ai-content-drop

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the batch-scoping behavior but does not explicitly clarify that no actual generation occurs; that must be inferred from the word 'estimate'. 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 a single sentence that immediately states the core action and scope, then adds the optional batch condition. Every word earns its place, with no redundant or irrelevant detail.

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 simplicity, full parameter coverage, present output schema, and read-only annotations, the description is sufficient. It communicates the purpose, the required model_id, and the optional quantity semantics that an agent needs 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?

Schema description coverage is 100%, so both parameters are already well-documented. The description adds modest semantic context by tying 'specific model' to model_id and 'batch of several generations' to quantity, but it does not provide additional syntax, constraints, or examples 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 uses a specific verb ('estimate') and a clear resource ('credit cost'), scoped to a specific model and optionally a batch. This clearly distinguishes it from sibling tools like generate_video, which actually create content, and list_models, which simply enumerates models.

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 intended use case is implied: call this when you need to know the credit cost before generating with a model. However, the description does not explicitly state when to prefer this over alternatives, nor does it mention that it should be used before generate_video or that it does not trigger generation.

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 adds substantial behavior beyond annotations: it reveals the async job pattern, the billing condition ('Charges credits only on success'), and the API-key requirement with setup URL. Annotations only state read/write/idempotent/destructive hints; the description enriches the contract with operational and auth details, with 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?

Four sentences, each earning its place: purpose, async workflor, cost guidance, auth requirement. The most important fact leads, and the description is compact with 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?

For a mutation tool with an output schema and five documented parameters, the description covers everything an agent needs to call it correctly: what happens on invoke, how to track the job, cost implications, and mandatory auth setup. No significant 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 description coverage is 100%, so the baseline is 3. The description does not add any parameter-specific semantics beyond what the schema already provides, but it doesn't need to; the schema documents all five parameters adequately.

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 ('Start generating an AI video') and immediately clarifies the asynchronous contract ('Returns a job id immediately — poll get_generation'). It distinguishes itself from the sibling get_generation by naming the polling relationship, so an agent can tell initiation from status-checking without opening other schemas.

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 to alternatives: 'poll get_generation until status is completed' names the follow-up tool, and 'Call estimate_credit_cost first if the cost matters' states the condition for using the cost estimation sibling. This gives clear when-to-use and when-to-use-other 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 mark it read-only/idempotent/non-destructive, and the description adds a crucial operational requirement: an API key with exact Authorization header instructions and the URL for obtaining it. This goes beyond structured 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?

Three short sentences, front-loaded with the core purpose, followed by usage timing and only essential auth setup detail. 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?

Given zero parameters, an output schema, and annotations covering safety, the description provides all necessary context: what the tool returns at a high level, when to call it, and how authentication must be configured.

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 there is nothing for the description to explain about inputs. This matches the baseline for parameterless tools; no semantic gap exists.

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 and content: 'Credit balance and plan for the authenticated account.' It differentiates from siblings like generate_video or get_generation by focusing on account-level billing/plan data, and the usage phrase reinforces its role.

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 timing context: 'Check this before a batch of generations.' It does not explicitly name alternative tools or when-not-to-use conditions, but the primary use case is clearly stated.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the 'published' scoping constraint and 'markdown' return format, which is useful but not rich behavioral context. No contradictions 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?

A single, front-loaded sentence states the action, resource, format, and input in about a dozen words. Every element 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?

For a simple one-parameter tool with a full output schema, annotations covering safety, and no nested objects, the description provides everything an agent needs: what to pass (slug), what it returns (full text as markdown), and the published-only constraint. No missing critical context.

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

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

Schema description coverage is 100%: the slug parameter is already documented with a description and example. The description's 'by slug' adds no new meaning beyond the schema, so it meets the baseline but does not exceed 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?

The description uses a specific verb ('Fetch'), states the resource ('full text of one published article'), specifies the output format ('as markdown'), and identifies the key identifier ('by slug'). This clearly distinguishes it from sibling tools like search_articles or list_generations without needing to open 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?

The description implies when to use the tool (when you have a slug and need the full published text) but does not explicitly state when not to use it or mention alternatives such as search_articles for finding articles. Usage context is implied, not explicitly routed.

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 annotations already mark the operation read-only, idempotent, and non-destructive. The description adds genuinely useful behavioral context beyond annotations: the API key requirement, the exact status vocabulary, and the returned fields (video URL, credits charged). 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?

Two sentences front-load the core purpose and then provide essential auth setup context. The auth instruction is a bit long but is operationally important for the agent, and there is no redundant 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 single-parameter read-only lookup with an output schema present, the description covers what the tool does, what it returns, and the required authorization. An agent has everything needed 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%, with the parameter already documented as 'The id returned by generate_video.' The tool description adds no additional parameter-level meaning beyond what the schema provides, 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 precise verb ('Check'), a specific resource ('one generation by id'), and the exact data returned: status values, finished video URL, and credits charged. This clearly distinguishes it from list_generations and generate_video.

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 makes clear it is a single-record lookup by id, especially with the schema note that the id comes from generate_video, which implies polling after generation. It does not explicitly name alternatives or state when not to use it, but the context 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?

Beyond the readOnly/idempotent/destructive annotations, the description discloses the API-key requirement, the exact Authorization header format, and how keys are obtained. It also adds newest-first ordering. 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?

Two sentences with no filler: the first front-loads purpose and ordering, the second states the auth requirement and key-creation URL. Every part 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 read-only list tool with one optional parameter and an output schema, the description combined with annotations and schema leaves no critical gap: what is listed, ordering, auth requirements, and limit behavior are all covered without needing to restate return values.

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

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

The schema already provides 100% description coverage for the only parameter, limit, including the allowed range and default. The description adds no parameter-specific meaning, so the baseline of 3 is appropriate.

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

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

The description opens with 'List recent generations on the authenticated account, newest first' — a specific verb, resource, scope, and ordering. This clearly distinguishes it from siblings such as get_generation (single resource) and list_models (different 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?

The description clearly establishes the context: list recent generations for the authenticated account, with explicit ordering. However, it does not explicitly name alternatives or exclusions, such as using get_generation for a single specific generation, so it stops short of a 5.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: the list includes flat credit cost per generation, which is a meaningful output detail an agent would want to know. 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 a single sentence that immediately states the action, resource, and key output detail. There is no filler or redundancy; every word earns its place, and the most important information is front-loaded.

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

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

Given low tool complexity, two optional parameters fully described in the schema, annotations covering safety/idempotency, and an output schema present, the description is complete enough for correct invocation. Nothing critical is missing for an agent to call this tool properly.

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 both parameters (type with enum/default, max_credits) are adequately documented in the schema. The description does not add extra semantic detail for parameters, but with full schema coverage the baseline of 3 is appropriate.

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

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

The description clearly states a specific verb ('List'), a precise resource ('AI video or image models available on AI Content Drop'), and adds the key detail that each model has a flat credit cost per generation. This differentiates it from sibling tools like list_generations (list generations, not models) and estimate_credit_cost (estimate costs, not list models).

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

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

The description implies when to use this tool — when you need to see what models are available and their per-generation credit costs. However, it does not explicitly mention alternatives, conditions, or when not to use it. Given the siblings, explicit routing would strengthen this, but the intent is 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?

Annotations already cover the safety profile with readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds content scope but no additional behavioral traits such as pagination, rate limits, or result ordering, so it stays at a baseline 3.

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 filler. It packs the verb, resource scope, and topic coverage efficiently, making it easy for an agent to parse quickly.

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 search tool with two fully documented parameters, safety annotations, and an output schema, the description is largely sufficient. It is slightly incomplete in that it doesn't hint at the recommended flow of searching then fetching an article via get_article, but nothing essential to invoking the tool correctly 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%: query and limit are both documented with examples and constraints. The description adds no parameter-specific meaning beyond what the schema already provides, so the baseline 3 is appropriate.

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

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

The description uses a specific verb and resource: 'Search AI Content Drop's published guides and model comparisons' about AI video generation, prompting, and ad creative. It clearly differentiates from generation and listing tools, though it does not explicitly distinguish itself from the closely related get_article sibling.

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 discovering published content by keyword, such as 'kling vs sora', but it provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives like get_article for retrieving a specific article by ID.

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