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

Adds genuinely non-obvious context beyond the annotations: zero-data-retention routing and that Obscurify.ai does not store prompts, plus the default model and its cost/speed tradeoff. Annotations already declare openWorldHint=true and destructiveHint=false, and the description is consistent with both. It stops short of covering latency, per-call billing, or what the reply payload looks like.

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?

Four sentences, front-loaded with the action and then the routing guidance; nothing is buried. Slight redundancy in restating the default model that the schema already carries, which keeps it from being maximally tight.

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 what an agent needs to call this correctly: single-turn scope, privacy posture, default model, and where to go for alternatives. With no output schema, the omission of return-shape detail (token usage, finish reason) is a minor gap rather than a blocker.

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 all seven parameters are already documented with defaults, ranges and enum values; the description's default-model note duplicates the schema's own 'model' field text. It adds only the cross-reference to list_models for discovering valid model IDs, 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 and resource ('send one prompt to any Obscurify.ai model and get its reply') and immediately fixes the scope as single-shot, which is what separates it from the sibling chat tool. The one-line rationale about why you'd delegate to another model adds intent, not just mechanics.

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?

Names the alternatives and the conditions that select them: follow-ups needing earlier turns go to chat, comparisons go to second_opinion, model discovery goes to list_models. Both exclusions and the pointer for valid model IDs are explicit, leaving nothing 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?

Annotations already declare readOnlyHint and openWorldHint, so safety is covered. The description adds useful context about what status information is exposed and why an agent would check it, though it does not mention caching or freshness of the balance data.

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 compact sentences: the first defines what is returned, the second gives usage triggers. No filler, and the most important information comes first.

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 must explain return values, and it enumerates the key fields well. It could go slightly further on response shape or error behavior, but it is sufficient for an agent to call this zero-parameter 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 tool takes zero parameters, so the baseline is 4. There is no parameter syntax to explain, and the description correctly does not invent any.

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: shows the account's balance, plan, credit usage, token usage, and model access. It is clearly distinguishable from generation-focused siblings like ask, chat, and generate_image.

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 to use it before large batches and when a call fails with 402 (balance) or 403 (plan) errors. This gives concrete trigger conditions that an agent can act on without 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?

The annotation set (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false) already tells the agent this is a non-destructive but stateful open-world call. The description earns credit beyond that by disclosing the critical operational fact that the server keeps no state, so the entire history must be resent each turn. It does not mention cost, rate limits, or truncation behavior for long histories.

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 short sentences, each doing distinct work: what it does, the statefulness contract, and the routing alternative. The default model is front-loaded at the end where a caller will look for it, and 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?

With six parameters, full schema coverage, and annotations, the description covers the essentials: statelessness, the history contract, and the sibling comparison. The lighter note is return shape — there is no output schema, so a brief mention of what the reply contains (assistant text plus optional reasoning text) would close the remaining gap.

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 in the schema, including the default model, token cap, and reasoning-effort enum. The description reinforces that `messages` carries the whole history (explaining why via statelessness) and repeats the default model, but adds little beyond the schema text. 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?

States a specific verb and resource (continue a multi-turn conversation with an Obscurify.ai model), specifies exactly what the caller must supply (the whole history in `messages`), and names the sibling it is not (`ask`). An agent can distinguish it from `ask` and `second_opinion` 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?

Explicitly routes single-question use to the sibling `ask`, which is the key decision point for this tool family. It does not address the other siblings (`second_opinion`, `generate_image`, `list_models`) or state any when-not-to-use condition beyond that one comparison.

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 that this is a non-readonly, non-destructive, open-world, non-idempotent operation, so the safety profile is covered. The description adds useful operational context: it returns the image and incurs a small cost from the account balance, which is exactly the kind of behavioral detail 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?

Three short sentences, front-loaded with the core purpose, followed by return/cost behavior and the alternative listing mode. Every sentence earns its place with no repetition 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?

Given seven parameters, full schema coverage, and no output schema, the description supplies the important missing pieces: what is returned and that generation costs money. It could specify the image return format or link cost more explicitly, but overall an agent has enough to invoke it 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 description coverage is 100%, so every parameter is already well documented in the input schema. The description mentions prompt, `list: true`, and model families, but adds little parameter-level meaning beyond what the schema provides. Baseline 3 is appropriate.

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

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

The description states a specific verb and resource: generate an image from a text prompt using named Stable Diffusion models. It also distinguishes the tool's listing mode and names the underlying provider, so an agent can immediately understand that this is image generation rather than chat or balance lookup.

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 clearly explains when to use the tool for generation and when to pass `list: true` to inspect available image models and prices instead. It does not explicitly discuss when to prefer a sibling tool such as `list_models`, but the image-generation context is clear enough that this is a minor gap.

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 and openWorldHint, so safety is covered. The description adds genuinely useful context beyond that: the returned field set and the meaning of each plan tier (any/casual/pro). It does not cover pagination or defaults for limit/sort, which are left to the schema.

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 tight sentences: what it returns, how to filter, and why to call it. No filler, and the field list is front-loaded before the filtering guidance.

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, yet the description enumerates the return fields, so an agent knows what comes back. Minor gap: it doesn't state default limit or that results are paginated/truncated, which matters for a 6-param 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?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning by expanding the plan enum into concrete user tiers ('any = every account with a balance; casual = Casual or Pro; pro = Pro only'), disambiguating the one parameter most likely to be misunderstood.

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 'List' plus resource 'Obscurify.ai chat models', with the returned fields enumerated (IDs, context window, pricing, tool support, plan). It clearly distinguishes itself from ask/chat/second_opinion by framing itself as the model-discovery step for those 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?

Explicitly states when to use it: 'Use it to find a model ID for ask, chat or second_opinion, or the cheapest model with what you need.' It also gives the filter-to-goal mapping ('Filter by name, plan, price or tool support') and notes no API key is required, removing a likely blocker.

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 (non-destructive, non-idempotent, open-world). The description adds useful behavior: the fan-out to 2-5 models and the default model set. It omits cost/latency implications of N parallel calls and what happens when one model errors or is unavailable, which matters for a non-idempotent multi-call 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, front-loaded with the mechanism and the return shape, followed by the defaults. Every clause carries information; nothing is padding.

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 describing the side-by-side return. With all five parameters documented and defaults stated, the only real gap is partial-failure behavior and cost/latency expectations when querying up to five models.

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 earns above that by naming the three default model IDs and the 2-5 range, which the schema does not supply. It adds no extra guidance on system/max_tokens/reasoning_effort beyond what the schema already documents.

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 action (send the same prompt to 2-5 models at once) and a specific return shape (every answer side by side). The multi-model fan-out is inherently what separates it from the single-conversation siblings ask/chat, so an agent can route 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 concrete use cases: compare answers, check a claim, review code or a plan, break a tie. That's clear positive guidance, but it never says when not to use it or names a sibling alternative (e.g. ask/chat for a single-model exchange).

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