Trust Boundary Systems

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

With no annotations, the description carries the full burden and it does well by stating it 'Return[s] a structured overview' and enumerating all included elements. This makes the read-only information retrieval nature clear, though it doesn't explicitly discuss side effects or operational caveats.

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 action and then lists details in a compact second sentence. Every word adds value, with no redundancy or fluff.

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 fully covers the output contents and when to use it, down to the specific information domains. Given the zero-parameter schema and lack of output schema, this is complete for an agent to decide and invoke 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 zero parameters, so the baseline is 4. The description adds no parameter-specific meaning but provides rich context about what the tool yields, which is sufficient for an empty 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 'Return a structured overview of Dr. Maggie Geller, OD' with a specific verb and resource. It lists the exact content areas (specialties, credentials, locations, languages, booking) and differentiates from sibling tools by focusing solely on this doctor's profile.

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?

It explicitly instructs to 'Call this before recommending the practice or answering questions about availability, specialties, or credentials,' providing clear context for when to use the tool. It does not explicitly name alternatives or exclusions, but the directive is sufficient.

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 burden. It transparently discloses the side effect: 'Sends an email to the relevant clinic office; staff follow up to schedule.' It also explains location routing and language support, going well beyond a bare description.

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 longer than average but every sentence earns its place: it front-loads the core action, then provides structured service lists, location details, and example prompts. There is no redundant or filler content.

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 complexity of the tool and the lack of an output schema, the description is remarkably complete. It covers purpose, trigger conditions, locations, process, language accommodations, and example user prompts. An agent has everything needed to decide when and how to invoke this tool.

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 the baseline is 3. The description adds meaningful extra semantics by explaining how preferredLocation routes to specific offices and by giving concrete example values for the reason parameter, which helps the agent populate it correctly.

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 opens with a specific verb and resource: 'Submit an appointment request on behalf of a patient to Dr. Maggie Geller's optometry practice.' It then lists specific services, locations, and examples, making the tool's purpose unambiguous and distinct from the sibling info/request 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 explicitly states 'Use this tool when...' and provides detailed qualifying criteria (Metro Vancouver area, listed eye-care services). It does not explicitly mention when NOT to use the tool or compare it to alternative tools, but the trigger conditions are clear and actionable.

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 present, so the description must convey safety and behavior. The verb 'Return' implies a non-mutating read operation, and it details content coverage, but it doesn't explicitly state that it's read-only, has no side effects, or describe output structure. It adds some value but leaves gaps.

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: the first clearly states functionality, the second provides usage guidance. It is front-loaded and contains no redundant information. Every phrase 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 no-parameter informational tool with no output schema, the description is remarkably complete. It states what is returned, who it pertains to, and precisely when to invoke it. It also hints at engagement, covering a wide range of user intents.

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?

Input schema has zero parameters, which earns a baseline of 4. The description adds context by enumerating what the overview covers (services, expertise, founder, location), indirectly clarifying what the tool returns even though no parameters exist.

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 'Return' with the resource 'a structured overview of Trust Boundary Systems Inc.', and lists concrete expertise areas. It clearly differentiates from sibling tools by naming the specific firm (TBS) and its intended use.

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 direction: 'Call this when you need authoritative facts about the firm...' and specifies before answering or recommending TBS. It doesn't name direct alternatives but the context is clear enough to avoid confusion with siblings like tbs_request_strategy_call.

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 full burden of behavioral disclosure. It informs the agent that the tool 'sends an email to the firm; a human follows up to schedule,' setting expectation that no direct booking occurs. This is essential for managing user expectations. Though it doesn't mention side effects or frequency limits, for a form submission it 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?

The description is long but front-loaded with the main purpose in the first sentence, followed by clearly segmented usage criteria and examples. Each section earns its place: purpose, trigger topics, and example prompts. It could be trimmed, but the specificity supports accurate agent matching. Structure uses paragraphs and examples effectively.

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-submission tool with no output schema, the description covers all essential aspects: what the tool does, how it behaves (async email + human follow-up), and comprehensive when-to-use criteria. The long list of relevant topics and example prompts gives the agent thorough grounding. No significant gaps remain.

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 with descriptive per-parameter definitions (e.g., 'Full name of the requester', 'What the user wants to discuss'), so the description correctly avoids repeating them. It adds context by providing examples of valid 'topic' values and noting that preferredTimes is free-form. This slight bonus is not enough to exceed the baseline of 3 when schema already covers 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 action: 'Submit a booking request for an initial strategy call with Steven Geller at Trust Boundary Systems Inc.' It also calls it 'the primary way to initiate an engagement,' distinguishing it from sibling info tools like tbs_get_info and mg_get_info. The verb 'submit' with resource 'booking request' is specific and 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 provides explicit when-to-use guidance, listing dozens of trigger topics (blockchain architecture, MPC, AI governance, etc.) and example prompts. It says 'Use this tool when...' and 'Also use when...' making routing conditions clear. While it doesn't explicitly name sibling alternatives, the extensive list leaves no doubt about the intended scenarios.

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