aio

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond those: there is no answer key, the measurement is about variable choice rather than correctness, and the item set is public with CC BY 4.0 licensing. This clarifies what the data actually represents, which the annotations do not convey.

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 composed of four tight sentences, each adding a distinct piece of information: the core item set and per-layer counts, the no-answer-key nature, included extras (template and rules), and the submission follow-up plus license. There is no redundancy or filler; it's optimally concise.

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 a single optional parameter and no output schema, the description is complete. It covers the content (forced-choice scenarios), structure (105 items per layer), absence of ground truth, included presentation template and submission rules, and the recommended next step (submit_bench_run). An agent has enough context to select and 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 100% coverage with a clear description for the layer parameter. The tool description enriches the enum values by mapping L4 to values, L3 to evidence, and L2 to sources, giving semantic meaning that the schema's plain enum lacks. This adds value beyond the structured 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 opens with 'Fetch the public forced-choice item set of the agent-submitted benchmark track', using a specific verb (fetch) and a well-defined resource (forced-choice item set). It clearly distinguishes this from siblings like get_eval_items by specifying the agent-submitted benchmark track, making its 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 establishes a clear usage context: it describes the data content and instructs the agent to 'Answer the items and submit them with submit_bench_run', tying it to a follow-up action. It also explains the optional layer parameter ('Omit for all 315') but does not explicitly contrast with alternative tools like get_eval_items, so it lacks direct exclusion 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?

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context beyond annotations by detailing the data contents (value L4, evidence L3, source L2 hierarchies, reliability figures) and the license (CC BY 4.0). This provides meaningful behavioral context without contradicting annotations.

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, using two sentences to convey the tool's output, parameter usage, and licensing. Every sentence adds value, with no redundancy or 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 has one optional parameter and no output schema, the description adequately communicates what data the user will receive (distributions, reliability figures, links) and how to control the model parameter. It could be more explicit about the return format, but it is sufficient for effective use.

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

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

Schema description coverage is 100%, so the parameter 'model' is fully documented. The description repeats the same instruction ('Omit "model" to get every measured model') as the schema, adding no new semantics. Baseline 3 is appropriate since the schema carries the complete parameter information.

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 returns judgment distributions from the AIO 20003 benchmark, with specific components (value/evidence/source win-rate hierarchies, reliability figures, links to raw JSON). It uses a specific verb and resource, and it distinguishes itself from sibling tools like get_bench_items by focusing on distributions rather than raw items.

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: it explains what the tool returns (per-model distributions) and how to control the model parameter ('Omit "model" to get every measured model'). However, it does not explicitly compare with alternatives or state when not to use this tool, so it lacks 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?

Goes well beyond the read-only annotations by disclosing item contents (bilingual scenario, question, provision, response format, weight), the absence of expected hierarchies, the implication that Gate A score is a floor, and the licensing. No contradiction with annotations.

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 primary purpose and uses every sentence to add distinct value (contents, exclusions, usage, limitations, license). Despite length, it is focused and non-redundant.

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?

With no output schema, the description fully explains the response contents, what is omitted, how it fits into the broader evaluation process, and its limitations. It also provides licensing and scope context, making it complete for an 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 coverage is 100% for the single optional parameter packId, which already includes an example and default. The description does not add additional parameter-level details, so baseline 3 is appropriate.

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

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

Clearly states a specific verb ('Fetch') and resource ('public item set for a standards pack'), and identifies the Gate A half of AIO Tier 0. It distinguishes itself from sibling start_eval_attempt by noting it's only for practice or Gate A alone.

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?

Explicitly says when to use it ('to practise or to score Gate A alone') and when not to use it for a signed report, directing users to the dual-gate flow with start_eval_attempt. Also clarifies the scope of what the items measure versus what they do not.

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 cover read-only, idempotent, non-destructive behavior. The description adds value by disclosing the specific content (e.g., 19 value codes, 10 evidence codes), the machine-readable nature, and the CC BY 4.0 license, which are not present in annotations.

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 sentences, front-loaded with the core purpose, then a dense list of included items and a direct usage instruction. Every phrase earns its place with no redundancy 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 simple read-only vocabulary retrieval tool, the description covers what is returned, why to use it, and licensing. The optional layer filtering is documented in the schema, so the description is complete enough without repeating 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 input schema fully documents the single optional 'layer' parameter with enum values and a clear description, providing 100% schema coverage. The description does not need to add parameter details; the use case for emitting/validating records implicitly contextualizes the parameter.

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 returns the machine-readable AIO Framework vocabulary, enumerating exactly what is included (value codes, evidence codes, source codes, context axes, record grammar, JSON Schema). This distinguishes it from sibling tools focused on items, benchmarks, or certifications.

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 says 'Use this to emit or validate AIO 20002 records,' giving a clear use case. It does not explicitly mention alternatives, but the phrasing plus the sibling set makes the context clear enough that this tool is for vocabulary/reference.

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 cover readOnly, idempotent, and non-destructive. The description adds return-value details (abstract, PDF URL, citation) and license (CC BY 4.0), providing useful context beyond annotations.

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

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

The description is a single concise sentence, front-loaded with the verb 'Fetch', includes an example and key outputs, with no unnecessary words.

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

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

With only one parameter and no output schema, the description covers purpose, id format, return contents, and license, making it fully sufficient for an agent 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 schema fully documents the only parameter 'id' with description and example. The description repeats the same example, adding no new semantic value 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 states it fetches one AIO paper by id, lists specific return contents (bilingual abstract, PDF URL, citation), and distinguishes it from siblings like list_papers which would list multiple papers.

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 use when a specific paper id is known and full details are needed, and provides clear context. It doesn't explicitly mention alternatives or when-not-to-use, but the scope 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?

Annotations already declare readOnly, idempotent, and non-destructive hints, so the safety profile is covered. The description adds valuable behavioral context: it returns all agenda items in full, the decision if recorded, and comment submission instructions. The CC BY 4.0 note also informs the agent about licensing constraints, which is not in annotations.

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

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

The description is two sentences, front-loaded with the core fetch behavior, then the usage guidance, and the license note. Every sentence earns its place with no redundancy 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 simple one-parameter, read-only tool with no output schema, the description is complete: it explains the input format, the exact return contents, the intended use case, and licensing. Given the low complexity and rich annotations, nothing important 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 100% and the schema already explains the 'id' parameter with the same example ('rfc-2026-001'). The description repeats this example but adds no new semantic information beyond what the schema provides, so the baseline score of 3 is appropriate.

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

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

The description uses the specific verb 'Fetch' and identifies the resource as 'one public RFC round by id', listing the exact contents (agenda items, reference documents, decision, comment instructions). This clearly distinguishes it from siblings like list_rfcs (which lists multiple) and get_paper (a different resource type).

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 explicit usage context: 'Use this before submit_rfc_comment so the comment answers an agenda item that is actually open.' This tells the agent when to use this tool relative to a sibling. It does not explicitly exclude alternatives like list_rfcs, but the context is clear enough for a single-item fetch.

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 read-only, idempotent, and non-destructive. The description adds meaningful behavioral context: the V/E/S mapping, version pinning semantics, certificate references, and the CC BY 4.0 license. While it could mention return format or error handling, it goes beyond what annotations provide.

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

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

Two concise sentences. First sentence states action and scope; second explains version pinning and its real-world implication. No filler or repetition of schema details.

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-pack fetch with two parameters, the description adequately covers the key output (V/E/S mapping) and the version-pinning behavior. It lacks explicit error/not-found handling but is otherwise complete given the tool's simplicity. The license note adds unexpected but useful 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?

Schema coverage is 100% with basic descriptions for both parameters. The description adds semantic value by explaining the relationship between id and version (certificates reference {id}@{version}) and that version is for pinning, which is not present in the schema 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?

Description states a specific verb ('Fetch') and resource ('one standards pack'), with the unique qualifier 'by id' and the distinguished content 'full per-provision V/E/S mapping'. This clearly differentiates it from sibling 'list_standards_packs' which would return multiple packs.

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?

Clear context: says to pass 'version' to pin a specific pack version and explains that certificates always reference {id}@{version}, which implies when version matters. Does not explicitly name alternatives or exclusion criteria, but the id-based fetch and version pinning give enough usage guidance for the intended use case.

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 readOnly, idempotent, and non-destructive hints. The description adds meaningful behavior beyond this: it specifies the exact return fields, that all papers are included, and licensing/citation requirements. This is useful context the annotations do not provide.

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

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

The description is two sentences, front-loads the core action and output, then adds licensing/citation detail. Every sentence contributes information without redundancy, making it highly efficient.

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 list tool with no output schema, the description fully specifies the return contents (id, track, year, bilingual title/abstract, PDF URL) and adds citation instructions. It is complete for the tool's purpose; no other behavioral expectations are 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 baseline is 4. The description does not need to add parameter semantics, and the schema coverage is 100% with an empty structure. No additional parameter info is required.

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

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

The description uses the specific verb 'List' with resource 'every paper published by AIO' and enumerates exact output fields (id, track, year, bilingual title/abstract, PDF URL), making the purpose unambiguous. The phrase 'every paper' distinguishes it from sibling get_paper, which presumably retrieves a single paper.

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

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

The description implies usage (when you need all papers) but does not explicitly state when to use this tool over alternatives like get_paper. There is no mention of exclusions or prerequisites, only an implicit 'use for complete list' 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description adds value beyond those by detailing the output structure ('status, comment window, what it is about, where to comment') and the domain rules (AIO Public RFC Process v1.0 with Draft ≥ 14 days, Candidate ≥ 30 days). This extra context is helpful and does not contradict the annotations.

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 well-structured: three sentences that front-load the primary purpose, then provide the entry contents and process/licensing details. There is no redundancy or filler; every sentence contributes meaningful 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?

Given the tool's simplicity (no parameters, no output schema), the description is complete: it states what the tool returns, what the entries contain, and the relevant process rules and license. It is fully sufficient for an agent to select and invoke the tool correctly, especially with annotations covering safety.

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 baseline score is 4. The description does not need to explain parameter semantics, and it does not repeat any schema information. The lack of parameters is fully compensated by the description's focus on output and 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 clearly states the tool's action and resource: 'List the AIO public RFC rounds'. It further explains what these rounds are and what each entry carries, distinguishing it from siblings like list_standards_packs and get_rfc. The verb 'List' is specific and the resource is well-defined.

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

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

The description implies usage: it lists open RFC rounds for public comment, but it does not explicitly state when to use it over alternatives, nor does it mention any exclusion criteria. Sibling tools such as get_rfc or submit_rfc_comment exist but are not referenced, leaving the usage context implicit 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, covering the safety profile. The description adds valuable behavioral context beyond annotations: the packs are versioned formalizations, and AIO certifies conformance only to its own formalization, not the issuing body's endorsement, plus the CC BY 4.0 license. This is meaningful extra 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 three sentences and front-loaded with the key action. The additional sentences about certification semantics and the CC BY 4.0 license are essential context, not filler — 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?

For a zero-parameter list operation with no output schema, the description provides sufficient context: it defines what standards packs are, highlights the important caveat about conformance being to AIO's own formalization rather than the issuing body's, and mentions the license. This is complete for the tool's simplicity.

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

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

The input schema has zero parameters, so the baseline is 4. The description adds no parameter-specific details, but none are needed with an 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 clearly states 'List the standards packs' with a specific verb and resource. It further defines what a standards pack is (versioned formalizations of external reference norms) and easily distinguishes from sibling get_standards_pack by focusing on the plural listing operation.

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 verb 'List' implies this tool is for enumerating all standards packs, and the explanation of what they are provides context. However, it doesn't explicitly mention when to use this tool over get_standards_pack or any other alternative, so guidance is only implied rather than stated.

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?

The description goes well beyond annotations. It discloses that the tool 'writes a pending record to the public registry pipeline' (a mutation, consistent with readOnlyHint=false), emphasizes the need to ask the user, and explains the nature of the result (signed score report, no verdict). This adds significant behavioral context beyond the generic annotations.

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 focused paragraph of three sentences. Every sentence carries essential information: the registration requirement, the pending-write behavior with user-consent instruction, and the non-certification caveats. There is 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 registration tool with no output schema and relatively low annotation richness, the description covers all critical aspects: what it does, why registration matters (registry weight), the pending nature, the user-consent requirement, what Tier 0 does/doesn't provide, and the scope of the score report. This is comprehensive for the tool's complexity.

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

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

Schema description coverage is 100%—every parameter has a detailed description. The tool description mentions 'model (name and version)' and 'operator (name and email),' but this only restates what the schema already conveys. It adds no extra parameter-level semantics, so the baseline score of 3 applies.

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

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

The description clearly states the action ('Register a model') and the target (AIO Tier 0 measurement), and it explicitly distinguishes this from certification with 'Tier 0 does not certify.' The title reinforces the purpose, and the description is specific enough to differentiate from sibling tools like verify_certification.

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: registration is required for a measurement to carry weight, and it instructs 'ask the user before calling it.' It implicitly tells you when not to use it (not for certification or legal conformity) but does not explicitly name alternative tools, so it stops short of fully explicit alternatives.

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, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds valuable behavioral details: it returns specific fields, notes the 'trimmed proxy' limitation, and states the CC0 license, which goes beyond the annotations 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 three short sentences, each earning its place: the first states the purpose and scope, the second lists return fields, and the third notes licensing. It is front-loaded with the action verb and contains zero 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?

With a simple 2-parameter schema, strong annotations, and no output schema, the description wisely includes the return field list and licensing, making the tool sufficiently complete for an agent. Minor gaps remain around pagination behavior and exact search syntax, but these are inferable from the parameters and schema.

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?

Both parameters (query and perPage) have thorough descriptions in the schema covering types, constraints, and defaults, so the schema already provides full semantic coverage. The tool description adds no additional parameter-specific meaning, so a baseline score of 3 is appropriate.

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

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

The description opens with the specific verb 'Search' and identifies the resource as the AIO Atlas, a trimmed proxy over OpenAlex focused on AI, governance, and societal effects. This clearly differentiates it from sibling tools like get_paper or list_papers by emphasizing a search-oriented operation.

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—whenever a free-text search over scholarly AI works is needed—by specifying the index scope and return fields. However, it does not explicitly name alternatives or state when not to use it, such as for retrieving a specific paper by ID, making the guidance implied rather than fully 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?

The description goes well beyond the annotations, disclosing the 24-hour expiry, exactly one submission, private rotating Gate B pool, opaque handles, and that the score report is issued under the fixed identity. It also states the provision label is withheld by design. These are critical behavioral traits not visible in readOnlyHint/idempotentHint/destructiveHint.

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 carries operational weight: issuance, handle mechanics, identity fixation, expiry, single submission, and score report uniqueness. It is front-loaded with the core action and then systematically covers important constraints 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?

Given six parameters and no output schema, the description fully compensates by explaining the return value (exam paper and attemptId), the lifecycle (24h expiry, one submission), and the consequence (signed score report). It covers prerequisites and next steps, making it complete for an agent to invoke 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 covers 100% of parameters, so the baseline is 3. The description adds meaning by emphasizing that model name/version and operator name/email are REQUIRED and fixed at this point, and that the score report is pinned to this identity. This provides value beyond the raw schema definitions, though it does not elaborate on packId or operatorUrl.

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 begins with a specific verb and resource: 'Start one AIO Tier 0 attempt and receive the exam paper', clearly distinguishing this from sibling tools like get_eval_items or submit_eval. It further explains the dual-gate nature and the opaque handle scheme, leaving no ambiguity about 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?

It provides explicit operational guidance: 'call submit_eval with the returned attemptId' and 'there is no other way to obtain one [score report]'. It also instructs to 'ask the user before calling it' because identity is fixed, which is a clear prerequisite. This effectively tells the agent when and how to use the tool versus alternatives.

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 only declare readOnlyHint=false, openWorldHint=true, and idempotentHint=false. The description goes well beyond: it explains that attribution is tied to the key, not declared fields; that submissions are stored as 'pending' with AIO review before publication; that published runs are labelled 'agent-submitted' and never merged with curated results; and that publication is self-reported, not certification. This rich behavioral context is vital for an action with 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 a compact paragraph but each sentence carries weight: purpose, prerequisite and attribution, completeness rule, processing and review, publication semantics, disclaimer, and a user-consent directive. It is front-loaded with the purpose and maintains a logical flow from invocation to post-submission behavior. No 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 (multi-layer submissions, key-based attribution, async review, publication semantics) and no output schema, the description covers all critical aspects: prerequisites, constraints, the review pipeline, the 'agent-submitted' label, the disclaimers, and the ask-user directive. The only minor omission is the exact response format, but the description sufficiently explains what happens to the submission.

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 the baseline is 3. The description adds meaning beyond the schema by clarifying the layer-completeness constraint (all 105 items in a layer must be present or the layer omitted) and the attribution semantics of apiKey (run attributed to the key's holder, not declared values). This supplements the schema's structural descriptions, though it doesn't detail each parameter's format beyond that.

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+resource: 'Submit answers to the agent-track item set from get_bench_items.' This clearly distinguishes it from sibling tools like submit_eval by naming the exact source item set. The title echoes and complements this, leaving no ambiguity about the tool's function.

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 the prerequisite (AIO agent key with bench:submit scope), references the source data (get_bench_items), and gives an explicit instruction to 'Ask the user before calling this.' It also specifies the layer-completeness rule. However, it does not explicitly contrast with alternatives like submit_eval, so the 'when-to-use vs alternatives' guidance is implicit 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?

Beyond annotations, the description discloses: the attempt is consumed; report is signed and published to a public registry; there is no pass threshold; scores must be compared only outside the margin; figures are provisional; conditions are self-declared and unverifiable. It also disclaims certification status. No contradiction with annotations.

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?

Though long (~600 words), it is well structured and mostly front-loaded with the essential flow. Some redundancy around the no-pass-threshold message ('no band is a pass', 'they gate nothing', 'is not certification') but these each nuance the point, so the length is defensible for the tool's complexity.

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

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

The description covers prerequisites, input semantics, output report contents, interpretation guidance (margin), side effects, and limitations (provisional, not certification). With no output schema, it fully describes the return payload. It also addresses edge cases like missing reference data and undeclared conditions, making it suitable for an agent to invoke correctly with minimal additional info.

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 description adds key semantics beyond the schema: explains how `answers` must interleave Gate A and Gate B ids, the opaque per-attempt handles, ignored ids, and zero-scoring for unanswered items; explains `conditions` are unverifiable and undeclared keys become null; clarifies `attemptId` is required for a report and params are cross-checked against the attempt. Given 100% schema coverage, this is substantial value-added.

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?

First sentence uses the specific verb 'Submit' with a concrete resource ('Tier 0 answers') and the title clarifies the deliverable ('score report'). It clearly distinguishes from siblings like start_eval_attempt and verify_certification by explaining the exact path to a score report and the consequence of omitting attemptId.

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?

Explicitly instructs to pass attemptId from start_eval_attempt and states this is 'the only path to a score report'. It warns about side effects (publication, attempt consumption) and says 'ask the user before calling this'. Also clarifies when Gate A-only scoring occurs and points to verify_certification for signature verification.

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?

The description discloses several key behaviors beyond annotation hints: comments become part of a public review record, email is stored but never published, comments are stored as 'pending' with review before publication, nothing is published automatically, and closed rounds reject comments. It also clearly states the write nature ('writes on the user's behalf') and the need for user consent, which adds significant transparency beyond the readOnlyHint=false annotation.

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 a clear purpose, then efficiently covers critical operational details (identity requirements, pending review, closed-round rejection, user consent) without redundancy. Every sentence contributes meaningful context, making it appropriately sized for the tool's complexity.

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

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

Despite having no output schema, the description fully covers the tool's workflow: prerequisites (open round, real name/email), processing (pending review), constraints (closed-round rejection), and user consent. It explains the publish behavior and email storage. This is complete for a submission tool of moderate complexity.

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 already provides 100% coverage for all parameters, including descriptions for each. The description adds value beyond schema by explaining the rationale for 'name' and 'email' (real identity required for attribution), the meaning of 'position' (support, object, comment), and the pending state of 'body'. This contextual information enhances understanding of how parameters are used, so a score above the baseline is warranted.

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 with a specific verb and resource: 'Submit a comment on an open AIO public RFC round.' It distinguishes itself from sibling tools (e.g., 'list_rfcs', 'get_rfc', 'submit_eval') by focusing on commenting on a specific RFC round, not just retrieving or submitting other data.

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

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

The description provides clear context for when to use the tool: 'open AIO public RFC round' and notes that 'a comment on a round whose window has closed is rejected.' It also instructs to 'ask the user before calling it,' which guides appropriate use. It does not explicitly name alternative tools, but since no sibling exists for this exact purpose, 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?

Annotations already cover read-only, idempotent, and non-destructive behavior, but the description adds rich behavioral context: return fields (record, documentType, Ed25519 signature check, outdated/withdrawn status, payload + public key), what a verified signature means, and what it does not mean for score reports. No contradiction.

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 but information-dense, with no filler. It flows logically from general purpose, to record types, to return fields, to interpretation. Every sentence adds necessary 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?

With no output schema, the description compensates by listing the return components and the meaning of verification. It also explains the two record types and the offline reproducibility scenario, leaving no significant gap for a read-only verification tool.

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

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

Schema coverage is 100% and the schema already explains the ID format. The description adds semantic value by explaining that both ID patterns verify here, and that an id not found in the registry was not issued by AIO, which is useful interpretive guidance 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 opens with a specific verb-resource pair: 'Verify an AIO registry record by id.' It further distinguishes two record types (score report vs legacy certificate) and explicitly notes that both are handled here, which sets it apart from sibling retrieval 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 clearly defines the scope: verification of registry records only, and clarifies that non-registry ids were not issued by AIO. Although it doesn't explicitly name alternative tools, the context makes it obvious when to use this tool versus simply retrieving items.

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