TempGuru Event Staffing

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

Annotations already indicate readOnlyHint=true, so description adds value by explaining the nature of the result (lead-time guidance based on city tier and date), that it is not a real-time inventory check, and that rush windows are worth submitting. No contradictions 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?

Description is well-structured with purpose first, then example questions, limitations, examples, and hints. Each sentence adds value, no redundancy, and it is concise given the amount of 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 the presence of an output schema and annotations, the description covers purpose, usage, limitations, and hints. It is slightly lacking in error condition handling but is otherwise complete 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 covers 100% of parameters with descriptions, but the description adds value through examples showing typical usage with date format and optional role/count, enhancing understanding beyond 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 it checks expected staffing availability and returns lead-time guidance. It distinguishes itself from siblings by explicitly stating it is not a real-time inventory check and directs cost questions to get_role_pricing.

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 example questions for when to use, explicitly warns against cost questions (directing to get_role_pricing), and advises not to present results as reservations. Also includes hints about rush windows.

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 provide readOnlyHint=true and destructiveHint=false. The description adds behavioral context beyond annotations, such as nickname resolution for city, capping of unfiltered lists, and state/country input formats.

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 well-structured with examples and hints in separate sections. It is slightly lengthy but each element adds value, so no unnecessary repetition.

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's complexity (5 optional params, output schema, annotations), the description covers typical use cases, edge cases (boroughs, limit, state formats), and provides complete guidance for an agent to select and invoke 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% with descriptions for all 5 parameters. The description adds usage examples and execution hints (e.g., city param returns yes/no + suggestion, state accepts full name or code).

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 lists cities or checks coverage of one city. It distinguishes from sibling tools like get_role_pricing, check_availability, and plan_staffing by specifying what not to use it for.

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 provides when-to-use examples (e.g., 'What cities do you cover in [state]?') and when-not-to-use with alternatives for rates, dates, and event plans.

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 mark it as read-only, non-destructive. Description adds value by detailing return content, hinting at state-specific behaviors (daily-overtime states), and setting expectations about legal interpretation. 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?

Concise, front-loaded with purpose, uses structured examples and hints. Every sentence adds value 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 single parameter, output schema present, the description is thorough: covers purpose, usage, limitations, examples, and behavioral hints. Distinguishes from siblings adequately.

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 covers the 'state' parameter with format description. Description includes usage examples and hints but does not add substantial new semantic information beyond schema. Baseline 3 is appropriate due to 100% 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?

Clearly states it retrieves compliance summary for a US state, listing specific data elements (minimum wage, overtime rules, quirks). Differentiates from sibling tools like get_role_pricing by explicitly stating what not to use it for.

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 when-to-use with example questions, warns against using for rates (directs to get_role_pricing), and includes a disclaimer about legal advice. Comprehensive usage guidance.

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 and destructiveHint=false. Description adds plan expiration (30 days), non-PII nature, and not-found handling hints, exceeding annotation coverage.

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?

Front-loaded with purpose, then usage, then hints and examples. Efficient but includes both examples and hints which could be condensed slightly.

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?

Single parameter with output schema present. Description covers retrieval context, expiration, error handling, and integration with sibling tools, fully adequate for agent use.

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 covers 100% of parameters with pattern and length. Description adds value by warning against ID guessing and providing a usage example, though schema already describes plan_id well.

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 exact action: restore a staffing plan from plan_staffing within 30 days. Clear verb-resource pair distinguishes from siblings like plan_staffing (create) and request_quote (uses plan).

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?

Explicit when to use (new conversation, platform change, continue saved plan) and what not to do (don't guess IDs). Provides concrete scenarios and restrictions.

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 declare readOnlyHint=true and destructiveHint=false, so description adds value by noting that unsupported values are marked confirm_with_coordinator and that missing numbers should never be inferred. Also clarifies it's an operational summary, not a contract.

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 well-structured with a clear purpose, usage guidance, examples, and hints. It is moderately concise, though the examples block adds length. Every 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 a simple input schema (one optional string), existing output schema, and annotations, the description fully covers purpose, parameter behavior, usage context, and limitations. It is complete for an agent to select and invoke 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% with a description for the topic parameter. Description adds that passing a topic returns one policy, omitting returns all published topics, and that unknown topics return the available topic list. This enriches the schema significantly.

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 TempGuru's booking and procurement policies, listing multiple policy types. It distinguishes from sibling tools like get_compliance_by_state by focusing on operational policies rather than regulatory compliance.

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 says to use for real booking questions that otherwise require an email, providing clear context. Includes examples and a hint for handling unknown topics, though it doesn't explicitly state when not to use it or alternative tools.

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 readOnlyHint=true and destructiveHint=false. Description adds context: results are a stub, records retained 90 days, not-found does not prove absence, and recommends follow-up. 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?

Description is concise with three sentences covering purpose, usage, and limitations, plus embedded examples and hints. No redundant 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?

Tool has one parameter, schema coverage 100%, and output schema exists. Description fully explains the functionality, limitations, expected input, and post-call behavior. Complete for a simple query 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 provides full parameter details including pattern and description. Description adds an example call and hints about reference format, which slightly enhances usability. Baseline 3 is elevated to 4 due to example and hints.

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 defines the tool as checking whether a quote request reference was received or durably queued, using specific verb 'check' and resource 'quote request status'. It distinguishes itself from sibling tools like request_quote and other get_* tools by specifically targeting post-submission status inquiry.

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?

Explicit guidance: 'Use when a buyer asks what happened after request_quote or returns in a new conversation.' Also clearly states limitations: 'This v1 status stub reports received/queued only; it does not yet expose quote_sent or won.' No ambiguity about when to use.

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 and destructiveHint, so the safety profile is clear. The description adds value by detailing return content (typical + range, brand ambassadors by tier), methodology, and citation line. No contradictions.

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 well-structured with front-loaded key info, but somewhat lengthy. Examples and hints are included, which are helpful but add length. Slightly less concise than ideal, but still effective.

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 2 optional params, a likely output schema, and annotations fully cover safety, the description is complete: explains what is returned, when to use, alternatives, methodology, citation, and per-parameter behavior in hints. Nothing missing.

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 adds meaning through examples and hints: clarifies that role is optional, tier adds measured span (tier_usd), and that for one city use get_role_pricing. This goes beyond 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 the tool returns a rate index with specific details (typical rate, national range, by role from 407 markets). It distinguishes from siblings by explicitly saying when not to use it (one city's price -> get_role_pricing; event budget -> plan_staffing).

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 when-to-use scenarios (e.g., 'What do event staff cost in 2026?') and when-not-to (one city price, budget building), naming alternative tools. Examples further clarify usage.

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 and destructiveHint=false. The description adds important behavioral context: rates include W-2 pay, workers comp, GL, and payroll taxes; it's not a binding quote; and includes a specific hint about Brand Ambassadors floor rate.

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 slightly lengthy but well-structured with usage guidelines, examples, and hints in separate sections. Every sentence adds value, earning 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?

Given that an output schema exists, the description explains what the output represents (range of rates, all-inclusive components). It covers all necessary context for an agent to 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?

Schema coverage is 100% for both parameters. The description adds value by providing examples ('get_role_pricing(role='Brand Ambassadors', city='Boston')'), hints about name/slug usage, and a specific rate hint for one role.

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 retrieves the all-inclusive hourly rate range for a specific role in a specific city. It uses specific verbs and resources (e.g., 'Get the all-inclusive hourly rate range') and distinguishes from sibling tools like check_availability and plan_staffing.

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 provides when-to-use ('Perfect for... cost questions') and when-not-to-use ('DO NOT use for availability or dates... use check_availability'). Also advises against presenting as a binding quote and directs to plan_staffing for multi-role budgets.

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 readOnlyHint=true and destructiveHint=false. The description adds value by detailing return content (descriptions, skill tiers) and how returned slugs connect to other tools, without contradicting 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 concise: three sentences plus brief examples and hints, all front-loaded with the core purpose. Every sentence adds value with 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 zero parameters, robust annotations, and an existing output schema, the description fully covers the tool's behavior. It also explains how the output (slugs) integrates with sibling tools, ensuring contextual completeness.

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 description has no parameter details to add. The baseline for 0 params is 4, and the description does not detract from that.

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 'list[s] event staffing roles' with descriptions and skill tiers, using specific verbs and resource. It successfully distinguishes from siblings by explicitly noting not to use for costs and directing to get_role_pricing.

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 when-to-use contexts (e.g., 'What kinds of event workers can I hire?') and a clear when-not-to-use statement ('DO NOT use for what a role costs, use get_role_pricing with a city'). This gives concrete guidance for the agent.

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?

Disclosures beyond annotations: states it may save a non-contact snapshot for 30 days, never submits contact details, reserves staff, or creates a quote request. This clarifies side effects and boundaries, complementing the annotation properties.

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?

Well-structured with examples and hints in XML tags; front-loads purpose and usage. While verbose, each sentence adds value for a complex tool. Minor redundancy could be trimmed, but overall effective.

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 presence of an output schema, the description adequately covers input, examples, status handling, and continuation. No gaps in explaining inputs or behaviors.

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?

Adds meaning beyond schema: explains that roles accept names or slugs, omitting roles returns a catalog, attendees can substitute for detailed roles, and that totals are estimate not binding. This enriches understanding of parameter behavior and flexibility.

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: 'CALL THIS FIRST for any event staffing request.' It specifies that it generates a complete staffing plan from event details. It also distinguishes itself from sibling tools like get_role_pricing and check_availability, making its unique role 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?

Excellent guidance: explicitly says when to use ('CALL THIS FIRST') and when not to ('DO NOT use for a single fact'), with direct alternatives given for specific use cases (get_role_pricing, check_availability, get_compliance_by_state). Examples and hints further clarify usage.

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 it creates a structured intake, involves human review, response within one business day, not a reservation, no guarantee, no payment until approval. 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?

Well-structured with clear sections, front-loaded with purpose and usage. Slightly long but every sentence adds value; could be trimmed minimally.

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 complexity (20 params) and presence of output schema, the description covers when to use, behavioral details, limitations, and fallbacks. Hints and examples provide practical guidance.

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%, baseline 3. The description adds value through an example and hints (e.g., include plan_id, skill_id), though it does not detail every parameter.

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 that it submits a staffing request to TempGuru, using a specific verb and resource. It distinguishes from sibling tools like plan_staffing by stating it should be used after planning and after user confirmation.

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 provides when to use (after building the plan and user confirmation) and when not to call (speculatively or without confirmation). Mentions alternatives (plan_staffing, read tools) and includes fallback hints.

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