aio

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

Annotations already declare readOnly/idempotent/closed-world, and the description adds substantial context beyond them: the data provenance (private local directory), the caveat that declarations are not measurements and passed no AIO review, the fallback mechanism, and the selection_basis/is_fallback fields. This is exactly the extra behavioral context structured fields cannot 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?

Front-loaded with the core action and every sentence carries weight (caveat about non-measurement status, privacy note, fallback semantics are all needed). The first sentence is dense and parenthetical, hurting readability slightly, but there is no filler.

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

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

Despite having no output schema, the description enumerates the meaningful return fields (rule id, strength, declaration date, condition, aligned_with/departs_from, lookup_context.selection_basis, is_fallback) and warns that a fallback is not the organization's conclusion. For a 5-parameter tool with a nested object, nothing an agent needs to interpret results is missing.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: the condition axes determine matching and trigger a fallback when no declaration matches, and lookup_context.selection_basis reports which path was taken. That ties the nested condition object to observable output behavior rather than just restating 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?

States a specific verb and resource — reading one organization's own norm file to determine which of two AIO values it declared first — and scopes it clearly to private org data rather than public Commons data. It never names the sibling aio_lookup_reference as the public-data alternative, so sibling differentiation remains implicit rather than explicit.

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 is given about the data source (local private org norm files under AIO_ORG_NORMS_DIR, not public Commons data), which scopes when this tool applies. It also explains the fallback path, but offers no explicit when-not guidance or named alternative to steer the agent away from it.

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

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

Beyond the read-only/idempotent annotations, it discloses that every answer carries `superseded_by`, that a silent domain falls back to BASE and is flagged with `base_layer_hit` (excluded from headline counts), that the dataset has no expert review, and that `found: true` means only a code match exists. These are exactly the caveats an agent needs to avoid over-reading a result.

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?

Purpose is front-loaded in the first sentence and the density is high, with almost every clause carrying a caveat the agent needs. It is a single dense block rather than a structured list, and the trailing CC BY 4.0 note is marginal, but little is genuinely wasted.

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?

There is no output schema, so the description carries the burden of explaining the return value — and it does: direction, strength weight scale (3/2/1), scope (전칭/한정), grounding rule ids, citation, plus the lookup_context fields. Together with the BASE fallback rule, nothing essential for correct interpretation is missing.

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

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

Schema coverage is already 100%, so the baseline is 3; the description earns above baseline by clarifying that `domain` echoes the request while `lookup_context.answer_domain` shows where the answer came from, and that the `condition` axes are never actually recorded in v0.9. That is semantic value the schema cannot express.

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

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

The description states a concrete verb and resource: ask the kr-lnpd@0.9 reference dataset which of two AIO value codes Korean statutes rank first within a domain. It also scopes the source (statute-only, not court decisions), which implicitly separates it from the org-priority sibling. It stops short of naming `aio_lookup_org_priority` as the counterpart, so the differentiation is inferable rather than explicit.

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 tells the agent this is the legacy September 2026 statute-only edition and points to the current unified hierarchy with a concrete URL and data path, which is real routing guidance. It does not state a crisp 'use tool X instead when Y', so the agent must draw the conclusion itself.

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. 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 cover read-only/idempotent/non-destructive, yet the description adds substantial context: who sets status, that expertReview stays 'not_expert_reviewed' and canonicalDataChanged stays false, and that an unknown id and a wrong receipt produce the same not-found error. That is real behavioral disclosure beyond 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 core purpose leads, followed by status values, return contents, and stability/error semantics. It is somewhat dense with parenthetical detail, but every clause carries information rather than 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 no output schema, the description carries the return-value burden and does so: it enumerates the status set with reflected/not-reflected mapping, describes the reviewer response and history, and clarifies stable fields plus error behavior. Nothing needed to call or interpret it 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% – both parameters are documented there with their patterns and origin. The description only restates that the ids come from submit_contribution, adding no syntax or format detail beyond the schema, so baseline 3 applies.

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

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

States a specific verb (Check) and resource (one contribution's review status) and enumerates exactly what comes back: status, reviewer response, and history. It references submit_contribution as the source of the ids, distinguishing it from the sibling that creates contributions.

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?

Makes clear this is used with the contributionId and receiptToken produced by submit_contribution, implying the post-submission polling context. It does not name alternatives or explicit when-not conditions, but the context for use is 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, idempotentHint and non-destructive, so the safety profile is covered; the description reinforces it but also adds genuinely non-structured semantic caveats — the format example is 'not an actual finding', AIO's interpretation is 'not expert-reviewed', and there is 'no verdict that the model agrees or disagrees' with the court decision. It does not, however, say what happens on an unknown taskId.

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 purpose and read-only guarantee are front-loaded and each clause carries information (layer definitions, caveats). It is a dense single paragraph heavy on em-dashes and parentheticals, which costs a little readability but wastes no sentences.

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?

There is no output schema, so the description must describe the return shape — and it does, laying out the data-observation, interpretation, court-decision and comparison layers and what each contains. For a read-only single-fetch tool whose annotations carry the safety profile, nothing an agent needs 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 single parameter's description already gives the source list and a concrete example id, so the schema does the heavy lifting. The prose adds no format or validity detail beyond that; baseline 3 applies.

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

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

The description names a specific verb and resource ('Fetch one contribution task in full') and enumerates exactly what comes back: question, materials, format example, completion criteria, public scope, plus layered evidence. The word 'one' and 'in full' clearly distinguish it from the sibling list_contribution_tasks, which the parameter description also references.

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

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

Usage is only implied: 'Read-only: calling this never submits, registers, or sends anything' steers agents away from using it for submission, and the schema points at list_contribution_tasks for obtaining the id. But there is no explicit when-to-use statement, no statement of prerequisites, and no named alternative for the fetch-one 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?

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, non-destructive and closed-world, so the safety profile is covered. The description adds value beyond that by enumerating the return payload (full agenda items, reference documents, recorded decision, comment submission instructions) and noting the CC BY 4.0 license. It doesn't address error behavior for a missing or non-public id, which keeps it at 4.

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, zero waste: the fetch-and-return scope is front-loaded, and the workflow rule follows immediately. The license tag is terse and unobtrusive.

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?

There is no output schema, so the description carries the burden of explaining return contents — and it does, itemizing agenda items, reference documents, decision, and comment instructions. For a one-parameter read tool with full annotation coverage, nothing an agent needs 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 single id parameter is already documented with the same "rfc-2026-001" example. The description adds no format or validation detail beyond the schema, so the baseline 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?

States a specific verb and resource ("Fetch one public RFC by id") with a concrete id example, and the singular "one" plus "public" scoping clearly separates it from list_rfcs. An agent can distinguish it from siblings without opening the schema.

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?

Gives an explicit sequencing rule: use this before submit_rfc_comment so the comment targets an open agenda item. That is real when-to-use guidance tied to a named sibling. It does not cover the discovery case (use list_rfcs to find an id first) or any when-not-to-use case, so it falls short of a 5.

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

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world. The description adds useful context beyond annotations: that score reports reference {id}@{version} and that omitting version defaults to the most recent. It does not describe error behavior for unknown ids or response size, but the annotation coverage lowers the burden.

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?

Front-loaded with the action and resource, followed by the version hint and the reference format. The trailing 'CC BY 4.0.' is a licensing note of marginal relevance to invocation, slightly diluting conciseness.

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 two-parameter read tool with full schema coverage and clear annotations, the description covers purpose, version semantics, and version-reference format. Minor gaps are return shape (no output schema) and unknown-id behavior, but nothing critical for correct invocation.

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

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

Schema coverage is 100%, so both parameters are already documented. The description reinforces the semantics of version (pinning, default to latest) and the {id}@{version} reference format, which adds a little meaning beyond the schema but the schema does most of the work.

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?

Specific verb+resource: 'Fetch one standards pack by id' and states what payload it returns (full per-provision V/E/S mapping). This clearly distinguishes it from the sibling list_standards_packs and from lookup 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?

Usage is implied by 'by id' versus the sibling list_standards_packs, and the version-pinning guidance implies when to pass version. However, there's no explicit statement of when to prefer this over list_standards_packs or when not to use it.

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

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered; the description adds real value by spelling out that calling it 'never submits, registers, or sends anything' and by stating the optional-contribution and CC BY 4.0 licensing context. It does not disclose pagination or the meaning/limit of 'first' tasks.

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?

Purpose is front-loaded in the opening clause and the field enumeration is genuinely informative rather than filler. The second half is one dense run-on sentence and the trailing 'CC BY 4.0.' fragment is abrupt, costing some structure.

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 parameters, no output schema, and annotations fully covering safety/idempotency, the description supplies everything an agent needs: what the list contains, the fields per task, the project stage, and that the call is non-committal. The only minor omission is how many tasks 'first' returns.

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 takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies; the schema itself is empty with additionalProperties=false.

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

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

States a specific verb (List) and resource (contribution tasks of the AIO shared project on conditional value priorities), and even enumerates the per-task fields returned (id, type, question, status, observed cases). It is clearly distinct from the singular get_contribution_task sibling, though it never names that sibling explicitly and leaves 'the first' vague as to count.

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

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

It gives situational context ('the project is at an early, preparing-to-recruit stage; the tasks are published for review') and clarifies that contributing is optional, which implies when the list is relevant. However, there is no explicit when-not guidance and no routing to alternatives like get_contribution_task for a single task or submit_contribution to act on one.

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 cover the safe/idempotent/read-only profile, so the description's added value is real: it enumerates what each entry carries (status, comment window, subject, where to comment) and states the process deadlines of ≥14 days for Draft and ≥30 days for Candidate. It omits ordering, pagination, and whether the list is exhaustive, which are the remaining operational unknowns for this exchange.

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 purpose clause is front-loaded and the resource definition follows immediately; the entry-fields sentence earns its place by describing output. The process-version and 'CC BY 4.0' license tail is metadata of marginal selection value and slightly dilutes an otherwise tight definition.

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 parameters and no output schema, the description carries the return shape itself and does so adequately (status, window, subject, comment location), plus the window-length rules. It leaves the interaction loop underspecified — nothing tells the agent that commenting happens via `submit_rfc_comment` for an item obtained from this list.

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 takes zero parameters, so per the baseline there is nothing for the description to disambiguate. The schema is empty and no parameter explanation is required or offered, and none is missing.

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

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

States a specific verb (List) and a precisely characterized resource (AIO public RFCs, defined as public comment periods for contested standards-pack or methodology decisions). The definition of what an RFC is here meaningfully separates it from sibling resources like standards packs or papers. It stops short of naming `get_rfc` as the single-record counterpart, so differentiation is inferred rather than stated.

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?

Browsing intent is implied by 'List' and by the promise that each entry carries status and comment window, but there is no explicit when-to-use or when-not guidance. With both `get_rfc` and `submit_rfc_comment` as siblings, the description should say to use this to find an RFC and the others to read or comment on one; that routing is absent.

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 the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description earns credit for the substantive additions it does make: packs are AIO's own formalization, a Tier 0 measurement yields a score report rather than certification, and no issuing body endorses the result. That is real interpretive context an agent could not infer from structured fields; only return shape/pagination is left unstated.

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

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

Three sentences, front-loaded with the verb and resource, then the definitional and legal caveats. Nothing is redundant and every clause carries information an agent needs to interpret the results correctly.

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 parameters, no output schema, and annotations covering safety, the description supplies the conceptual model needed to call it (what a pack is, what it is not). It stops short of describing the fields each list entry returns, but the essential framing is present.

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?

Zero parameters, so there is nothing for the description to disambiguate — the baseline of 4 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?

Opens with a specific verb and resource ("List the standards packs") and immediately defines what a pack is — versioned formalizations of external norms into AIO Framework hierarchy values. An agent can distinguish it from get_standards_pack by the list-vs-get verb, though the description never names that sibling explicitly to sharpen the split.

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

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

Usage is only implied: the tool is obviously for discovery/browsing of available packs. The description gives no explicit "use this when..." guidance, no statement of when to prefer get_standards_pack for a single pack, and no prerequisites or 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?

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?

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?

Goes well beyond the annotations, disclosing that the contribution is stored as pending, nothing is published automatically, contactEmail is never published or returned, the receiptToken is shown only once, and that resubmission is rate-limited and duplicates rejected. This matches the non-idempotent, non-readOnly annotations rather than contradicting them. Slight gap: it does not spell out the review timeline or what happens to rejected contributions.

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?

Front-loads the core action and the safety constraint, and every clause carries information (privacy, consent, pending state, tokens, rate limits). It is a single dense block rather than a scannable structure, which costs a point but there is little 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?

No output schema exists, so the description supplies the return contract (contributionId plus a one-time receiptToken and the need to hand both to the user). Combined with the consent and privacy rules for a 10-parameter mutation tool, an agent has everything needed to call it safely and correctly.

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

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

Schema coverage is 100%, so baseline is 3, but the description adds real meaning: it explains the three contributorKind values, when agentName is required, that publicName may be a pseudonym shown after review, and that contactEmail is stored privately. It also ties the confirmation flags to a concrete honesty condition rather than restating their type.

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

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

States a specific verb (submit) and resource (one contribution to an AIO shared-project task) with clear scope. It is immediately distinguishable from siblings like get_contribution_status or list_contribution_tasks, which the description even names as consumers of its output.

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 states when to call ("ONLY when the user has asked you to submit"), when not to ("never upload the user's private conversations, documents, or model responses on your own"), and the condition under which the confirmation flags may be set. It also names the follow-up tool (get_contribution_status) and what it needs.

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

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

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?

Goes well beyond the annotations: annotations only say readOnlyHint=false and openWorldHint=true, while the description discloses the moderation lifecycle ('stored as pending'), that nothing publishes automatically, that email is stored but never published, that name/affiliation/position/body do get published, and that it acts on the user's behalf. That is exactly the context an agent needs before a public write.

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?

Front-loaded with the action and the two hard requirements, then the moderation and consent details. Slightly repetitive ('nothing is published automatically' largely re-says 'stored as pending — AIO reviews every comment before publishing'), but no sentence is 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?

No output schema exists, and the description compensates by explaining the resulting state (pending, moderated, never auto-published) and the failure mode (closed window). For a non-idempotent public write, nothing an agent needs in order to call it responsibly is missing.

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

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

Schema coverage is 100%, so every parameter is already documented, and much of the description's field talk (email stored never published, name as published) restates schema text. It does add one genuine signal — that name, affiliation, position, and body all become part of the published record — but that is marginal over the schema, so baseline 3 fits.

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

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

States a specific verb and resource ('Submit a comment on an open AIO public RFC') and immediately narrows scope to open RFCs, which separates it from get_rfc and list_rfcs in the sibling set. An agent can tell what this does without opening the schema.

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 gives preconditions ('Requires a real name and a working email address'), the blocking condition ('a comment on an RFC whose window has closed is rejected'), and a consent rule ('ask the user before calling it, and use their own words'). It even warns that every RFC published so far is closed, which is the decisive usage fact.

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.