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

With no annotations provided, the description carries the full burden. It discloses a significant behavioral trait: the operation atomically reserves task reward into escrow, which implies a state-changing, financial side effect. This is valuable context beyond the schema. It could add more (e.g., what happens if the task is already accepted, whether funds are locked until submission), but the core side effect is clearly stated.

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

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

Two short sentences with no filler. The primary action is front-loaded, and the critical escrow behavior is stated immediately after. Every word earns its place.

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

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

The description is adequate for a single-parameter tool with no output schema, but it leaves some gaps. It does not mention error conditions (e.g., task already accepted, insufficient balance), whether the operation is reversible, or what the return value indicates. Given the financial side effect, a bit more context would be helpful, but the core behavior is clear.

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 the single parameter (task_id). The description adds no additional meaning about the parameter beyond what the schema provides, which is acceptable given the high coverage. Baseline 3 is appropriate.

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

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

The description clearly states the action ('Accept an open task from the marketplace') and adds a key behavioral detail ('Atomically reserves task reward into escrow'). It distinguishes itself from siblings like approve_task and create_task, though it does not explicitly name them.

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: the tool is for accepting an open task, and the escrow detail suggests it is used when a worker commits to a task. However, it does not explicitly state when to use this tool versus alternatives like approve_task or discover_tasks, nor does it mention prerequisites such as having sufficient balance.

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 burden. It discloses a key behavioral effect (releasing escrowed AIC), which is important. However, it doesn't mention reversibility, permission requirements, or potential failure conditions. For a financial mutation, this is a moderate disclosure but not comprehensive.

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

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

A single sentence that front-loads the action and its consequence with no filler. Every word contributes 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?

For a single-parameter tool with no output schema, the description covers the core purpose and effect. It doesn't specify the return value or error scenarios, but given the simplicity and the clear workflow context, the missing details are minor and an agent can infer the expected behavior.

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

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

The schema already documents the only parameter 'task_id' with 'Task ID to approve', providing 100% coverage. The description does not add any additional parameter meaning beyond what the schema offers. Baseline 3 applies.

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

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

The description clearly states the action ('approves'), the resource ('worker submitted result'), and the effect ('releasing escrowed AIC'). It distinguishes itself from siblings like accept_task (worker accepting) and submit_result (worker submitting) by specifying the creator's role and the financial release.

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

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

The description implies the workflow stage: after a worker submits a result and before rating. It explicitly mentions 'Creator' and 'worker submitted result', which signals when to use it, though it doesn't explicitly list alternatives or exclusions. The context is clear enough for an agent to select it appropriately.

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

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

With no annotations, the description carries the burden of behavioral disclosure. The word 'check' strongly implies a read-only operation and 'authenticated agent' suggests required auth context, but it does not explicitly state that no state changes occur or describe return format/errors. Some useful context is added, but gaps remain.

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

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

A single, front-loaded sentence with no filler. Every word contributes to the tool's purpose, agent target, and balance scope.

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

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

The tool is simple: one required parameter, no enums, no output schema. The description covers the main function and agent scope. It could explicitly mention the return values (available vs. escrowed amounts), but the description is largely adequate for this simple call.

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 agent_id parameter is already fully documented. The description adds only a reference to 'authenticated agent', which does not meaningfully extend the schema's explanation. Baseline of 3 is appropriate.

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

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

The description uses a specific verb ('Check') with a clear resource ('available and escrowed AIC balances') and identifies the target ('your authenticated agent'). None of the sibling tools relate to balance checking, so it is easily distinguishable.

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 clearly indicates the tool's function—checking balances—so an agent can infer when to use it. There are no sibling balance tools, so exclusion guidance is unnecessary. It does not explicitly state alternatives or conditions, but the context is clear.

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

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

With no annotations, the description must carry the behavioral burden, and it does disclose the key side effect: the task is published to the market and carries an AIC reward. However, it does not mention whether the reward is deducted or escrowed, whether publication is immediate or subject to approval, or what happens after creation.

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 entire description is a single, front-loaded sentence with no filler words. Every phrase earns its place: the action, the marketplace, the reward currency, and the worker audience.

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

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

The description is enough for a simple create operation because the schema covers all parameters clearly. But without annotations or an output schema, the agent is not told what the tool returns, whether the task is immediately visible, or what constraints (e.g., sufficient balance or deadline validation) could cause failure.

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 baseline is 3 and the parameter meaning is already fully documented in the schema. The description adds only a small amount of context by connecting 'AIC reward' to the reward parameter and explaining the audience for the task, but it does not enrich individual parameter semantics beyond the schema.

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

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

The description uses a specific verb ('Publish') and a specific resource ('a new task to the AI Labor Market'), and it names the marketplace context and the reward mechanism. This clearly distinguishes it from siblings like discover_tasks, accept_task, and approve_task, which concern finding, taking, or approving rather than creating.

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?

Usage is only implied: 'Publish a new task' signals that this is the creation action, in contrast to discovery or acceptance siblings, but no explicit when-to-use guidance or alternatives are given. There are no exclusions or conditions stating, for example, that a task should not be created when an existing task already matches the need.

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?

There are no annotations, so the description carries the full burden. It discloses only that the tool searches/filters tasks and does not mention read-only behavior, authentication needs, pagination, rate limits, or result structure. It is not misleading, but it is thin.

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, front-loaded sentence that names the verb, resource, and key filters without filler. It is concise and easy to parse, though it could include a bit more contextual detail without becoming bloated.

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

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

For a simple search tool with three optional parameters, the description is adequate but not complete. It omits result-format expectations, pagination, permissions, and any note that only open tasks are returned by default, and there is no output schema to 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 the schema already documents all three parameters. The description adds minimal value by echoing 'capability' and 'minimum AIC reward,' but it does not clarify the status default or provide any new parameter context.

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 a clear action ('Search for open tasks') and resource ('AI Labor Market') and names two filter dimensions. It is distinguishable from siblings like get_task_details or create_task, though it does not explicitly name any alternative.

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

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

No guidance is given for when to use this tool versus alternatives such as get_market_stats or get_task_details. The intended use is only implied by the word 'Search' and the task-focused resource.

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

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

With no annotations, the description carries the full burden. It discloses the nature of the operation ('Fetch' – implying read-only), the real-time freshness, and the specific data returned (agents, open jobs, transacted AIC volume). It does not mention potential rate limits or failure modes, but for a simple fetch tool this is sufficient 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 a single, concise sentence that front-loads the purpose and includes the key metrics. Every word earns its place, with no filler or 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?

For a parameterless, read-only fetch with no output schema, the description fully specifies what the tool does and what data it returns. An agent can invoke it correctly with no further information. There are no gaps in prerequisites, side effects, or return format.

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 zero parameters, and schema coverage is trivially 100%. The baseline for 0 parameters is 4, and the description correctly avoids adding unnecessary parameter information. It contributes nothing extra, but none is required.

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 'Fetch' and the resource 'real-time aggregate economic metrics from the AI Labor Market', listing the specific metrics (agents, open jobs, transacted AIC volume). This is unambiguous and easily distinguished from siblings like get_task_details or check_balance, which focus on individual tasks or wallet balances.

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 market-level economic data is needed, but it does not explicitly state when to use it vs alternatives. There are no direct alternative tools for market stats among the siblings, so the intended context is implied rather than stated. No exclusions or when-not-to-use conditions are 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It does characterize the operation as a non-mutating 'Retrieve' and lists what data will be returned, but it does not mention error behavior, access requirements, or response structure, leaving meaningful 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 with no filler. It front-loads the verb and immediately enumerates the key return categories, though it could be slightly more precise about the distinction from market-level stats.

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

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

For a simple one-parameter read tool, the description adequately covers what the tool returns and that it targets a specific task. However, without an output schema or annotations, it stops short of describing failure modes or clarifying when a sibling tool would be more appropriate, so completeness is only moderate.

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

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

Schema description coverage is 100%, and the schema already documents task_id as a string with the 'tsk_...' pattern. The description adds little beyond implying that the task is identified by its ID, so the baseline of 3 is appropriate.

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

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

The description clearly states the action ('Retrieve') and the resource ('full task details... for a specific task'), plus enumerates the content areas returned. This distinguishes it from action-oriented siblings like approve_task, create_task, and accept_task, and from get_market_stats by focusing on individual task data.

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

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

The description implies usage when a specific task_id is known but provides no explicit guidance on when to choose this tool over discover_tasks or get_market_stats. It also omits exclusions or prerequisites, so an agent gets little directional help 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?

There are no annotations, so the description carries the behavioral burden. It discloses the main side effect—updating worker reputation—and implies a write action. However, it does not mention permissions, whether ratings are final or can be overwritten, or what the response contains, leaving notable behavioral 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 a single, front-loaded sentence with no filler. It communicates the action, timing, rating dimensions, and purpose efficiently, making every word earn its place.

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

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

For a six-parameter tool with no output schema and no annotations, the description is somewhat minimal. It provides the core context and the schema handles parameter definitions, but it omits important operational details such as whether sub-ratings are optional, response behavior, or any constraints beyond task completion, leaving an agent with moderate uncertainty.

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

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

Schema description coverage is 100%, so the schema already documents each parameter. The description adds context that the rating spans quality, accuracy, timeliness, and reliability, and that score is the overall rating, but it does not provide meaningful details beyond the schema's own property descriptions.

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

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

The description states a specific action ('rates worker'), a clear trigger ('after task completion'), and the rating dimensions ('quality, accuracy, timeliness, reliability') plus the underlying purpose ('update weighted anti-Sybil reputation'). This clearly distinguishes rate_worker from siblings like approve_task or submit_result, which involve different actions and resources.

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 usage context: the creator rates the worker after task completion. It does not explicitly name alternatives or say when not to use the tool, but the timing and role are unambiguous enough for an agent to select it appropriately 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.

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

With no annotations, the description carries the behavioral burden. It discloses that it grants capital and returns credentials, but does not mention side effects like whether multiple calls create multiple agents, any prerequisites, or reversibility. Some ambiguity remains about the operation's consequences.

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

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

Two concise sentences with no redundancy. The core action and key outputs are front-loaded, and every word earns its place.

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

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

The description covers the core purpose, the automatic capital grant, and the return of credentials, which is sufficient for a simple registration tool. It lacks explicit guidance on repeated calls or error handling, and without an output schema it only hints at the response shape, but it is largely complete for its simplicity.

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

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

Schema coverage is 100%, so each parameter is already documented with descriptions. The tool description adds no additional parameter-specific guidance beyond what the schema provides, so a baseline of 3 is appropriate.

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

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

The description clearly states the verb (register) and the resource (a new autonomous AI agent on the market), and adds the automatic capital grant and API credential return. No sibling tool performs registration, so it is easily distinguished.

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 phrase 'new autonomous AI agent' implies this is for first-time setup, and no sibling tool offers similar functionality, so the context is clear. However, it lacks explicit exclusions like 'do not call if you already have credentials' or guidance on idempotency.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the action claims escrowed payment, but does not explain whether the submission is final, whether payment is released immediately, whether resubmission is possible, or what consequences follow for the escrow.

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, front-loaded sentence with no filler. Every word contributes to explaining the action, its required context, and 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?

For a financially significant mutation with no annotations and no output schema, the description lacks critical context: what happens after submission, what response or error states to expect, and whether the action is one-time or reversible. The agent is left guessing about the outcome of a money-related 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?

While schema coverage is 100%, the description adds semantic value by framing task_id as an 'assigned task' and result as 'completed work payload.' This helps the agent understand the expected relationship and state of the parameters beyond the bare schema labels.

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

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

The description uses a specific verb, 'submit', with a clear resource, 'completed work payload', and states the goal, 'claim escrowed AIC payment.' This clearly distinguishes it from siblings like accept_task, approve_task, and rate_worker, which serve different workflow stages.

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?

It provides clear context for when to use the tool: after completing an assigned task and when claiming payment. It does not explicitly mention alternatives or exclusions, but the workflow stage is evident from the wording, so it is more than merely implied.

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