agiscorecard-web3-incentives

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

Annotations already cover read-only, idempotent and closed-world behavior, so the bar is low; the description still adds payload character ('methodology, limits, source links, fictional worked examples'), a privacy note ('no user records'), and a citation obligation on the returned canonical URL. It does not describe failure behavior for unknown IDs.

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?

Three compact sentences, front-loaded with the retrieval action, then payload contents and constraints. Every clause carries information 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?

For a single-parameter read tool with a full output schema and rich annotations, the description supplies all that is missing: parameter provenance, content type, privacy boundary, and a post-call action. An agent has everything needed to call and use 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 description coverage is 0%, so the description must carry the load, and it supplies the key semantic: the id is the identifier returned by search rather than an arbitrary string. It never explains the only legal enum value ('incentives') or what that reference covers.

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?

States a specific verb and resource ('Retrieve a public tool reference') and scopes it to the ID produced by search, which separates it from the search sibling. The wording 'public tool reference' is slightly looser than the title's 'Web3 method,' leaving a small gap in exactly what is being fetched.

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 precondition: the ID must come 'from search,' which routes the agent through the correct sibling first. It does not state exclusions or what to do when no ID matches, so it falls short of an explicit when/when-not pair.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds value by stating 'Parameters are processed remotely without application persistence,' clarifying no state is stored. It also explicitly notes the worksheet does not establish Sybil resistance or distribute tokens, which are important limitations not captured by annotations. This goes beyond what annotations provide, so a 4 is appropriate.

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 with a clear lead sentence stating the core function, followed by limitations and usage hints. It is moderately concise, though the list of exclusions ('No live subnet data, fraud detection, Bittensor emission replication, staking recommendation or token distribution') is a bit redundant. Overall, it is efficient and 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?

For a calculation tool with an output schema, the description adequately covers purpose, limitations, and input sourcing. It mentions how to obtain exact inputs (via fetch or example resource) and clarifies it is a worksheet without persistence. While it does not detail output structure, that is covered by the output schema. It lacks explicit when-to-use guidance, but given the exclusions and clear scope, it is reasonably complete.

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

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

Schema coverage is 100%, so all parameters are described. The description adds relational meaning by explaining that customerRevenue and tokenSubsidy are separated, and that budget is allocated using 'accepted work and quality weights'—tying to contributors' acceptedTasks and quality fields. It also mentions 'Contributor caps' (capPercent) and 'exact budget conservation,' which clarifies how budget is handled. This exceeds the literal schema descriptions, warranting a 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 tool's function: it separates customer revenue from token subsidy and allocates a declared reward budget based on accepted work and quality weights. It also lists specific exclusions (no Sybil resistance, no token distribution) that distinguish it from sibling tools like those handling live subnet data or fraud detection. The purpose is unambiguous and the resource (Incentive Lab) is named.

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 implicit usage guidance through exclusions (e.g., 'No live subnet data, fraud detection, Bittensor emission replication') but does not explicitly state when to use this tool versus alternatives. It mentions retrieving inputs via fetch or reading an example resource, but this is about input sourcing, not tool selection. There is no explicit 'use this when...' guidance, so the guidance is implied but not fully 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?

Annotations already establish read-only, idempotent, non-destructive behavior, and the description adds valuable behavioral context: bilingual (English and Chinese) name support, public scope, and the empty-query listing behavior. These details go beyond what annotations or schema provide without contradicting them.

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 clean sentences with no filler. The primary action is front-loaded, and the supporting usage details are kept minimal and relevant.

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 search tool with annotations and an output schema, the description is complete: it defines the search scope, supported query forms, and the empty-query behavior. No essential information for invoking the tool correctly 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 is 0%, so the description carries the burden of explaining the query parameter. It clarifies that queries are names, that English and Chinese are accepted, and that an empty query has special meaning. It does not fully specify matching behavior (e.g., exact vs partial), but it compensates well for the schema gap.

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 ('Search') and resource ('this server’s public AI/Web3 tools and citation pages'), making the tool’s function immediately clear. It is distinct from all sibling tools, which focus on checking permissions, comparing costs, or reading profiles rather than searching a corpus. The title and description align well.

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

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

The description clearly indicates when to use the tool: when searching for AI/Web3 tools or citation pages on this server. The note about empty query listing available worksheets provides a concrete usage scenario. It does not explicitly name alternatives or exclusion conditions, so it misses the top score.

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