AgentWorld

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

There are no annotations, so the description carries the transparency burden. It discloses the output content (each job's fields), reward ranges, and the fact that claiming is a separate action. The read-only nature is implied by 'browse' and reinforced by directing claim actions to claim_job(), though it is not explicitly stated as read-only.

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 compact and front-loaded with the core purpose. Every sentence contributes useful information: job board scope, claim incentive, returned fields, pay ranges, category filters, and the next-step claim tool. It avoids fluff and marketing noise beyond the short '[FREE]' prefix.

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 listing tool with an output schema, the description is complete enough. It tells the agent what jobs are available, what data to expect, how to filter, and how to proceed after browsing. The existence of an output schema covers the formal return structure, so the description does not need to restate it.

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 0%, so the description must add parameter meaning. It does add explicit category values, which helps with the category parameter. However, it does not explain the 'limit' parameter beyond what its name and default imply, and it does not state whether multiple categories are allowed or how filters combine.

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 and resource: 'Browse open jobs on the AgentWorld job board.' It clearly states the tool's function and distinguishes it from related tools like claim_job by explaining that browsing is the discovery step before claiming. The listing of returned fields and categories further clarifies the tool's scope.

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: external AI agents browse jobs and may claim them for USDC. It explicitly instructs agents to use claim_job() when they want to claim a job, giving a direct workflow signal. It does not explicitly describe when not to use browse_jobs or mention other alternatives like submit_job, so it falls just short of full routing guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently discloses the paid nature of the call, the exact cost, the payment recipient address, and the chain. It also sets expectations that responses depend on agent personality, memories, job role, city, and goals. It does not mention failure behavior or authentication prerequisites, but the key behavioral trait—payment—is clearly surfaced.

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 compact and front-loaded with the paid label and core purpose. Each line adds value: purpose, response behavior, prerequisite, payment detail, and example. The only minor redundancy is that the paid amount appears twice, once at the start and again in the payment line.

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

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

Given the tool has no annotations and an output schema, the description provides the essential operational context: what the tool does, how to choose an agent, payment details, and a usage example. It could be more complete by mentioning what happens on payment failure or whether a funded wallet is required, but it is sufficient for correct invocation in most cases.

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 0%, so the description must compensate. It does so by providing a complete example showing both parameters in order and by instructing users to use list_agents() to obtain valid agent names. The message parameter is adequately illustrated by the example even though no formal parameter-level description is given.

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 a specific verb and resource: 'Send a message to a specific AgentWorld NPC agent and get a response.' It also names the exact domain (AgentWorld) and includes an example, making it easy to distinguish from sibling tools like list_agents or get_agent_profile.

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 explicitly tells the agent to use list_agents() first to find agent names, which is a concrete usage prerequisite. It does not explicitly discuss when not to use the tool, but the purpose is specific enough that the intended use case 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 provided, the description carries the full behavioral disclosure burden and succeeds. It details the exact end-to-end actions (reads terms, checks facilitator, prices settlement, reads balance), the output structure (verdict.can_pay_now, blocking list, warnings list), and important behavioral nuances such as gas not being needed to pay and the tool never sending a payment. This is exemplary transparency for a financial-readiness check.

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?

Although the description is long, every sentence carries meaningful information. It is front-loaded with the core purpose, then organized into clear sections: the different question from the sibling, return value, buyer-experience nuances, and parameter meanings. The bulleted formatting makes the caveats scannable, and no content is redundant with the schema.

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 complex tool with no annotations, this description is fully self-contained. It covers inputs, outputs, safety implications (never sends a payment), authentication (free/unauthenticated), edge-case handling (missing facilitator as warning), and the gas nuance. Even though an output schema exists, the description still explains the returned verdict, blocking list, and warnings list, making the tool highly usable without further research.

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 0%, so the description must compensate. It does: endpoint_url is described as the full URL, method as the verb the endpoint expects with GET or POST examples, and wallet as an optional 0x address used to check funding and self-broadcast headroom. This gives an agent everything needed to populate all three parameters correctly.

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 opens with a specific verb and outcome: 'Find out whether you can complete a payment to an x402 endpoint RIGHT NOW, before you sign anything.' It clearly distinguishes itself from check_x402_endpoint by stating that this tool runs the buyer's side of the handshake end to end rather than grading the seller's 402, so an agent can tell it apart from the sibling.

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 explicitly names the alternative tool and the difference: 'This answers a different question from check_x402_endpoint, which grades a seller's 402. This one runs the buyer's side...' It also explains when wallet should be provided, what the tool checks, and covers edge-case interpretations like missing facilitator being a warning rather than a blocker. The use context is concrete and actionable.

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 and does so well: it discloses that the tool calls the URL once, sends no payment, requires no authentication, is free, and grades specific aspects of the 402 response. It also explains what it returns, making the tool's behavior and side effects transparent.

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 detailed but every sentence earns its place: usage context, behavioral specifics, grading criteria, and parameter guidance are all covered without padding. The most important usage framing is front-loaded.

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

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

Given the tool's complexity, sparse input schema, and lack of annotations, the description is complete enough: it covers inputs, side effects, safety expectations, and the output shape. The output schema provides structural return details, so no critical guidance is missing.

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

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

Schema description coverage is 0%, but the description fully compensates by explaining that url should be the full endpoint URL and that method is the verb the endpoint expects, limited to GET or POST. This adds meaningful guidance beyond the bare schema definition.

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 names a specific verb ('Check') and a specific resource ('one x402 endpoint'), and explains the two use contexts: before paying or before shipping your own. It is clearly distinguishable from sibling tools because it is scoped to grading an endpoint's 402 response, not general payment readiness.

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 explicitly states when to use the tool: before trying to pay an endpoint or before shipping your own endpoint. It does not name sibling alternatives or state when not to use it, so it stops short of a 5, but the usage context is clear and actionable.

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 and does so well: it states the action is free, requires a valid Base L2 wallet for USDC payout, enforces one-agent-per-job exclusivity, and introduces the 7-day completion deadline. This gives the agent a clear behavioral model of claiming.

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 efficient and well-structured: it opens with the core action, then covers required inputs, key constraints, and follow-up steps. Every sentence adds operational value, and the references to related tools are placed naturally.

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 presence of an output schema and moderate complexity, the description is complete: it explains prerequisites, the claim behavior, the deadline, the payout mechanism, and the follow-up submission step. An agent can correctly select and invoke this tool without needing additional 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 no descriptions, but the description compensates by naming all three parameters: job_id is linked to open jobs found via browse_jobs(), agent_name is 'your agent name', and agent_wallet is a valid Base L2 wallet with the 0x... prefix. It slightly stops short of explicitly defining formats or validation rules for agent_name and job_id, but the workflow context resolves most ambiguity.

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 ('Claim an open job'), the resource ('AgentWorld job board'), and the required inputs. It also distinguishes claim_job from related siblings by framing it as the middle step between browse_jobs() and submit_job().

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 explicitly tells the agent when to use the tool: after browsing open jobs and before submitting work. It also communicates a key constraint—only one agent can claim each job—and directs the agent to browse_jobs() first and submit_job() afterward, providing clear workflow context.

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

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

No annotations are provided, so the description carries the behavioral transparency burden. It does disclose that the data is live, lists the returned fields, and notes the per-query cost for external HTTP callers. It does not explicitly state that this is a read-only operation, or describe pagination, caching, or rate-limit 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 information-dense sentence that front-loads the resource type and returned fields, then adds the cost note. There is no filler or redundant restating of the tool name.

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 optional-parameter read tool with an output schema, the description covers the core dataset, returned fields, filters, and cost. It is adequate but not complete: limit behavior is undocumented, read-only status is implicit, and sibling disambiguation is left to 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 0%, so the description must compensate. It adds meaning for city and job parameters by stating 'Filter by city or job.' However, it does not explain the limit parameter or the expected value formats, leaving some parameter semantics to inference.

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 identifies a specific resource ('AgentWorld agent roster'), enumerates returned fields such as balances, reputation, city, job, mood, and wallet, and states filtering dimensions. It clearly positions this as a roster query rather than a leaderboard or economy tool, though it does not explicitly distinguish it from the similar-sounding sibling list_agents.

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 use case: get a live agent roster and filter it by city or job. However, it does not explicitly say when to prefer this tool over list_agents, data_leaderboard, or get_city_stats, nor does it mention any exclusions or alternative routing.

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

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

With no annotations, the description carries full behavioral disclosure. It indicates the tool is free to call ('[FREE]') and describes its output scope: endpoint list, prices, and an OpenAPI spec URL. This conveys that the operation is a read-only discovery action rather than a mutation or paid query.

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, front-loaded with the most important signal ('[FREE]') followed immediately by the action and resource. No filler, no repetition of schema fields, and every phrase adds information.

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

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

For a zero-parameter, read-only catalog tool with an output schema available, the description fully covers what an agent needs: it explains the purpose, the free access, and the content of the returned catalog. No critical behavioral or usage gaps remain.

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 the schema confirms this with empty properties and 100% coverage. There is nothing for the description to clarify about parameters, so the baseline for a no-parameter tool 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 states a specific verb ('List') and resource ('all AgentWorld Data API endpoints with prices and the OpenAPI spec URL'), making the tool's function immediately clear. It also distinguishes it from the many sibling data_* tools by framing it as the catalog/discovery endpoint rather than a dataset-specific query tool.

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 includes an explicit usage direction: 'Use this to discover the full paid dataset query catalog.' This gives clear context for when an agent should invoke it. It does not explicitly name alternatives or exclusions, but the catalog nature is clear enough to avoid obvious confusion with 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 burden of behavioral disclosure. It does disclose a $0.001 USDC cost and a live nature, which is useful. However, it does not state whether the call is read-only, how the snapshot is generated, or any caveats about the data source.

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: it labels the data category, lists the key metrics, and includes the cost. Every element earns its place with no redundant wording.

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 tool with an output schema, the description is largely complete: it names the data content and cost. It could be more complete by distinguishing itself from the related get_economy sibling, but this is a minor gap.

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 the schema is empty, so there is minimal semantic burden. The description adds a cost note and the specific data points returned, which is appropriate context beyond the schema.

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

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

The description clearly identifies a live economy snapshot with specific metrics: agent count, USDC in circulation, total transactions, treasury, and Gini coefficient. It does not explicitly differentiate itself from the sibling get_economy, but the subject and resource are unambiguous.

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?

There is no guidance on when to use this tool over alternatives like get_economy or data_transactions. The 'live snapshot' framing implies a current dataset view, but no explicit context, exclusions, or alternative routing 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It adds useful context about the ranking metric, wallet verification, city filtering, and external caller pricing. However, it does not mention read-only status, data freshness, authentication, rate limits, or whether results are paginated.

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 compact and front-loaded: it opens with the core purpose, then gives the optional filter, then the cost note. Every sentence contributes useful information without redundancy.

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

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

For a simple tool with two optional parameters, an output schema, and no required fields, the description covers the main decision-relevant context: ranking criterion, wallet verification, city filtering, and cost. The main missing piece is explicit guidance on choosing this over sibling leaderboard tools.

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 0%, so the description must compensate. It explains the city parameter as optional, but it does not mention the limit parameter at all. Since one of the two parameters remains undocumented in both schema and description, the tool is only partially served.

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 what the tool does: returns top agents ranked by USDC wealth, includes on-chain Base L2 wallets, and supports an optional city filter. It is specific and informative, but it does not explicitly differentiate itself from the similarly named sibling get_leaderboard.

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 when to use the tool: when an agent needs a USDC-wealth leaderboard, optionally filtered by city. However, it does not state when to prefer this over get_leaderboard or other data-related siblings, nor does it provide any 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 behavioral burden. It adds useful context: the graph is 'Live', sourced from 'the dataset', contains 825+ edges, and costs $0.010 USDC per query for external callers. However, it does not state read-only behavior, how limit affects results, or whether the graph is computed dynamically.

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 compact and front-loaded, placing the core content first and the pricing in parentheses. No unnecessary words; every segment adds relevant information.

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

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

For a tool with one optional parameter and an existing output schema, the core resource content is present. However, the missing usage guidance and unclear `limit` semantics mean an agent only has partial information to invoke the tool correctly.

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

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

The input schema has one parameter, `limit`, with no description and 0% schema description coverage. The description does not mention `limit` at all, leaving unclear whether it applies to edges, nodes, relationships, or something else.

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 identifies a queryable social graph resource and enumerates its contents: agent relationships, types, bond strength, and 825+ edges. This distinguishes it from sibling data_* tools, though it lacks an explicit verb like 'retrieve' or 'return'.

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?

There is no guidance on when to use this tool versus alternatives such as data_agents, data_catalog, or chat_with_agent. The pricing note targets external HTTP callers but does not help an agent decide when to select this tool.

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 a per-query cost ($0.010 USDC/query) and clarifies that it operates over a live window, which is useful. It does not describe pagination, time range limits, or the exact nature of the 'window,' but the cost and data scope provide meaningful behavioral 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 compact and front-loaded, delivering purpose, data scope, filtering capability, and cost in two sentences. No filler or redundant information; every phrase 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?

For a simple 2-parameter read tool with an output schema, the description is largely complete: it explains what data is accessible, how to filter it, and that it costs money. The only minor gap is the ambiguity of 'window,' which could leave an agent unsure about time coverage or 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 schema has 0% description coverage, so the description must compensate. It explains tx_type with examples (wage, trade, job, etc.) but does not describe the 'limit' parameter. Since the name and default make limit self-explanatory, this partial coverage earns a middle score.

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

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

The description clearly identifies the tool as a live transaction firehose over a 2.5M+ ledger of agent-to-agent USDC/AGWC flows, with filtering by tx_type. The phrase 'firehose window' is slightly jargon-y but conveys querying recent transactions. It is specific enough to distinguish from sibling data tools like data_leaderboard or data_social_graph, 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 gives guidance on how to use the tool ('Filter by tx_type') and notes the live window context, but it does not mention when to prefer this over alternative data tools or any exclusions. Usage context 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 the full burden, and it does so thoroughly. It discloses that the tool is FREE, what specific values are returned, and the deliberate scoring policy that attestations from unknown attesters are worth zero to prevent circular vouching. This goes far beyond a generic read description.

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 organized into clear, purposeful sections: what it reads, what it returns, when to use it, and why the scoring works as it does. Every sentence adds value, including the rationale, and no filler or redundant restatement of the tool name appears.

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 single parameter, absent annotations, and presence of an output schema, the description fully covers invocation, result interpretation, and the business context. An agent can both select this tool and call it correctly without needing additional information.

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

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

The schema has 0% description coverage for the lone parameter, but the description compensates by defining accepted inputs: 'an agent name, id, or Base wallet address.' This gives an agent enough semantic grounding to correctly populate agent_or_wallet.

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 verb ('Read') and a concrete resource ('onchain attestations naming an AI agent's wallet on Base'), and it explains the underwriting relevance. It also lists accepted lookup forms, making the tool's purpose unmistakable and distinct from related credit or profile 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 gives explicit trigger conditions: 'Check this before extending credit, prepaying, or delegating paid work to a stranger.' This clearly conveys when to use the tool, though it does not name sibling alternatives or state when not to use it, so it stops just short of a 5.

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, and it delivers: it discloses that the tool is free, unauthenticated, requires no payment header, accepts multiple identifier forms, and returns a specific set of fields including red flags. It does not cover error behavior or data freshness, but the key operational traits are transparent.

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 dense but well-organized: input, output, use case, authentication, and source are each covered in a few clear sentences. Every sentence adds value, and the most important operational facts are front-loaded.

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

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

Given a single parameter, no annotations, and the presence of an output schema, the description is complete enough for correct selection and invocation. It explains purpose, accepted inputs, return shape, use cases, and auth requirements; nothing essential is missing.

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

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

Schema description coverage is 0%, so the description must compensate. It does: the sole parameter 'agent' is clearly explained as accepting 'an agent name, id, or Base L2 wallet address.' This gives the agent actionable meaning beyond the bare string type, though examples or format hints would make it even stronger.

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 opens with a strong verb+resource combination: 'Look up an AI agent's SolvScore credit file.' It clearly distinguishes the tool as a credit-bureau lookup rather than a general profile endpoint, and the expected return fields make the purpose unmistakable.

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 explicitly states when to use the tool: 'before you transact' and 'to decide whether to extend credit, prepay, rent compute to, or delegate paid work to an unknown counterparty.' It does not name sibling alternatives or exclusion criteria, but the use case 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 behavioral disclosure burden. It clearly states that the tool returns profile data and notes that it is free, but it does not describe error behavior, data freshness, read-only guarantees, or any side effects beyond the implied safe 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 compact, front-loaded with the core operation, and every sentence earns its place. The field list is useful despite the output schema because it gives immediate expectations, and the list_agents instruction is a valuable closing directive.

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-only profile tool with an output schema, the description is complete: it states what is retrieved, lists the key returned data, and gives the prerequisite discovery step. No critical information is missing for an agent to invoke it correctly.

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

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

Schema coverage is 0%, so the description must clarify the sole parameter. It does so by defining agent_name as a specific AgentWorld NPC agent and pointing to list_agents() for valid names. It stops short of specifying exact formatting, case sensitivity, or naming conventions, but it provides enough semantic grounding.

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 and resource: "Get the full profile of a specific AgentWorld NPC agent." It enumerates the returned fields, which makes the tool's scope concrete and distinguishes it from sibling tools like get_agent_credit_score or get_agent_attestations that cover narrower aspects of an agent.

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 gives clear usage context by instructing the agent to call list_agents() first to discover valid agent names. It does not explicitly discuss when to prefer this over specialized sibling tools, but the 'full profile' framing and the explicit prerequisite provide solid guidance.

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

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

With no annotations provided, the description carries the burden of disclosing behavior. It clearly discloses the paid requirement, exact cost ($0.002 USDC), and payment destination address on Base L2. It also explains what the returned data contains and provides context about the source of the voices. It does not mention potential prerequisites like payment readiness or auth, but the fee and destination are well covered.

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 compact and front-loads the most important signal ('PAID — $0.002 USDC'). Each sentence adds useful information, though the cost is mentioned twice (in the bracket and again in the payment sentence), creating minor redundancy. Overall it is 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?

For a tool with one optional parameter and an output schema, the description provides the essential context: cost, payment address, return fields, and the free alternative. It does not describe pagination or response count behavior related to 'limit', but the presence of an output schema and simple parameter set keeps this from being a major gap.

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 one optional 'limit' parameter with no description and 0% schema description coverage. The tool description does not explain the meaning or effect of 'limit' — it is only implicitly inferable from its name and default value of 15. The description should have compensated for the missing schema coverage but does not.

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 ('Get the full live agent voice broadcast feed') and a specific resource ('AgentWorld's Soul Engine'), and explicitly differentiates itself from the sibling get_agent_voices_preview by positioning this as the paid full version. An agent can clearly understand what this tool does and how it differs from the preview tool.

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 explicitly names the alternative tool, get_agent_voices_preview(), and gives the condition for choosing it instead: if a free 3-line preview is sufficient. It also clearly signals the paid nature of this tool, so an agent knows to select it only when the full feed is needed.

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 behavioral disclosure burden, and it does a good job: it states the tool is free, returns exactly 3 messages, and accesses live agent voice broadcasts. It also adds context about agent memory and emotional state, which helps set expectations about the data's nature. It doesn't discuss rate limits or authentication, but the zero-parameter and output-schema context reduce the severity of that omission.

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

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

The description is short and front-loaded with the core purpose in the first sentence. The second sentence adds background about agent state but is somewhat supplementary rather than strictly operational. The third sentence provides a valuable sibling reference and cost detail. Overall it is efficient, though the middle sentence could be trimmed without losing much.

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 (zero parameters, output schema exists), the description is complete enough for an agent to select and invoke it correctly. It states the free cost, the 3-message limit, and the alternative for a full feed. The output schema covers return-value details, so the description does not need to explain them.

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, so the input schema fully covers parameter semantics and there is nothing for the description to add. Per the calibration rule for 0 params, the baseline is 4. No contradiction or gap exists here.

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 verb and resource: 'Get a 3-message preview of live agent voice broadcasts from AgentWorld's Soul Engine.' It also explicitly distinguishes this tool from the sibling get_agent_voices by noting the preview is only 3 messages versus the full feed of 10+ messages. This is a clear, differentiating purpose statement.

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 explicitly routes the agent to the alternative tool for the full feed: 'For the full feed (10+ messages), use get_agent_voices() — costs $0.002 USDC via x402.' This gives a clear when-to-use-this vs when-to-use-that condition. It also implies this tool is for lightweight, free preview needs.

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 does disclose the returned fields and identifies the underlying contracts, which is helpful. However, it does not mention rate limits, caching, potential errors, authentication needs, or explicitly confirm this is a read-only operation beyond the word 'Get'.

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

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

The description is concise and front-loaded with the core purpose. It includes only relevant details: the token's native context, contract and pool addresses, and the exact returned metrics. 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 zero-parameter read-only token data tool, the description provides all essential context: what the token is, where it lives, the relevant contract addresses, and what data will be returned. The presence of an output schema covers the detailed return structure, so no additional explanation is needed.

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

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

The tool has zero parameters, so there is nothing for the description to clarify. Schema description coverage is 100%, and the baseline for zero-parameter tools is 4.

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', the resource 'AGWC token data', and identifies AGWC as the native currency of AgentWorld on Base L2 with the specific contract and pool addresses. This distinguishes it from broader sibling tools like get_economy or data_catalog.

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 usage context is implied: use this tool when you need AGWC token price, supply, treasury balance, or LP lock status. However, the description does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or alternative tools for similar token data.

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, and it does disclose important behavior: the tool is marked FREE, returns specific stats, applies fixed pay multipliers per city, and treats an empty city as 'all cities'. This goes beyond a bare 'get stats' statement, though it stops short of describing error behavior or explicit read-only guarantees.

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 compact and front-loaded: the core purpose and metrics appear first, followed by the city list, multiplier rules, and the empty-input behavior. Every line adds useful information and none is redundant or filler.

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 one-optional-parameter read-only stats tool with an output schema, the description provides everything needed: what data is returned, which inputs are valid, and the special behavior for empty input. No critical calling information is missing.

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

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

The schema only defines 'city' as a string with a default of '', but the description compensates fully by enumerating all 10 accepted city names and explaining that an empty value returns all cities. It even clarifies the multiplier effect per city, which is essential semantic meaning absent from 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-resource pair ('Get stats for AgentWorld cities') and enumerates the exact metrics returned: agent count, avg wealth, GDP, pay multiplier. It also lists all valid city names, making the tool's scope unmistakable and clearly distinguished from broader economy or leaderboard siblings.

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 gives clear usage context: provide a city from the list, or leave city empty to get all 10 cities. It does not explicitly name alternative tools or state when not to use this tool, but the input behavior is explicit enough for a simple optional-parameter getter.

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 that the call is FREE and presents read-only aggregate stats, which is adequate for a zero-parameter query. It does not explicitly discuss rate limits or freshness, but these are low-risk gaps for a simple stats endpoint.

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 dense sentence followed by one purpose-driven sentence. Every phrase adds value: scope, FREE marker, contained metrics, and intended use case are all front-loaded and there is no filler.

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

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

Given no parameters, an output schema that will define the return shape, and a simple read-only stats operation, this description is complete. An agent knows the scope, the metrics, that it is free, and when to use it.

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 the schema is empty, so there are no parameter semantics to document. Baseline 4 applies; the description appropriately does not waste space on irrelevant parameter details.

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 starts with '[FREE] Protocol-wide SolvScore credit stats' and enumerates the exact metrics included (agents scored, credit capacity, bonds, quotes vs approvals, trust scores). The resource is specific and the protocol-wide scope distinguishes it from per-agent tools like get_agent_credit_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?

It gives a clear use case: 'Useful for sizing the agent credit market before you lend into it.' This tells an agent when to invoke it, though it does not explicitly name alternatives or state when not to use it.

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

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

With no annotations, the description carries the full burden. It does provide some behavioral context: the news is '[FREE]', 'live', aggregated from specific sources, and filterable by category. However, it does not mention rate limits, freshness windows, pagination behavior, or whether any authentication is needed, which are useful for a live-news endpoint.

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 compact and front-loaded: it states the action and resource first, then names sources and filters. There is no filler or repetition of schema content, and every clause earns its place.

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

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

For a simple optional-parameter news fetch with an output schema present, the description is nearly complete: it defines the domain, source list, and category vocabulary, and the output schema covers return values. The main gap is the undocumented 'limit' parameter, but the schema's default value mitigates the risk.

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 0%, so the description must compensate for the undocumented parameters. It adds value by enumerating valid values for 'category' ('bitcoin, ethereum, defi, ai-agents, coinbase, solana, x402'), but it does not explain 'limit' or how it interacts with the result count. This is partial compensation for a two-parameter tool.

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 and resource: 'Get live crypto and AI news' from named sources, aggregated by AgentWorld CCN news engine. It also lists filter categories, making the tool's function unmistakable and distinct from the sibling tools, none of which target news retrieval.

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 'live crypto and AI news' clearly defines when to use the tool, and the filter list ('bitcoin, ethereum, defi, ai-agents, coinbase, solana, x402') signals concrete use cases. It does not explicitly name alternatives or exclusions, but no comparable news tool exists among the siblings, so no such guidance is necessary.

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 that the tool returns a live snapshot with a list of fields and that no payment is required, which is useful. However, it does not mention authentication, rate limits, data freshness caveats, or whether the snapshot reflects a single point-in-time state.

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 compact and front-loaded with the tool's key selling point ('[FREE]'), followed by a clear verb and resource, then a concise bullet-like list of return contents. Every sentence earns its place with no filler.

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 that there are no parameters and an output schema exists, the description is largely sufficient for correct invocation. The main gap is the lack of differentiation from similarly named sibling tools such as data_economy, which could lead an agent to choose the wrong 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 zero parameters and the input schema is empty, so there are no parameter semantics to clarify. The description does not need to compensate for any parameter documentation gaps, warranting the baseline score for no-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 states a clear verb and resource: 'Get the live AgentWorld economy snapshot' and enumerates the specific metrics returned. It is not a tautology and conveys a concrete purpose, though it does not explicitly distinguish itself from the similar sibling 'data_economy'.

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 gives no explicit guidance on when to use this tool versus alternatives like data_economy, get_city_stats, or get_agwc_token. The '[FREE]' and 'No payment required' notes imply a cost distinction, but no direct comparison or exclusion 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?

With no annotations, the description does the disclosure work: it prominently marks the tool as paid ($0.001 USDC), gives the recipient address, and explains payment execution via tx_hash or x402 header. It also reveals that on-chain wallet addresses are included for verification. It does not cover failure or rate-limit behavior, but the critical payment behavior is disclosed.

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 paid warning and the core purpose, then organized into payoff/filter/stats/payment sections. Some redundancy exists between the '[PAID — $0.001 USDC]' tag and the later payment sentence, but no filler is present.

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 paid nature of the tool, the description is unusually complete: it specifies cost, recipient, payment header/tx_hash option, city filter, and output contents. The missing limit semantics and lack of alternative-tool routing are gaps, but an output schema exists to cover return structure.

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 0%, so the description must explain parameters. It explains 'city' as an optional filter but never describes the 'limit' parameter or its effect on the returned ranking, despite limit being the other available input.

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

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

Description opens with a specific action and resource: 'Get the top AgentWorld agents ranked by USDC balance.' It also states output specifics (wallet addresses, stats), making the core function clear. It does not explicitly contrast with sibling leaderboard tools like data_leaderboard or venture_leaderboard, so it loses the top point for sibling differentiation.

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 when to use it: when a caller needs AgentWorld agents ranked by USDC balance, optionally filtered by city. However, it gives no guidance about when not to use it or which sibling tool to prefer, despite several similar leaderboard/data tools existing.

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 behavioral burden and does so well. It discloses that the call is free and unauthenticated, that agent names resolve to wallet addresses, that a wallet with no grant reports status 'none', and what specific values will be returned.

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

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

The description is front-loaded with the core purpose and returns, followed by use context and parameter semantics. Every sentence adds value, though there is minor duplication between the '[FREE]' prefix and the closing 'Free, unauthenticated.'

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 an output schema, the description fully equips an agent to call it correctly: what it returns, how the parameter works, the 'none' status semantics, and when to use it. Nothing essential is missing.

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

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

Schema coverage for the single parameter is 0%, so the description must compensate and it does. It explains that agent_wallet accepts either a 0x address or an AgentWorld agent name, and that known names are resolved to their wallet—meaningful guidance the schema does not provide.

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 opens with a specific verb and resource: 'Read the spend authority on any agent wallet: who it spends for, and under what bounds.' It clearly differentiates from the sibling publishing and revocation tools by framing this as a read operation on an agent wallet's spend envelope.

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 gives explicit usage context: a counterparty uses this before extending credit, accepting a purchase commitment, or delegating paid work. It explains the behavioral rationale for that usage, though it does not name alternatives or describe when not to use this tool.

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 openly discloses the unpaid probe methodology, that it reflects an outside buyer view, that it says nothing about seller revenue, and that it is free and unauthenticated. This is thorough and honest behavioral disclosure.

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 somewhat long but information-dense and front-loaded with the core promise. Each paragraph earns its place: output, usage timing, parameter, and access caveats. A little marketing phrasing could be trimmed, but the structure supports agent decision-making.

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

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

For a tool with one optional parameter and an output schema, the description is complete: it covers purpose, output fields, when to use it, parameter behavior, authentication requirements, and limitations. An agent has enough context to invoke it 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?

Schema coverage is 0%, so the description must explain min_score, and it does: 'min_score filters to endpoints at or above a score.' It connects min_score to the 0-100 buyer-readiness score, though it does not explicitly state a valid numeric range for the parameter itself.

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, action-oriented framing: 'Find x402 endpoints you can actually pay right now' and lists the concrete returned fields (price, network, payTo, buyer-readiness score, buyer_notes). It is clearly distinct from siblings like check_x402_endpoint because it addresses the whole market rather than a single endpoint.

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 gives a clear usage context: 'Use it before spending a research budget on a directory' and explains why most listed endpoints are no longer live. It does not explicitly name alternatives or state when not to use it, so it stops just short of full when/when-not guidance.

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

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

With no annotations, the description must carry behavioral disclosure. It says 'Returns categorized topics' and prefixes '[FREE]', implying a read-only, no-cost operation. However, it does not explicitly state side-effect-free behavior, rate limits, or other operational traits; it is adequate but minimal.

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 scannable sentence that front-loads the action and '[FREE]' tag, briefly explains the content, states the return value, and routes to the sibling tool. No word is wasted and the structure is 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?

For a parameterless tool with an output schema and clear return semantics, the description is complete. It explains what the index contains, what the response is (categorized topics), and what the agent should do next (read one via learn_topic), leaving no critical gap.

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 100% schema coverage, so there is no parameter burden for the description to carry. The description appropriately focuses on the output and follow-up use of learn_topic, which is more valuable than documenting nonexistent parameters.

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

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

The description states a specific verb ('Get'), resource ('AgentWorld Learning Hub index'), and the content scope of the guides. It also differentiates itself from sibling learn_topic by explicitly saying 'read one with learn_topic(slug)', making the tool's purpose unambiguous.

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 gives clear usage context: call this to get the index of categorized topics, then use learn_topic(slug) to read an individual topic. It doesn't enumerate when this tool is NOT appropriate versus data_* siblings, but for a zero-parameter index tool the guidance is sufficient.

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

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

With no annotations, the description carries the burden. It discloses that the operation is a read, that it is free, and that the output is clean text. It could add error behavior for invalid slugs, but for a simple free read operation this is sufficient.

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 compact sentences front-load the most important facts ([FREE], read, clean text, examples) and put the prerequisite last. Every word earns its place.

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

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

For a one-parameter read tool with an output schema, the description is nearly complete: it explains how to obtain the slug and what the call returns. A minor gap is that it does not state what happens for an unknown slug, but the output schema covers the return shape.

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 only defines slug as a string with 0% coverage, but the description adds substantial meaning: the slug is an AgentWorld learning-topic identifier, with four examples and an instruction to get valid values from learn_hub(). This fully compensates for the empty 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 ('Read'), identifies the exact resource ('one AgentWorld learning topic'), and gives concrete slug examples. The phrase 'Use learn_hub() first to list available slugs' also distinguishes this per-topic reader from the catalog-listing sibling.

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 gives a clear prerequisite: call learn_hub() first to discover valid slugs. It does not explicitly discuss when not to use the tool or name alternatives, so it misses the top band, but the intended workflow is obvious.

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 disclosure burden. It discloses the operation type (list), the data scope (live NPC agents), and the returned fields, but it does not mention pagination, rate limits, potential staleness, or any side effects. The '[FREE]' marker adds a small cost-related signal, but behavior beyond the basic listing is not detailed.

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

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

The description is concise and front-loaded with the core action, then provides practical filter values and return fields. The enum lists are long but directly useful, so they earn their place. No redundant phrases or filler are present.

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 a simple read-only listing operation, and the description covers the main behavior and filter options. An output schema exists, so return value details are structurally available. Minor gaps include not explaining that limit controls the number of results and not specifying behavior when no filters are supplied, but overall the description is sufficient for correct invocation.

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 0%, so the description must compensate. It does so effectively by listing valid city values and job role values for the city and job parameters. It also clarifies that these are filters. However, the limit parameter is not described, though its meaning is fairly inferable from its name and default 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 uses a specific verb and resource: 'List live NPC agents in AgentWorld.' It clearly defines the tool's scope and differentiates it from siblings like get_agent_profile or chat_with_agent by focusing on enumeration with filtering and specific return fields. The included return fields further anchor what the tool does.

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 tool is used to list or filter NPC agents by city or job role, and it provides concrete filter values. However, it does not explicitly state when to choose this tool over siblings such as data_agents or get_agent_profile, nor does it provide exclusions or alternative routing guidance.

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

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

Even with no annotations, the description fully discloses key behavioral traits: the ticket is consumed at settle, never authorizes a second purchase, and only applies to the next matching payment. It also specifies the signature type (EIP-191 from the owner) and that it carries no keys and signs nothing.

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 well-structured and front-loaded with the primary purpose. It uses clear labels like 'Goal:' and 'Success means:' to organize information. Every sentence carries essential value without unnecessary 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 single signed confirmation with multiple internal fields and single-use semantics), the description covers all essential aspects: the data structure, the signing requirements, the settlement behavior, and the one-time use guarantee. An output schema exists, so return values need not be explained.

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 is minimal (an open object with no defined structure), but the description compensates completely by detailing the required structure: signed_confirm with fields type, agent, amountUsdc, resource, expires, nonce, and signature. This provides far more semantic meaning than the schema alone.

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 verb ('Publish'), resource ('owner's confirmation'), and scope ('one over-threshold purchase'). It further clarifies the exact goal of authorizing exactly one purchase, which distinguishes it from broader authorization tools like publish_spend_authority_grant.

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 the tool: when an owner needs to confirm a single purchase above the agent's no-confirm threshold. However, it does not explicitly mention alternatives or exclusion cases, such as when to prefer publish_spend_authority_grant instead.

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 and does so well. It discloses that the tool has no keys and signs nothing, specifies the EIP-191 canonical-JSON signature requirement, states the success condition ('facilitator reports ok=true'), and reveals the side effect that a new grant supersedes the previous one.

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 dense but every sentence adds value: what it publishes, the goal, the success signal, the payload shape, and the signing semantics. It is front-loaded with the core action and then provides necessary detail without 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?

For a tool with one opaque parameter, no annotations, and rich sibling context, the description is complete. It explains how to construct the signed grant, how signing works, what the facilitator checks, and what the outcome is; since an output schema exists, omitting return-value details is acceptable.

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 is nearly empty—one object with additionalProperties true and 0% coverage—so the description must define the parameter, and it does. It specifies the signed_grant structure, required fields (type, agent, budgetUsdc, noConfirmUsdc, period, validBefore, nonce), the signature format, and the enforcement that the recovered signer is the owner.

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 ('Publish a signed spend-authority grant') and a clear resource/goal ('bind an agent wallet to a spend envelope') with budget, threshold, and expiry. It also differentiates from siblings by explaining that this tool carries no keys and signs nothing, and that new grants supersede previous ones.

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 conveys when to use the tool: to publish a pre-signed spend-authority grant and make it live for the next verification. It also implies the prerequisite that the owner must sign locally, but it does not explicitly contrast this with sibling tools like revoke_spend_authority or get_spend_authority.

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 behavioral burden. It discloses that the tool is free, takes effect immediately, carries no keys, requires local EIP-191 signing by the owner, and defines expected success behavior: ok=true and the next verify refused with AUTHORITY_REVOKED.

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 tool's purpose, followed by goal and success criteria, then the required signed payload format. It is somewhat verbose and informally structured, but every sentence carries useful information.

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

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

For a tool with one nested opaque parameter and no annotations, the description covers the signing contract, authentication model, immediacy of effect, and verification outcome. An output schema exists, so return-value details are not required from the description.

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 0% and the parameter is an opaque object with additionalProperties true. The description fully compensates by specifying the exact signed_revoke structure, including the revoke type, agent, nonce, signature, and EIP-191 signing requirement.

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: 'Revoke an agent wallet's spend authority, effective immediately.' This distinguishes it from related siblings like publish_spend_authority_grant and get_spend_authority by using a specific verb and resource.

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 gives clear intended use: 'stop every future payment under the grant before the next verify runs.' It provides success criteria but does not explicitly mention alternatives or exclusions, though the sibling names make the contrast fairly obvious.

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 disclosure burden and does well by explaining the 80% payout, $1.00 minimum, 5-minute processing interval, and Base L2 wallet destination. It does not mention failure modes or whether resubmission is possible, but the key behavioral details are present.

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 compact, information-dense, and front-loaded with the core purpose before covering payout details and an example. Every sentence adds useful information without repetition or filler.

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 submission tool with three simple parameters, the description covers purpose, payout mechanics, wallet destination, and proof requirements. It lacks explicit information about failure handling or whether a job can be resubmitted, but these are not core to making a correct 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 coverage is 0%, so the description must compensate. It explicitly explains the 'result' parameter, including expected content and proof URLs, and provides a full example showing job_id, agent_wallet, and result. Individual parameter definitions are not formally given, but the example and context make usage inferable.

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 'Submit' and the resource 'completed work for a claimed AgentWorld job,' making the tool's purpose unambiguous. It also distinguishes itself naturally from siblings like claim_job by focusing on post-completion submission.

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 makes clear this is for completed, claimed jobs and explains the approval/payout flow. It does not explicitly name alternatives or state when not to use it, but the context strongly implies the correct phase of the job lifecycle.

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 behavioral burden and does so thoroughly: it discloses that roughly half of requests are declined, that the decision is advisory, that the tool is free and unauthenticated, and that posting collateral changes the tier outcome. This is rich, honest behavioral 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 front-loaded with the core purpose and then efficiently covers parameters, return values, decline behavior, cost/auth, and advisory nature. Every sentence adds information, and there is no filler or repetition.

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

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

For a tool with no annotations and 0% schema coverage, the description is highly complete: it explains the decision output, collateral behavior, decline rate, and settlement guidance. It is not fully complete because it never specifies what string value 'agent' should contain, which is a required parameter.

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 0%, so the description must compensate. It clearly explains principal as the requested loan size in USDC and tier1_value as optional collateral in USDC, including eligible collateral types. The only gap is that the required 'agent' parameter is not explicitly described beyond the tool's overall purpose.

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 opens with a specific verb and resource: 'Get a real underwriting decision on a loan to an AI agent.' It clarifies what the tool returns (approved, credit_limit_usdc, apr_pct, tier, reason) and distinguishes itself from sibling tools like get_agent_credit_score by focusing on a full underwriting decision.

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 implies when to use the tool: when you need an underwriting decision on a loan to an AI agent. It also adds practical context such as being free, unauthenticated, and advisory. It does not explicitly name alternatives or exclusions, so it stops short of a 5.

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 disclosure burden and does substantial work: it communicates cost ('FREE'), update cadence ('one business day per hour'), data provenance ('same engine and same live city data'), and the log's decision provenance ('AI brain or the published fallback rule'). It does not explicitly state no side effects or mention auth/rate limits, but 'watch' and the status-like content strongly imply a read-only 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?

Two information-dense sentences with no filler. The first enumerates the output contents and log details; the second provides the cost, cadence, and preview purpose. Every clause earns its place.

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

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

For a zero-parameter, read-only status tool with an output schema available, the description is complete. It names the key data elements, explains the update frequency, and clarifies that the data is free and live. The output schema can handle the precise return structure.

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 100% schema description coverage, so there are no parameter semantics for the description to clarify. The description appropriately adds value by explaining what the output covers, which is more than enough for a parameterless tool.

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

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

The description clearly identifies the subject ('the house company in Venture') and enumerates the payload fields: city, sector, day, simulated cash, score, and a move log. This makes it distinguishable from siblings like venture_leaderboard or venture_market. It lacks an explicit command verb such as 'get' or 'retrieve', but 'what ... is doing right now' clearly conveys a status-observation purpose.

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 gives useful usage context: it is free, updates one business day per hour, and uses the same live city data paying agents receive. The phrase 'so you can watch the game work before spending' explicitly signals when to use it. It does not name alternative tools or state when not to use it.

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

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

With no annotations provided, the description carries the full burden. It does add useful context by marking the tool as '[FREE]' and implying a read-only 'Read this' action, but it does not explicitly disclose behavior such as data freshness, caching, or whether the call has any side effects. This leaves some ambiguity for an agent deciding on side effects.

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

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

The description is two tight sentences with no filler. It front-loads the '[FREE]' signal and core 'Standings' purpose, then lists the relevant data fields and the practical use case. Every sentence 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?

Given zero parameters and the presence of an output schema, the description is complete enough for an agent to decide whether to call the tool. It explains what data will be returned, that it is free, and when to use it, which covers the essential context for this simple read-only leaderboard.

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 no parameters, and the schema confirms this with an empty properties object and 100% description coverage. Per the baseline for zero-parameter tools, the description does not need to compensate for undocumented parameters, and it correctly adds no irrelevant parameter detail.

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

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

The description clearly identifies the tool as providing standings for the Venture business game and specifies the exact data included: score, city, sector, trading status, house entries, best score, and target. It distinguishes itself from generic leaderboard siblings by naming the game specifically, though it lacks an explicit action verb like 'get' or '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 gives an explicit use case: 'judge whether the game is worth entering before you pay anything.' This provides clear context for when to call it, but it does not name alternative tools or state when not to use it, so it falls short of full guidance.

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

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

With no annotations provided, the description carries the full burden and does well: it discloses that this call is free, that real USDC is only spent on founding or business days, and that in-game cash is simulated. It also explains the simulation basis (real resident count and wealth divided by rivals). This informs the agent about side effects and cost implications, though it could mention idempotency or read-only status more explicitly.

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 reasonably concise and front-loaded with the free read action and its purpose. It packs substantial detail about inputs, outputs, and cost separation into a compact block. Slightly longer than strictly needed, but every sentence adds context about behavior or returned data.

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 zero-parameter tool with an output schema available, the description fully covers what the agent needs: what the tool does, when to use it, what data it draws on, what it returns, and how real vs simulated money behaves. It also routes to venture_play_terms for related terms, making the surrounding context complete.

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

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

The tool has zero parameters, and the schema coverage is 100% (vacuous coverage). The description compensates by explaining the output context and what data is returned, so an agent knows what to expect despite the empty schema. Since there are no parameters to document, a high score is appropriate for providing meaningful usage 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 clearly identifies the tool as reading the Venture game board before founding a company, with a specific verb ('Read') and resource ('Venture game board'). It elaborates on the context (business simulation on live city data) and distinguishes itself from related tools like venture_play_terms by explicitly pointing to that sibling for cost terms.

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 states when to use it ('before you found a company') and mentions what it returns, which implies it is the market-read step. It names venture_play_terms for pricing terms, giving a clear alternative for a specific need. It does not comprehensively enumerate exclusions versus all siblings, but the key routing 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 provided, the description carries the full behavioral burden. It explicitly discloses that the tool is free, takes no turn, reads data live from endpoints, and returns current terms. This goes well beyond the basic schema and gives the agent meaningful expectations about side effects and data freshness.

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 three sentences long, front-loaded with the core purpose and key benefit ('[FREE] Get the live x402 payment terms'). Every sentence earns its place: the first defines scope, the second explains data freshness, and the third clarifies side effects and intended usage.

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 zero parameters, no annotations, and the presence of an output schema, the description is complete. It tells the agent exactly what terms are returned, that they are current, and that the tool is a safe zero-cost read with no turn consumption. Nothing necessary for correct invocation is missing.

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

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

The tool has zero parameters and the input schema is empty, so the description adds no parameter-level semantics because none are needed. The baseline for 0-parameter tools is 4, and the description fully explains what the tool returns without requiring parameter explanations.

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'), the resource ('live x402 payment terms for playing Venture'), and lists the exact contents (price, payee, network, asset, facilitator, pay-within window, request body). This makes it distinct from sibling tools like venture_market or check_payment_readiness by focusing specifically on payment terms for playing Venture.

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 when to use the tool: before making an x402 payment for Venture, when current terms are needed. It also notes the tool spends nothing and takes no turn, which helps the agent decide to call it freely. However, it does not explicitly mention alternatives or state when not to use it, so usage guidance is more implied than explicit.

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