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?

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral context by specifying that expected hierarchies are not included in the response and describing the per-item fields (bilingual scenario, question, reference norm, response format, weight). This 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 concise sentences: the first states the action and purpose, the second details the item composition, and the third notes the omission and workflow. No wasted words, and the most important information 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?

This is a simple read-only tool with one optional parameter and no output schema. The description fully covers what items are, what they contain, what is excluded, and how to proceed after fetching. It is complete 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 coverage is 100% and the single parameter packId is fully described in the schema with an example. The description merely references 'standards pack' but does not add syntax or format details beyond the schema. Thus a baseline 3 is appropriate.

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

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

The description uses the specific verb 'Fetch' with a clear resource ('public item set for a standards pack'), and immediately distinguishes this from siblings by linking it to AIO Trust Certification Tier 0 Baseline. It also clarifies the item structure and explicitly states what is not included, 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 provides clear context for when to use the tool: to obtain the scenarios for certification. It also gives workflow guidance by instructing to answer every item and submit via submit_eval. However, it does not explicitly contrast with sibling tools like get_bench_items, so it stops short of a full when/alternatives explanation.

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 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=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?

Beyond the annotations (readOnlyHint:false, destructiveHint:false, etc.), the description discloses that it 'writes a pending record to the public registry pipeline,' requires user confirmation, is free, is pinned to a model version, and is not a legal conformity assessment. 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 four sentences, each earning its place: purpose, importance, side effect/consent, and limitations. It is front-loaded with the core action and avoids 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?

The description thoroughly covers purpose, required registration data, side effects, and certification scope. With no output schema, it omits explicit return-value details, but this is a minor gap given the tool's straightforward nature and rich behavioral 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%, so baseline is 3. The description reinforces that modelName/modelVersion and operatorName/operatorEmail are required and adds semantic weight to modelVersion by stating 'Certification is pinned to a model version,' thus going slightly beyond schema descriptions.

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

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

The description begins with a clear, specific verb-resource pairing: 'Register a model for AIO Trust Certification, Tier 0 Baseline.' It explicitly distinguishes the tool from siblings like submit_bench_run and verify_certification by focusing on the registration action and the pending registry record.

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 contextual signals: registration is prerequisite for measurements to carry weight, and it explicitly instructs to 'ask the user before calling it.' While it doesn't name alternative tools directly, the context makes the appropriate use case unambiguous.

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?

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?

The description goes far beyond the annotations, disclosing deterministic scoring specifics (exact hierarchy match 1.0, adjacent code 0.5, weighted mean, pass at 0.7), public registry publication, Ed25519 certificate validity, and the limited scope of the certification. This is rich behavioral context with no contradiction to 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 core purpose, then methodically covers prerequisites, side effects, scoring, certificate properties, and limitations. Every sentence is information-dense and earns its place 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?

Given no output schema, the description compensates by clearly explaining the outcome: a certificate is issued, the result is published, scoring is deterministic, and verification is possible. It also covers prerequisites and the non-legal nature of the certification, making it complete for correct selection and 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?

The schema already provides 100% coverage with detailed descriptions and examples for all parameters, so the baseline is 3. The description adds broader behavioral context (registration required, publication, scoring) but does not add parameter-level syntax or format details 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 the tool's function: 'Submit answers to the Tier 0 public item set for automatic scoring.' It distinguishes this from sibling tools by focusing on Tier 0 certification and automatic scoring, and the title reinforces the certificate outcome.

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 strong context for use, including that registration is REQUIRED, the outcome is published, and the user must be asked before calling. It also points to verify_certification as the follow-up for verifying the certificate, though it does not explicitly contrast with submit_bench_run or other 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, idempotentHint, and destructiveHint. The description adds beyond that: it enumerates return components (certificate record, Ed25519 signature check, expiry/revocation status, canonical payload + public key) and discloses the behavioral semantics of a non-registry ID. This is exactly the kind of contextual value expected.

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 action and resource. The first sentence states purpose; the second details return values and the closed-world behavior. No filler or redundant restatement of annotations.

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-only tool with strong annotations, the description is fully complete: it explains the return payload, the verification components, and the interpretation of a missing ID. No output schema exists, so the description's listing of return fields is especially valuable.

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 describes certId format with 100% coverage, so baseline is 3. The description adds a concrete example and clarifies that an unknown ID means 'not issued by AIO', giving the parameter meaningful semantics beyond the schema's format hint. This earns 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 opens with a specific verb ('Verify'), names the resource ('AIO Trust Certification certificate'), and gives a concrete ID format example ('AIO-C0-7QP2K4MN'). It clearly distinguishes this from sibling register_for_certification (create vs. validate).

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: to confirm a certificate is valid and AIO-issued, especially given the closed-world note that absent IDs are definitively not issued. However, 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.