or what 'ticker information' entails (e.g., price, volume). This is inadequate for a tool with no 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 front-loads the core action and resource. There is no wasted text, making it appropriately sized and easy 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'ticker information' includes (e.g., fields returned) or behavioral aspects like performance or errors. For a data retrieval tool in a financial context, this leaves significant gaps for the agent.

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

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

Schema description coverage is 100%, with the parameter 'symbol' well-documented in the schema as 'Market symbol, e.g., "BTC-KRW"'. The description adds no additional parameter semantics beyond this, so it meets the baseline for high schema coverage without compensating value.

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 resource ('current ticker information') with scope ('for a specific market'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_markets' or 'get_orderbook', which might provide related market data, so it doesn't reach the highest 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 minimal guidance by specifying 'for a specific market', but it lacks explicit when-to-use instructions, alternatives (e.g., vs. 'get_markets' for listing markets or 'get_orderbook' for depth data), or exclusions. This leaves the agent with little context for tool selection among siblings.

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

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it creates an account with specific capital (100M KRW), grants trading access to 318 markets with 'minimal constraints,' and imposes requirements for orders (reason and confidence). It doesn't mention rate limits, error conditions, or response format, but covers the core operational behavior adequately.

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 highly concise and front-loaded, with three sentences that each earn their place: the first states the core action and resources, the second defines constraints and scope, and the third specifies order requirements. There is zero wasted text, making it efficient 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 complexity (investment account creation with trading rules), no annotations, and no output schema, the description is reasonably complete. It covers the purpose, capital, market access, and order constraints, but lacks details on return values, error handling, or authentication needs. For a tool with this scope, it's adequate but not exhaustive.

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 for its single parameter ('model_name'), so the baseline is 3. The description adds value by implying the parameter's purpose in context ('Your AI model name for display'), though it doesn't provide additional syntax or format details beyond the schema. This slight enhancement justifies a score above baseline.

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

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

The description clearly states the specific action ('Join Autonomous AI investment') and resource ('dedicated account with 100M KRW seed capital'), distinguishing it from siblings like 'register' or 'activate' by specifying investment-focused functionality. It provides concrete details about capital amount and market access that differentiate it from other tools.

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

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

The description implies usage context through phrases like 'your AI can freely trade 318 markets' and 'Every order must include reason and confidence,' suggesting this is for AI-driven trading scenarios. However, it doesn't explicitly state when to use this versus alternatives like 'register' or 'activate,' nor does it provide exclusion criteria 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 adds valuable context about required fields for Autonomous accounts and the importance of specific, time-bound reasons, warning that generic reasons 'will be publicly visible and reflect poorly on your analysis.' However, it doesn't disclose other behavioral traits like rate limits, authentication needs, error handling, or what happens after order placement (e.g., confirmation, execution details). The description provides some behavioral insight but leaves gaps for a financial transaction tool.

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

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

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by specific requirements and warnings. Every sentence adds value—no wasted words. However, it could be slightly more structured (e.g., bullet points for requirements) but remains efficient and clear.

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 (11 parameters, financial transaction tool) and no annotations or output schema, the description is moderately complete. It covers key aspects like Autonomous account requirements and reasoning quality, but lacks details on behavioral traits (e.g., rate limits, auth), error scenarios, or return values. For a tool with significant parameters and no structured output, it should provide more context to be fully helpful to an AI agent.

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

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

Schema description coverage is 100%, so the schema already documents all 11 parameters thoroughly. The description adds minimal parameter semantics beyond the schema: it emphasizes that reason_ko, reason_en, and confidence are 'required for Autonomous accounts' and that reasons must be 'specific and time-bound,' referencing a guide. However, it doesn't provide additional syntax, format details, or examples beyond what's in the schema descriptions. With high schema coverage, the baseline is 3.

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

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

The description clearly states the tool's purpose: 'Place a buy or sell order on a market.' It specifies the verb ('place') and resource ('order'), but doesn't explicitly differentiate from sibling tools like 'cancel_order' or 'activate' beyond the core action. The description is specific about the action but lacks sibling comparison context.

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 usage context: 'For Autonomous accounts, reason_ko, reason_en, and confidence are required' and references a 'trade_reasoning_guide prompt.' However, it doesn't explicitly state when to use this tool versus alternatives like 'cancel_order' or 'activate,' nor does it provide clear exclusions or prerequisites beyond the Autonomous account requirements. The guidance is implied rather than explicit.

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 and does well by disclosing key behavioral traits: it handles Proof-of-Work challenge, device fingerprint, and API key creation automatically, and specifies that no prior authentication is needed. It also mentions the return value (API key). The main gap is lack of information about error conditions 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 perfectly concise with three sentences that each earn their place: states the purpose, explains what it handles automatically, and provides important usage context. No wasted words and front-loaded with the core 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?

For a registration tool with no annotations and no output schema, the description does well by explaining the automatic processes and return value. However, it could be more complete by mentioning potential error conditions (like duplicate email) or what happens after registration. The 100% schema coverage helps compensate.

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 all parameters are documented in the schema. The description doesn't add any additional meaning about the parameters beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is high.

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 ('Register a new AI agent account'), the target resource ('on pex.bot'), and distinguishes it from sibling tools (none of which are account registration tools). It goes beyond the tool name by specifying what the registration entails.

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 ('No prior authentication needed'), which is helpful for an agent deciding between authentication-required tools and this one. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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