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

No annotations are provided, so the description carries the behavioral burden. It implies a read-only operation by saying 'Check' but does not state whether the operation is side-effect-free, what it returns, or whether there are rate limits. For a simple balance check, this is acceptable but not fully transparent.

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

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

The description is terse and front-loaded with the core action. The second sentence adds practical usage guidance but repeats 'remaining,' making it slightly 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?

For a one-parameter tool with no output schema, the description provides a complete enough picture: what it checks, when to use it, and the required authentication context. Minor omissions are the exact return shape and a clearer read-only guarantee.

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

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

The input schema already fully documents the single parameter (api_key with 'Your API key') at 100% coverage. The description adds no additional parameter detail, meeting the baseline of 3.

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

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

The description clearly identifies the verb ('Check') and the resource ('remaining credit balance'), making the tool's function unambiguous and distinct from sibling tools like topup_credits or purchase_data.

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

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

It gives explicit context for when to use the tool ('Use after purchases'), which helps route an agent correctly. It does not explicitly list when not to use it or alternatives, but the API list makes the distinction reasonably clear.

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

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

With no annotations provided, the description bears the full disclosure burden. It reveals a rate-related behavior (free tier max 10 records per query) and the pathway for higher limits. But it does not disclose response format, error behavior, pagination quirks, or consequences of exceeding the tier quota.

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 short sentences: the first fronts the main purpose and mechanism, and the second provides the critical free-tier constraint and upgrade path. No words are wasted and the structure supports quick comprehension.

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 3-parameter tool with full schema coverage, the description supplies the main additional context an agent needs: the free-tier cap and how to get access to more records. The biggest gaps are not suggesting a preceding tool like list_datasets to find dataset names and not outlining the default return shape, but it remains broadly complete enough to call the tool.

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

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

The input schema already describes all three parameters, including the free-tier limit limit and the dataset name example. The description's mention of 'limit/offset' merely restates what the schema already covers, so it adds little semantic value beyond the structured details.

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

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

The description states the core operation clearly: 'Query records from a dataset' and identifies the pagination mechanism via limit/offset. This differentiates it from sibling tools such as list_datasets, which lists available datasets rather than records, making the 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 free-tier sentence gives explicit usage context: a query can return at most 10 records unless the user purchases products or tops up credits. However, it does not explicitly say when not to use this tool, nor does it point to a sibling like list_datasets when the dataset name is unknown.

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

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

No annotations are present, so the description must disclose behavioral expectations itself. The verb 'Get' implies read-only intent, but the description does not mention whether the operation is safe, cached, expensive, authenticated, or if it returns a single summary or a structured object.

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

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

The description is short and front-loaded, which is good for a trivial tool. However, it is under-specified rather than truly informative, so it is concise only in the sense of being short.

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?

Even though the tool has no parameters, it has no output schema and no annotations, and the description does not clarify what the output looks like, what 'databases' means, or when this tool should be preferred over sibling tools. An agent is left guessing.

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

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

The tool has zero parameters, so the input schema already fully describes the invocation contract. There is no meaningful parameter detail for the description to add.

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

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

The description identifies an operation ('get aggregate statistics') and a target ('databases'), but it is vague about what statistics are returned, from which databases, and in what scope. It does not distinguish the tool from siblings such as fetch_dataset or list_datasets.

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

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

There is no guidance on when to use this tool vs. the sibling tools, no exclusions, and no workflow context. An agent cannot tell whether this is the right tool for a user asking for a specific statistic or an overall dashboard summary.

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

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

No annotations are provided, so the description has the full burden of disclosing behavioral traits. It states an expected return value but does not explain what 'cryptographic provenance proof' contains, whether retrieval has side effects, whether credentials are needed, or how it behaves when the record exists in multiple datasets.

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

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

The description is a single, front-loaded sentence that names the action, the resource, and the scope with no filler words. Every word earns its place.

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

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

For a simple single-parameter tool, the description is minimally adequate, but it is not complete. There is no output schema and no explanation of the proof structure, potential errors, dataset selection semantics, or how this relates to sibling tools. The absence of annotations also leaves important safety and side-effect context uncovered.

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 record_id with a description, so schema description coverage is 100%. The description adds only a small contextual detail—'from current datasets'—which scopes the parameter but imposes no new format, constraints, or syntax beyond what the schema already provides.

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

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

The description states a specific verb ('Return') and resource ('cryptographic provenance proof for a record ID'), and scopes it to 'current datasets.' It is clear enough to convey the key operation, though it does not explicitly contrast with sibling tools like verify_footprint, whose purpose could overlap conceptually.

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

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

There is no explicit guidance on when to use this tool versus alternatives. The phrase 'current datasets' hints at a scope limitation, but the description never mentions verify_footprint, dataset-specific conditions, or any exclusion criteria, leaving usage decisions to inference.

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

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

No annotations are provided, so the description carries the transparency burden. It clearly conveys a read-only, non-destructive operation through 'List' and 'Free to browse.' It does not discuss rate limits or authentication, but for a zero-parameter catalog listing, it adequately signals the behavioral outcome.

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 action and output ('List all available datasets with record counts'), then adds a concise, useful list of dataset categories. Every clause earns its place—there is no filler or repetition of the tool name.

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

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

For a zero-parameter, read-only discovery tool with no output schema, the description covers the essential contract: list datasets, show record counts, and browse for free. It doesn't specify output formatting or explicitly route to siblings, but this is complete enough for a simple catalog listing tool.

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

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

The tool has 0 parameters, so the description has no parameter details to add. The baseline for 0-param tools is 4. The phrase 'List all available datasets' confirms there are no filters or inputs needed, which aligns with the empty input 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 states a specific verb and resource: 'List all available datasets with record counts.' This clearly distinguishes it from siblings like list_products (different resource), fetch_dataset (fetches a single dataset), and purchase_data (transactional). The added dataset-category examples reinforce the scope of the resource.

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

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

There is no explicit statement of when to use this tool versus alternatives. 'Free to browse' hints that this is a discovery/browsing endpoint before purchasing, but it does not say 'use fetch_dataset to get actual data' or 'use purchase_data to buy access.' Usage guidance is implied but not explicit.

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

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

With no annotations provided, the description carries the disclosure burden. It tells the agent this is a browsing/read operation, indicates the prerequisite of registration, and clarifies pricing units. It does not mention return shape or pagination, but those are less critical for a simple zero-parameter catalog listing.

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

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

The description is compact and front-loaded: the core action and pricing context are in the first sentence, while product categories and usage hint follow naturally. Every sentence contributes practical information without redundancy.

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

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

For a simple, parameter-free list tool, the description adequately conveys scope, pricing, included product families, and when it should be called. It could be slightly more explicit about returning product IDs for purchase_data, but the overall usage context is sufficient.

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

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

The tool has zero parameters, so the schema fully covers all inputs by construction. The description adds no parameter-specific detail because none is needed; per the 0-parameter baseline this 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 clearly specifies the verb 'Browse' and the resource 'all purchasable products with prices in credits', making it easy to distinguish from dataset-listing siblings. It also adds concrete catalog categories, giving the agent a practical picture of what the tool returns.

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

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

The description gives clear operational context: call after register_agent to see what to buy. It does not explicitly name alternatives or exclusions, but for a top-level listing tool the intended position in the workflow 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?

The description discloses the key behavior—credits are spent and an api_key is required—so the agent knows this is a purchase/billing action. However, with no annotations provided, it does not disclose failure modes, irreversibility, or what happens after a successful purchase beyond the immediate action.

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 only two sentences long, starts with the core action, and packs product context and api_key guidance without filler. It is easy to scan and no sentence is redundant with the schema.

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

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

For a two-parameter tool with 100% schema coverage and no output schema, the description is largely sufficient: it names the products, indicates the purchase action, and points the agent to the proper api_key source. A small gap is that it does not explicitly direct the agent to list_products for a definitive product catalog, but the sibling context makes this recoverable.

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

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

The input schema already describes both parameters and gives example values. The description adds useful semantic context beyond the schema by listing the categories of purchasable products (for example GPU compute, MCP unlimited, and DAG manifold tiers), which helps the agent reason about valid product_key values.

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

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

The description states a clear user action ('Buy a product with your credits') and specifies the resource type, then enumerates supporting product categories keyed to product_key values. This clearly distinguishes the tool from siblings like register_agent, list_products, and topup_credits.

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 context is implied rather than explicit: you purchase a product using credits, and you need an api_key from register_agent. There is no explicit instruction about when not to use it, such as pointing to list_products for browsing products or topup_credits for adding credits.

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

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

No annotations are provided, so the description carries full behavioral disclosure burden. It meets that burden by stating concrete side effects: immediate credit issuance ($5), no payment instrument needed, no human review, and immediate usability across specific categories (dataset queries, GPU inference, DAG manifold, DePIN/API access). It does not disclose behavior on duplicate registration (idempotency), which keeps it short of 5.

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 critical information is front-loaded: the opening sentence tells the agent what to do and what it gets. The remaining sentences are compact and each adds behavioral context (no card, immediate credit usability, next call). It is a single readable block with no wasted filler, although phrases like 'No credit card, no human, no Stripe required' are slightly redundant with one another.

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 call with 2 plain string parameters, no output schema, and no annotations, the description tells the agent everything needed to call it correctly: the purpose, the result (key + credits), the effect (immediate credit availability), and the follow-up step. The only unstated detail is the concrete shape of the returned API key or proof of registration, which is a minor gap for this low-complexity onboarding path.

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 baseline is 3; both parameters are already self-explanatory in the schema (contact email for delivery/top-up notifications; agent name). The prose adds no format caveats, uniqueness constraints, or delivery details beyond what the schema already states. It does not introduce any additional semantics.

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 the imperative 'START HERE' and a clear action-resource-outcome statement: 'Self-register as an AI agent to get $5 free credits and an API key.' It is semantically distinct from all siblings (list_products, purchase_data, topup_credits, check_balance, dataset and verification tools), so an agent can confidently classify it as the onboarding step 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?

Provides an explicit sequence of use: 'Call this first, then call list_products to see what you can buy.' It communicates that registration precedes all other actions and names the immediate next step. However, it does not address the re-run case (e.g., if the agent is already registered, use check_balance/topup_credits instead), so it lacks a when-not exclusion.

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

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

No annotations are provided, so the description carries the disclosure burden. It explains the two method behaviors (async USDC feed, Stripe checkout for a human to complete) but doesn't say whether there are reversibility, confirmation, or latency implications. The disclosure is useful but incomplete for a mutation/payment tool.

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 total, front-loaded with the core purpose, and every clause adds value. There is no fluff or repetition of schema fields. The alternative payment flows are packed concisely.

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?

Covers when to use and how to use the main payment paths, but no output schema or annotations to fill for the return value of the USDC path. The description doesn't state what is returned when reporting payment with tx_hash, nor an explicit guard that Stripe requires a human step. These gaps make a few details left to guess.

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?

Param schema covers all names and types (100% coverage), but the description adds meaning by linking 'tx_hash' to the USDC method and clarifying that Stripe returns a checkout link for a human. This provides conditional logic between method and tx_hash 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 a specific action and resource: 'Add credits when your $5 free balance runs out.' It clearly distinguishes this from sibling tools like purchase_data or check_balance by framing it as a balance top-up operation. The verb and subject are unambiguous.

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

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

Explicitly states when to use: 'when your $5 free balance runs out.' It also explains payment path choices: use USDC with a tx_hash or Stripe with a checkout link. It does not name an alternative tool but gives condition for invocation that is sufficient.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It says only that a SHA-256 verification is returned, without mentioning whether this is a purely read-only lookup, how the verification is computed, whether it requires credits, or what happens if the record or dataset does not exist.

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?

A single front-loaded sentence with no filler or repetition of the schema. Every word contributes to the definition, making it easy to scan and understand.

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 two simple parameters, the input side is well covered by the schema. However, with no output schema or annotations, the description leaves the exact return shape, error behavior, and expected output format unstated, and it does not place the tool in context relative to related verification tools.

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

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

Input schema coverage is 100%, so both dataset and record_id already have basic descriptions. The tool description adds the SHA-256 context but does not extend parameter semantics beyond what the schema already provides, 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?

States a specific verb ('Return') and object ('SHA-256 cryptographic verification'), making clear it produces a hash rather than mutating data. The phrase 'for a dataset or record' ties directly to the two input parameters and distinguishes it from siblings like fetch_dataset or get_zk_provenance.

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

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

There is no guidance about when to use this tool versus alternatives such as get_zk_provenance or fetch_dataset. The description implies a verification use case, but an agent would have to infer the decision criteria from the tool name rather than the description.

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