Open Uncensored

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

Annotations already declare readOnly=false, openWorld=true, idempotent=false, destructive=false, so the safety profile is covered. The description adds real value on top: it discloses the return payload (payment_url, session_id, claim_token) and that the purchase is completed by a human out-of-band via Stripe, which is behavior the annotations cannot convey. It does not mention session/token expiry or whether credits are granted automatically on claim.

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?

One compact sentence carries the action, the return values, the downstream sibling, and both parameter modes. Nothing is padded and the action plus output is front-loaded.

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

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

With no output schema, the description correctly shoulders the burden of describing what comes back, and it covers both calling modes for a 2-parameter tool. The only gap is post-payment semantics — whether the purchase must be claimed, and any lifetime on session_id/claim_token — which an agent orchestrating the flow would want.

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 would be 3, but the description adds the meaning of the omitted api_key case (create a new key) that the schema only implies with "Existing key to top up (optional)". That single clause materially changes how an agent will call the tool.

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 ("Start a credit purchase") and immediately describes the outcome (a Stripe payment_url plus session_id and claim_token). It also names the sibling it feeds into (check_purchase), so an agent can distinguish it from check_balance and the other siblings without opening a 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 branching rule — omit api_key for a new key, pass it to top up — and points at check_purchase as the follow-up call. It stops short of stating when not to use the tool or any preconditions around the human actually paying, so it is clear context rather than full when/when-not 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?

Beyond the annotations, the description discloses that the call returns the model and provider and charges the account in USD, which is important behavioral context for a paid, non-read-only operation. It still omits auth details and rate-limit behavior, but annotations already cover the safety profile.

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 with no wasted wording. It efficiently covers the action, the return values, and the billing consequence.

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 usefully explains the key return values: reply, answering model/provider, and USD cost. It is largely complete for a single-call chat tool, though it could clarify single-turn scope and model selection more explicitly.

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 input schema already documents model, system, api_key, message, and max_tokens. The description does not add parameter-level meaning beyond the schema, making the baseline 3 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 states a specific action: send one message to a model and return the reply plus model, provider, and cost. It is clear what the tool does, though it does not explicitly distinguish itself from siblings such as list_models or get_model.

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 no explicit when-to-use or when-not-to-use guidance and does not name alternatives. It implies this is for asking a model, but an agent gets no routing help versus list_models, get_model, or balance-related siblings.

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 this a safe, read-only, non-destructive operation, so the safety profile is covered. The description adds that the returned value is prepaid credit expressed in USD, which is useful. It does not disclose auth behavior (whether the optional api_key must be supplied or falls back to config) or whether querying affects anything.

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?

One short, front-loaded sentence fragment with zero filler. It could be tightened into a full clause ('Returns the remaining prepaid credit…') to remove the noun-phrase ambiguity, but nothing is 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?

For a trivial no-required-parameter read tool with no output schema, stating the returned quantity and its currency is nearly all an agent needs. Only the fallback/auth behavior for api_key is left entirely to the schema, which is acceptable here.

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?

There is a single optional parameter with 100% schema description coverage, so the schema already explains the api_key fallback. The description adds nothing about the parameter, which is the expected baseline when the schema does the heavy lifting.

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 the exact resource returned — remaining prepaid credit on the account, denominated in USD — which is specific enough for an agent to distinguish it from buy_credits and check_purchase. It lacks a verb (implicitly 'get/check'), but the resource is 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?

No guidance on when to call this versus siblings such as check_purchase or buy_credits, and no mention of what to do if the balance is low or zero. The agent must infer the use case from the name alone.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds a genuinely non-obvious behavioral fact: the api_key is returned exactly once, which explains the idempotentHint=false and warns the agent that a missed response cannot be recovered. It stops short of describing the unpaid/pending response.

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 tight sentences with zero filler; the source tool is named first and the success return is front-loaded.

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

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

There is no output schema, no annotation coverage of the pending-state behavior, and no parameter documentation, so the description is only marginally sufficient for a polling tool whose contract (what comes back when unpaid, whether retries are safe) matters 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 description coverage is 0% for two required parameters, so the description must carry the meaning of session_id and claim_token – yet it mentions neither. The exactly-once return implies the claim_token is single-use, but the agent must infer that rather than read it.

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 ('Poll') and resource ('a purchase'), names the sibling that produces the purchase (buy_credits), and says what is returned on success. An agent can distinguish this from buy_credits without opening either 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?

Usage is implied by 'Poll a purchase from buy_credits' – call it after starting a purchase – but there is no explicit guidance on polling cadence, when to stop, or what to do while the purchase is unpaid.

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

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

Annotations already declare read-only, idempotent, non-destructive, and closed-world behavior. The description adds useful topical scope, but does not state the return format, guide length or version, authentication requirements, or whether this is the canonical entry point. With annotations covering safety traits, a 3 is appropriate.

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

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

The description is a single front-loaded sentence naming the resource and its covered topics with no wasted wording. Every listed topic earns its place by helping the agent decide whether the guide contains what it needs.

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 zero-parameter read tool, the content list plus annotations provide enough to call it correctly and understand its scope. It falls short of 5 because it does not state the output form or explicitly frame the guide as the recommended starting point for onboarding.

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?

There are zero parameters, so there is no parameter meaning to add beyond the empty schema. The baseline for a no-parameter tool is 4, and the description does nothing to create parameter confusion.

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 the resource as the full guide to Open Uncensored for agents and enumerates the topics it covers, which distinguishes it from action-oriented siblings like buy_credits and chat. It stops short of an explicit retrieval verb such as 'read' or 'returns,' which keeps it from a 5.

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 topic list implies this is the onboarding/reference tool to consult before buying a key or calling the API, but the description never explicitly states when to use it, when not to use it, or how it relates to sibling tools. Usage is implied rather than directed.

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, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds the content shape (fields returned, example API call) but nothing about lookup failure behavior for an unknown id or whether results are cached/current.

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 that names the resource and then the payload; no filler, no repetition of the title.

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-param read with full annotation coverage and no output schema, listing the returned fields is exactly the right amount of detail. Only the unknown-id/error behavior is left unstated.

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?

Only one parameter and schema description coverage is 100%, with the schema supplying both the source ('from list_models') and an example id. The description adds no meaning beyond that, so the 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 and resource ('Details for one model id') and enumerates what comes back: price, context length, providers, score breakdown, example API call. It is clearly distinct from list_models, though it never names that sibling explicitly.

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 — 'one model id' suggests a follow-up to a listing call, and the schema's parameter note ('Model id from list_models') reinforces that, but the description itself gives no when-to-use or when-not-to-use guidance against list_models or chat.

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, openWorldHint=false and destructiveHint=false, so the safety profile is fully covered without the description's help. The description adds that the result spans every model and which dimensions are scored, but says nothing about result size, ordering, or refresh cadence.

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?

One front-loaded sentence that identifies the ranking and lists the dimensions, with no filler or repetition of the annotations.

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

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

For a no-argument read tool this is close to sufficient, and annotations cover the safety profile. However, with no output schema, the description does not indicate the shape of the response (a ranked list of models with per-dimension scores) or what terms like 'drift' and 'hard-line refusals' mean operationally.

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 the description carries no parameter burden and the baseline applies. Nothing in the schema needs compensating for.

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 resource (the OpenUncensored Score ranking of every model) and enumerates the scored dimensions, so the agent knows exactly what this returns. It does not explicitly distinguish itself from siblings get_model or list_models, which also concern models, leaving the boundary implicit.

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 when-to-use guidance, no prerequisites, and no named alternative. The agent can infer this is the tool for rankings, but it must guess how it differs from get_model and list_models rather than being told.

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

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

Annotations already declare read-only, idempotent, closed-world, and non-destructive behavior. The description adds useful context about the fields returned, but does not address pagination, result completeness, sorting, or whether all models are always included.

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 definition is a single well-structured sentence with the resource front-loaded. Every phrase contributes to describing what is returned, with no redundant wording.

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 listing tool with no output schema, the description adequately enumerates the key returned fields and annotations cover the safety profile. Minor gaps remain around pagination and ordering, but the definition is largely complete for this 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?

There are no input parameters, so the baseline is 4. The description does not need to explain parameter meanings.

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 that it returns every model with price per million tokens, context length, providers, and OpenUncensored Score rank. It distinguishes a bulk listing from a single-model lookup only implicitly through 'Every model,' but does not explicitly name siblings like get_model or get_rankings.

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 'Every model,' suggesting this is for broad model discovery rather than retrieving one model. However, the description gives no explicit guidance on when to use this versus get_model or get_rankings.

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 disclose the mutation profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false). The description adds one genuinely new behavioral fact — "A human reads every submission" — telling the agent there is no automated response. It does not say whether feedback is publicly visible, whether contact is required for a reply, or anything about rate limits.

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 short sentences, front-loaded with the action and recipient, with the reassuring "human reads every submission" placed last. Every clause carries information; nothing is 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 simple one-required-parameter submission tool with no output schema, this covers purpose, eligible content, and audience. The main gap is that it does not tell the agent what happens after sending (confirmation, ticket, follow-up) or whether contact is needed for a reply.

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 75% and the schema already explains model, contact, and message (including the 5000-char limit). The description's topic list loosely maps to the category enum values (bugs, docs, pricing, model requests) but adds no format or syntax detail beyond the schema, so the 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 gives a specific verb and recipient ("Tell the Open Uncensored team") and enumerates the exact subject matter it accepts — "missing models, bugs, pricing, docs, anything that got in your way." Nothing about it could be confused with siblings such as chat, get_guide, or buy_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 list of eligible topics implies when to reach for this tool, but it never states exclusions or alternatives (e.g., "use chat for immediate answers, use get_guide for documentation"). Usage is inferable rather than stated.

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