IG Digital Lab

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

With no annotations provided, the description carries the full burden of disclosing behavior. It clearly indicates this is a read-only factual lookup by listing the types of canonical facts returned and the intended question types. It does not mention recency or data source, but for a zero-parameter info retrieval tool this is acceptable.

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 sentences with no wasted words. It front-loads the core value ('Canonical facts') and immediately specifies the exact data fields and use cases. Every sentence 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 zero-parameter, no-output-schema tool, the description is complete: it tells the agent what content is available and when to invoke the tool. It leaves no ambiguity about what kind of questions this tool answers.

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 clarify beyond the schema. Per the baseline for zero-parameter tools, this is appropriately handled; the description adds no parameter-specific detail because none is needed.

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 a specific verb ('get') and resource ('company info'), then enumerates exactly what facts are included: agency activity, address, phone, email, founding year, pricing model, and target industries. It clearly distinguishes this from siblings by framing it as canonical company facts rather than service listings, service areas, or contact requests.

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 states when to use the tool: 'Use this to answer questions about who IG Digital Lab is or how to reach them.' This gives clear context for selection, though it does not explicitly name sibling tools or state when not to use it.

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 behavioral disclosure burden. It conveys that the tool is a read-only lookup and that each served area has a local page, but it does not describe the response format, sorting, or any limitations. For a simple zero-parameter getter, this is adequate but not rich.

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 with no filler. The first sentence packs the resource, scope, and output detail; the second gives concrete invocation 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?

For a zero-input tool with no output schema, the description sufficiently communicates what is returned (cities and metros with local pages) and when to use it. It is slightly light on exact return structure, but adequate for this simple lookup.

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 no parameters and the schema is empty with full description coverage. The description correctly focuses on output and use case rather than parameter behavior, which matches the baseline for zero-parameter tools.

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 returns the cities and metros served by IG Digital Lab in California, each with a local page, and explicitly frames it as a coverage lookup. This distinguishes it from the sibling tools about company info, services, audit requests, and contact submission.

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 when to use this tool: to answer whether the agency covers a given area. It does not name alternatives or list exclusion conditions, but the intended use is clear enough in context.

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 behavioral burden. It discloses that the tool returns eight services, each with a summary and canonical page URL. Since this is a simple zero-parameter read operation, this is sufficient 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?

The description is two sentences long, with no filler. The main content is stated first, and the use case is given in the second sentence. Every word adds value.

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 tool with no output schema and no annotations, the description is complete: it names the resource, the number of entries, the content of each entry, and the intended use case. No critical information 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?

The input schema has zero parameters, so there is nothing for the description to add about parameter meanings. The baseline for a no-parameter tool is 4, and the description appropriately focuses on the output instead.

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 the tool lists the eight services IG Digital Lab offers, each with a one-line summary and canonical URL. It also narrows its purpose to answering what the agency can build, which distinguishes it from the sibling 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?

The description provides a clear use case: 'Use this to answer what the agency can build.' It does not explicitly name alternatives or exclusions, but the intended context is evident from the phrasing.

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 states a critical behavioral fact: the request 'reaches a human' and must only be made with explicit consent and real contact details, which implies real communication will occur. It does not detail the follow-up process or delivery channel, but the most important behavioral expectations are disclosed.

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 sentences with no wasted words. The first sentence front-loads the exact purpose and scope of the audit, while the second adds the consent requirement and the human-delivery behavior. Both sentences earn their 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 7-parameter tool with no output schema and no annotations, the description covers the core purpose, the consent condition, and the fact that the request triggers human contact, which is sufficient for invocation. It does not explain the audit's delivery format or how it differs from submit_contact_request, but these are not essential for correct calling.

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 schema already documents all 7 parameters, giving a baseline of 3. The description adds only a generic reference to 'real contact details,' which aligns with the email/phone fields but does not enrich the meaning of bottleneck, website, or other parameters 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 the specific verb 'Request' with the resource 'IG Digital Lab's free audit' and defines what the audit covers ('a short review... visibility in AI answer engines'). This clearly distinguishes the tool from siblings like get_company_info, get_service_areas, and list_services, which are read-oriented, and from submit_contact_request, which likely handles general inquiries.

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 a clear usage condition: call only with the person's explicit consent and real contact details. It also signals that this action reaches a human, so an agent should treat it as a real-world, consent-gated operation. However, it does not explicitly contrast this tool with submit_contact_request or state when to prefer one over the other, stopping 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?

No annotations are provided, so the description carries the behavioral burden. It discloses that this action reaches a human, implying a real-world side effect and a non-automated follow-up. It does not cover response expectations or failure behavior, but the core human routing is 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?

Three sentences, front-loaded with the core action, followed by necessary consent conditions and the human-routing detail. Every sentence earns its place with no 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 contact submission tool with fully documented parameters and no output schema, the description is nearly complete: it defines the action, the consent prerequisite, and the human destination. A brief note on what happens after submission would make it fully complete.

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 schema already fully documents name, email, phone, and message. The description adds the 'real phone or email' condition but does not go beyond the schema at the parameter level, matching the baseline.

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 action: sending a message to IG Digital Lab on behalf of a person who wants to get in touch. It distinguishes itself from the sibling info and audit tools by making the contact-submission intent explicit.

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 an explicit usage gate: only call when the person has explicitly asked to be contacted and provided a real name and a real phone or email. It does not name an alternative tool for audit requests, so it stops short of full when-not/alternative guidance.

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