Agentcy - Your AI Marketing Agent

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

Annotations already cover key behavioral traits (read-only, non-destructive, idempotent, closed-world), but the description adds useful context about the output format ('date, day of week, and timezone') and its application ('constructing date ranges'), enhancing understanding beyond annotations without contradiction.

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

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

The description is front-loaded with the core purpose in the first sentence and adds a brief, relevant usage note in the second. Both sentences earn their place by providing essential information without waste, making it highly efficient.

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

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

Given the tool's low complexity (0 parameters, no output schema) and rich annotations, the description is mostly complete, covering purpose and usage. However, it could slightly improve by hinting at the return format or error cases, though this is minor given the 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?

With 0 parameters and 100% schema description coverage, the baseline is high. The description appropriately does not discuss parameters, as none exist, and instead focuses on the tool's output and utility, adding value without redundancy.

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

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

The description clearly states the specific action ('Get') and resources ('current date, day of week, and timezone'), making the purpose explicit. It distinguishes itself from sibling tools like 'list_sources' or 'marketing_data' by focusing on date/time retrieval rather than data listing or analysis.

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

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

The description provides clear context for usage ('Useful for constructing date ranges'), indicating when to use this tool. However, it does not explicitly state when not to use it or name alternatives among sibling tools, which prevents a perfect score.

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

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

Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds behavioral context beyond annotations: it explains the tool lists sources and configured domains, and includes the automatic behavior with single domain. 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 consists of two clear sentences. The first sentence states the purpose, and the second provides usage guidelines. No superfluous words; every sentence earns its place. Front-loaded with purpose.

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 should help the agent understand the return format. It says 'list available data sources and configured domains,' which implies a list, but does not specify details like structure or field names. However, the tool is simple and the guidance on automatic behavior completes the context. Slightly lacking in return detail, but overall 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 input schema has one optional parameter 'domain' with a description. The description does not add any additional meaning or examples beyond what the schema provides. With 100% schema description coverage, baseline is 3; no extra value is added.

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

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

The description clearly states the tool lists available data sources and configured domains, with a specific verb and resource. It also provides usage context by saying 'call this to discover services and domains before querying,' distinguishing it from unrelated siblings like get_current_date, marketing_data, and research.

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 explicitly tells the agent to use this tool before querying to discover services and domains. It also provides a rule: if exactly one domain exists, use it automatically without asking. This is clear, actionable guidance for when and how to use the tool.

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, openWorldHint, idempotentHint, and destructiveHint. The description adds behavioral insight: the server auto-routes internally to all needed sources and returns a combined response with same granularity as individual queries. This goes beyond annotations and helps the agent understand internal routing.

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 purpose and key instructions, but contains some redundancy (e.g., the single-call rule is mentioned twice). However, it is well-structured with clear sections for each parameter. A small reduction in repetition would make it more concise, but overall it is efficient for the amount of information conveyed.

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

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

Given the tool's complexity (7 parameters, no output schema, multiple data sources), the description addresses usage guidelines, parameter details, and edge cases (e.g., missing domain). It does not explain return format, but annotations and the 'combined response' hint partly compensate. Overall, it provides sufficient context for an AI agent to use the tool correctly.

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

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

The input schema has 100% coverage, so baseline is 3. However, the description significantly augments each parameter with practical usage details: domain includes when required and fallback advice, request emphasizes including everything in one question, dates include defaults and relative formats, format_hint explains when to set vs leave unset, source_hints describes auto-selection, and reference_images clarifies usage. This adds substantial meaning beyond the schema.

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

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

The description clearly states the tool's purpose: to query marketing data and analyze websites across multiple domains including analytics, SEO, advertising, etc. It distinguishes itself from siblings by emphasizing that a single call covers multiple data sources, and it contrasts with list_sources and research which have different scopes.

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?

Explicit guidelines are provided: always use a single call, do not split multi-source questions, call list_sources first if no domain is established, and ask the user when ambiguous. It also warns against setting format_hint for normal answers. These clearly indicate when and how to use the tool.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds valuable behavioral context: it searches multiple engines, reads webpages, extracts transcripts, etc., which aligns with these hints and provides further transparency.

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 concise sentences that front-load the purpose and cover major capabilities. No redundant words.

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

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

For a tool with 2 params and no output schema, the description fully explains the tool's broad capability (100+ engines, specific actions). It notes that a domain is optional and it works without one, covering the parameter context well.

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 3 is appropriate. The description repeats the idea of 'domain' being optional and 'request' being a natural language query, but does not add new meaning beyond the schema descriptions.

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?

Description clearly states the tool performs research across many engines (Google, Bing, YouTube, etc.) and specific actions (read webpages, extract transcripts). It distinguishes from siblings like get_current_date and marketing_data which have narrower scopes.

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 explicitly covers a wide range of use cases ('any topic', competitor research) but does not explicitly state when not to use this tool or compare to siblings. However, the context of sibling tools implies that for specific tasks, other tools would be more appropriate.

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