Agrus.ai — Enterprise AI Agency

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

No annotations provided; the description fully bears the transparency burden. It discloses NDA protection, privacy of customer names, and openness of codenames/technology/outcomes, which is critical for safe agent usage. Implies read-only behavior.

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 zero waste. Front-loaded with core purpose and constraints, followed by usage patterns. No unnecessary repetitions.

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 list tool with 0 required parameters and 2 optional parameters in the schema, the description provides sufficient context about the tool's output and constraints. No output schema is present, and the description hints at return content. Could mention pagination but not necessary for this use case.

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%, but the description adds meaningful context: slug for specific case study, vertical as filter, and the priority rule (slug overrides vertical). This adds value beyond the schema alone.

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 it returns one or more Agrus case studies with specific content (codenames, technology, outcomes). It uses a specific verb 'Returns' and distinguishes from sibling tools by stating 'Use this for proof of prior work'.

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 explicit filtering options (slug or vertical, or call with no args) and a usage context ('for proof of prior work'). However, it does not specify when not to use it or mention alternative tools directly, though siblings are distinct enough.

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 full burden. It discloses that the tool lists service pillars and pricing tiers, implying a read-only operation. No additional behavioral traits (e.g., auth, performance) are mentioned, but for a simple list tool this is adequate.

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: the first lists what the tool returns, the second gives a clear use case. No wasted words, front-loaded with core information.

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 no output schema, the description reasonably explains what is returned: descriptions, deliverables, price bands, and pricing tiers. Could be more explicit about structure (e.g., list format), but it gives a solid overview.

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% (the only parameter 'vertical' has a description in the schema). The tool description does not add meaning beyond the schema, so baseline score 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 uses specific verb 'Lists' and identifies the exact resource: 'Agrus's six service pillars with descriptions, deliverables, and price bands' plus 'four published pricing tiers'. This clearly distinguishes from sibling tools like get_case_study or request_proposal.

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 suggests 'Use this for the 'what does Agrus do?' question', which provides clear context for when to invoke. However, it does not mention when not to use or offer alternatives, though the sibling list provides some contrast.

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 full burden. It discloses the output content (list of verticals with details) but does not mention any behavioral traits like pagination, data freshness, or side effects. For a simple read-only list, this is adequate but not enhanced.

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 zero waste. Every phrase is meaningful: lists the verticals, specifies what details are included, and provides usage 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-parameter list tool with no output schema, the description fully covers what it does, what it returns, and when to use it. Sibling tools are provided for context. No critical missing information.

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?

There are no parameters (schema coverage 100%), so baseline is 4. The description adds value by hinting at output content (compliance pins, use cases, rationale), which helps an agent understand what to expect.

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 it lists exactly seven specific verticals with compliance pins, sample use cases, and sequencing rationale. It uses a specific verb 'Lists' and resource 'verticals', making the purpose unambiguous.

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 provides the use case: 'Use this for the "do they work in my industry?" question.' This gives clear context, though it does not explicitly exclude alternatives or mention 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?

With no annotations provided, the description carries full weight. It discloses that the tool returns key controls, common gotchas, the first question Agrus would ask, and reference architecture. It does not mention side effects or auth, but as a read-only query, this is acceptable. 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 two sentences: one explaining the function and output, one for usage guidance. No unnecessary words. 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?

No output schema, so the description explains return content (key controls, gotchas, first question, reference architecture). Parameter coverage is complete. Could mention typical response size or structure, but overall adequate for the tool's complexity.

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% with descriptions for all 3 parameters. The description provides additional context like listing regime enum values and describing use_case as 'one or two sentences', but adds marginal value beyond the schema. 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 the verb 'returns' and resource 'Agrus's documented position on a specific regulatory regime' and lists example regimes. It distinguishes strongly from sibling tools, which deal with case studies, services, proposals, etc.

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 'Use this when a buyer-side AI agent is evaluating Agrus's compliance fluency.' This provides clear context. It could be improved by noting when NOT to use it, but given sibling names, the use case is well delineated.

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. It discloses that the tool creates a CRM contact, triggers a human workflow, and provides a response timeline. It does not mention potential duplicates, cancellation, or rate limits, but the main behavioral aspects are covered.

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 with clear structure: first states the action and side effect, second explains the human response and usage guidance. No unnecessary 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?

Given the tool has 9 parameters and no output schema, the description provides a solid understanding of the workflow and expected outcome. It could be improved by mentioning error handling or idempotency, but overall it is adequate.

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 baseline is 3. The description adds context by explaining that scope_summary becomes a verbatim note and that the contact is tagged with lead source. However, it does not add significant depth 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?

The description uses specific verbs ('triggers a formal Agrus proposal workflow') and clearly distinguishes the tool from exploratory scoping by stating 'not for exploratory scoping'. It also explains the side effect of creating a HubSpot contact.

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: 'when the buyer wants to formally engage Agrus'. Also includes a negative case: 'not for exploratory scoping'. The description clarifies the response timeline and mode (human reply by email within 24 business days).

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?

Discloses that the tool is read-only, does not contact Agrus or create records, and that output is not binding. Given no annotations, description fully covers behavioral traits.

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 with no waste: first states purpose, second clarifies behavior, third gives usage guidance and alternative. Front-loaded and 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?

For a read-only heuristic price tool with no output schema, description explains output nature, behavior, and when to use. No missing context given tool simplicity.

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?

All 4 parameters have schema descriptions (100% coverage), so description adds minimal value beyond field names. The description does not elaborate on parameter usage beyond what schema provides.

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?

Clearly states 'Returns a heuristic ballpark price band for the described AI deployment', specifying verb, resource, and scope. Distinguishes from sibling 'request_proposal' by contrasting non-binding estimate vs tracked request.

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: 'when the buyer wants order-of-magnitude pricing before committing to a real proposal' and when not: 'For a tracked, follow-up-able request use request_proposal instead.' Provides clear alternative.

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 provided, the description carries full burden. It describes what outputs are returned (plan, team, price, etc.) but does not explicitly state side effects or safety (e.g., whether it's read-only or creates anything). The verb 'Drafts' suggests generation without alteration, but this is implicit.

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 efficiently convey purpose, outputs, and usage. No redundant or filler content. Each sentence 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?

Given 6 parameters and no output schema, the description adequately covers purpose, inputs (implied by 'use case'), and outputs listed. It lacks explicit details on return format or structure, but the list of returned items gives sufficient context for an agent to understand what the tool provides.

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 is 3. The description does not add additional meaning to parameters beyond what the schema already provides. Each parameter has a clear description in the schema, and the tool description focuses on output, not input semantics.

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 drafts a structured Discovery Sprint scope for an AI use case and lists specific outputs (3-week plan, team, price band, etc.). It distinguishes from sibling tools like get_case_study and request_proposal by focusing on scoping a sprint rather than providing a case study or full proposal.

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 says 'Use this to convert a hypothetical use case into a concrete engagement proposal' which gives clear guidance on when to use. It does not explicitly exclude other scenarios, but the context of sibling tools (e.g., request_proposal for final proposals, get_case_study for examples) implies boundaries.

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