Toronto Tech Week 2026 Events

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

With no annotations, the description carries the burden. 'Get' implies a read operation, and the list of returned fields gives some transparency. However, it does not mention error handling, authentication, or rate limits, leaving gaps in behavioral disclosure.

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 unnecessary words. It efficiently states the action, resource, and key return fields.

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 tool with one parameter and no output schema, the description adequately covers the main purpose and return contents. It could mention potential errors, but the complexity is low enough that this is not a significant gap.

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 has 100% coverage for the single parameter 'id', explaining its origin. The description adds no additional meaning beyond the schema, so a baseline 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 tool's verb ('Get') and resource ('one event by id'), with explicit detail fields (description, host, venue, link). This distinguishes it from sibling tools like search_events and list_facets, which handle lists or facets.

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 usage when you have a specific event id, and the schema notes the id comes from a previous search_events result. This provides clear context, though it does not explicitly exclude other 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals that the tool returns available filter values along with counts, which is the core behavior. However, it does not explicitly state a read-only nature or other side-effect information, though the verb 'list' implies it.

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 concise, consisting of two sentences that are front-loaded with the core action and followed by a practical usage hint. Every sentence provides value, and 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?

For a tool with no parameters and no output schema, the description covers all essential aspects: what it lists, the categories included, that counts are provided, and when to invoke it. It is sufficiently complete for an agent to decide on and use 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 tool has zero parameters, so the baseline score is 4. The description does not need to explain parameters, and it adds no parameter-specific information beyond what the empty schema already communicates.

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's purpose: 'List all available filter values (dates, themes, audiences, formats, neighborhoods) with counts.' It uses a specific verb ('List'), identifies the resource (filter values), and lists the categories, making it easily distinguishable from the sibling tools get_event and search_events.

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 usage guidance: 'Call this first if the user's question is open-ended.' This tells the agent when to use the tool, but does not explicitly name alternatives or state when not to use it. It implies a sequencing strategy rather than fully specifying alternatives.

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?

The description discloses that results are 'lightweight rows' and directs users to get_event for details, which is useful behavioral context beyond the schema. However, it lacks details about default ordering, pagination, or whether q is required, and with no annotations available, the description carries the full transparency burden.

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, front-loaded with the main purpose, and contains no filler or redundant content. Every sentence contributes meaningful 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?

Given 10 parameters, nested date_range, and no output schema, the description is too brief. It does not describe what the 'lightweight rows' include, how to effectively use date_range, or mention list_facets for exploring facets, leaving gaps for the agent on a moderately complex 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?

The input schema provides 100% description coverage for all 10 parameters, so the baseline is 3. The description's phrase 'Combine `q` with optional filters' adds marginal guidance on how to group parameters but does not explain each parameter's purpose or formatting 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 clearly states 'Search Toronto Tech Week 2026 events' with a specific scope and verb. It explicitly contrasts with get_event by noting 'call get_event for full details', distinguishing the sibling tools effectively.

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 advises 'Combine `q` with optional filters', giving a clear usage pattern. It names get_event as an alternative for full details, but does not mention list_facets or explicitly state when not to use the tool, leaving some ambiguity.

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