IG Digital Lab, Sacramento, CA

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

No annotations are provided, but the description clearly implies a read-only informational tool with no side effects. It does not explicitly disclose data sources or latency, but for a simple company info lookup, the behavior is sufficiently 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?

The description is a single, well-structured sentence that lists all relevant content categories and the usage context without any redundant words or filler. It front-loads the purpose and then lists specifics.

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?

The description covers what the tool returns (company facts) and when to use it. It does not specify the output format, but for a simple info retrieval, the details provided are sufficient for an agent to invoke and interpret the result 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 has zero parameters, so there is no parameter semantics to explain. The baseline score of 4 applies because there is nothing to clarify beyond the absence of inputs.

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 retrieves company info for IG Digital Lab and enumerates the specific content (services, address, phone, email, founding year, pricing, target industries). It uses the verb 'Get' and distinguishes this from other sibling tools that focus on service areas, service listing, or request forms.

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 this to answer questions about who IG Digital Lab is or how to reach them.' This direct instruction leaves no ambiguity about the intended use case.

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 supplied, so the description carries the full burden. It usefully discloses the return shape (one-line summary plus canonical page URL per service), which compensates somewhat for the missing output schema, but says nothing about read-only nature, freshness, or any auth/rate constraints.

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 content of the result is front-loaded ahead of the usage hint. Every clause 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?

With no input parameters, no annotations, and no output schema, the description covers what is needed: what the tool returns and when to reach for it. The only residual gap is the absence of any routing hint versus get_service_areas.

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 there is nothing for the description to disambiguate. Baseline 4 applies for a parameterless 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 resource (the seven services the agency offers) and even quantifies the result set, plus what each entry contains. It does not explicitly differentiate itself from the sibling get_service_areas, which an agent could plausibly confuse with this tool.

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?

"Use this to answer what the agency can build" gives a clear positive usage context, so an agent knows when this is the right call. It stops short of naming exclusions or pointing to get_service_areas for the adjacent question, so no alternative routing is provided.

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?

With no annotations, the description carries the burden and does well: it discloses the side effect (reaches a human), the delivery channel and latency (email within two business days), and a consent requirement. It omits what happens after submission or whether the request can be retracted.

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 offer and its definition, then the consent constraint and the human-reach warning. 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 form-style submission tool with no annotations and no output schema, the description covers purpose, timing, side effect, and the key consent gating. Return behavior is left implicit, but that is a minor gap given the tool's nature.

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 seven fields including the email-or-phone rule. The description only adds the 'real contact details' emphasis, which adds marginal meaning over the structured 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?

States a specific verb (Request) and resource (free audit), then elaborates what the audit actually is: a written review of manual processes emailed within two business days. This is far beyond a restated title and gives an agent a concrete mental model of the offer.

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?

Provides an explicit precondition (only with the person's explicit consent and real contact details) and context (it reaches a human). It stops short of naming the sibling offer, e.g. routing general inquiries to submit_contact_request, so no explicit alternative is given.

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.