Dribba

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

Annotations already declare the tool is read-only, idempotent, and non-destructive. The description adds context about what content is provided and how omitting the 'fields' parameter returns every fact. This is useful behavioral detail beyond the 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?

The description is a single, focused sentence that front-loads the tool's purpose and lists key facts. Every part contributes meaning without 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?

Given the low complexity, single optional parameter, and strong annotations, the description is nearly complete. It covers the main content areas and parameter behavior, though it does not detail exact return formatting or the full set of possible fact keys.

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 documents the 'fields' parameter and its omission behavior. The description adds value by enumerating valid fact keys (positioning, offices, team, distinctions, clients), helping the agent construct valid field subsets.

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 indicates the tool provides structured facts about Dribba and lists the fact categories. It is distinguishable from sibling tools about projects and services. However, it lacks an explicit verb like 'retrieves' or 'returns', so it is not a full 5.

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 use when company facts such as positioning, offices, team, distinctions, or clients are needed. It does not explicitly state when not to use it or name alternatives, so guidance is only implicit.

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 carry readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: estimates are approximate ('ballpark'), mirror an external calculator, and have a €30,000 minimum project budget.

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 short sentences with no filler. The core purpose is front-loaded, and the minimum budget constraint earns its place as a key operational detail. 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?

The description tells the agent the tool produces a budget/range and a minimum constraint, but with no output schema it does not explain the expected response shape, currency beyond the minimum, or how the optional parameters affect the result. It is adequate for recognizing the tool's job but leaves room for uncertainty around calling and interpreting results.

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 only 25% — only 'platforms' gets a description. The description does not compensate for the missing parameter semantics; it never explains what values like 'basic', 'custom', 'rush', 'normal', or 'mvp' mean or how they influence the estimate. The agent is left with enum labels and little guidance.

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 action: provide a ballpark budget and range for an app/product, clearly distinguishing it from sibling tools like company_facts, list_projects, list_services, and submit_contact_request. It also references dribba.com's public calculator, giving the agent a concrete anchor for the behavior.

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 phrase 'Ballpark budget and range for an app/product' gives clear context for when this tool applies, and no sibling tool competes for the same purpose. It does not explicitly state when not to use it, but the domain is distinct enough that usage is evident.

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, covering the safety profile. The description adds the resource scope and output fields but does not disclose ordering, pagination, or default behavior beyond what the schema already provides.

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 sentence that is direct, front-loaded, and free of filler. Every word contributes to identifying the tool's purpose and output.

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 endpoint with two optional parameters and full schema coverage, the description is sufficient. It names the output fields, and the annotations cover safety. Minor omissions like default limit or ordering are not critical for this level of 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 description coverage is 100%, so the schema fully documents limit and industry parameters. The description does not add any parameter-level detail, which is acceptable given the high schema coverage.

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 ('List'), the resource ('Dribba's case studies'), and the output fields ('slug, title, industry'). This distinguishes it from sibling tools like list_services without needing to inspect 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?

The description implies this is the tool for listing case studies, but it does not explicitly say when to use it over alternatives or when not to use it. There are no exclusions or comparison with list_services or other siblings.

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, covering the safety profile. The description adds that results include a one-line summary and URL, but it does not mention pagination, ordering, or other behavioral traits beyond what annotations and schema already provide.

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 wasted words. It states the action, the resource, and the output format compactly.

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 operation with one optional parameter, the description is complete: it names the resource, describes the return shape, and the schema documents the limit parameter. 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 coverage is 100%: the single optional 'limit' parameter is fully described in the input schema. The description adds no additional parameter-specific meaning, which is acceptable because the schema carries the burden.

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 ('List Dribba's services') and specifies the output content ('one-line summary and URL'), making the tool's purpose clear. It does not explicitly distinguish from the sibling 'list_projects', so it misses the full sibling-differentiation bar.

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?

No guidance is provided about when to use this tool versus alternatives such as 'list_projects' or 'company_facts'. The only implied usage is that one uses it when needing a list of services, but there are no explicit conditions or exclusions.

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 a mutating action (readOnlyHint=false, idempotentHint=false). The description adds that it sends an email and creates a lead, plus a rate limit, beyond what annotations provide. It does not contradict annotations. While it could detail failure modes or reversibility, the core behavioral disclosure is solid.

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, zero filler. The primary action is front-loaded, and the critical usage condition is stated boldly with 'ONLY'. Each clause earns its place: purpose, effect, confirmation requirement, rate limit, and response time.

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 mutating tool with no output schema, the description adequately covers the essential behaviour and constraints. It omits response format or error handling, but given the tool's simplicity and the strong usage guidance, the gap is minor. The user-confirmation requirement is the most critical context and is clearly stated.

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 three required and two optional parameters. The description reinforces which parameters must be confirmed ('name, email and message') but adds no new semantic detail beyond the schema, aligning with the baseline for high coverage.

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 ('Submit a real project inquiry to Dribba'), the mechanism ('sends an email and creates a lead'), and the critical precondition (after user confirms exact details). This distinguishes it from sibling tools like list_projects and estimate_project without ambiguity.

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 call: 'ONLY call it after the user has explicitly confirmed the exact name, email and message to be sent.' Also adds a rate-limit note and the expected response time (Dribba replies within 1h), giving the agent clear operational constraints.

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