TempGuru Event Staffing

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

Annotations indicate read-only and non-destructive, and the description adds context that this is not real-time and that a coordinator must confirm coverage. It also notes same-week backfills exist, giving useful nuance beyond 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 well-structured with definition, use cases, exclusions, examples, and hints. Each sentence serves a purpose—no fluff—and the front-loading is 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 output schema exists and annotations provide safety hints, the description covers the essential context: purpose, limitations, non-reservation assurance, and edge cases. It is comprehensive 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, so baseline is 3. The description adds value through example calls showing parameter combinations and positional usage, which helps apply the schema 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 clearly states the tool returns tier-based lead-time guidance for an event date and market, using specific verbs and resource. It explicitly distinguishes from siblings by saying it is not a real-time inventory check and directing 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 explicit use cases ('What's the typical lead time...') and exclusions ('DO NOT use for cost questions, use get_role_pricing'), plus a warning not to present results as a reservation. This gives clear when-to-use and when-not-to-use 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, but the description goes much further: it reveals that catalog presence is not availability, that a coordinator confirms orders, that unfiltered results are capped, and that city resolution handles nicknames/boroughs. These are non-obvious behaviors an agent must know to set expectations.

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 dense but efficiently structured: a clear lead sentence, usage guidance, caveats, examples, and hints. Every sentence adds actionable information, and the use of <examples> and <hints> sections makes it scannable. No filler or 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?

For a read-only catalog tool, the description covers all critical context: purpose, when to use (and when not to), parameter behaviors, output interpretation (catalog match vs. suggestion, totals/tier_breakdown), and limitations (cap, coordinator confirmation). With annotations and an output schema present, nothing essential is 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?

Even though input schema coverage is 100%, the description enriches parameters significantly: it explains that state accepts both codes and full names, city resolves nicknames/boroughs, limit caps results but still returns full counts in total/tier_breakdown, and provides valid usage examples like get_cities(tier='hub', country='CA'). This is valuable beyond the schema's field 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 opens with 'List configured market entries by tier, or match one city,' which is a clear, specific verb+resource statement that immediately distinguishes the tool from siblings like check_availability and get_role_pricing. It also includes concrete example queries and clarifies the catalog-only scope.

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 tells the agent when to use this tool ('Use for...'), when not to use it (not for availability/coverage), and names alternatives: 'Use get_role_pricing for rates, check_availability for dates, or plan_staffing for a full plan.' This is model guidance beyond what siblings provide.

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 indicate readOnlyHint=true and destructiveHint=false; description adds that it returns compliance summary and is non-binding legal info, no contradictions. Provides context beyond 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?

Front-loaded with purpose, includes examples and hints. Slightly verbose but every sentence adds value. Could be trimmed slightly without loss.

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, existing output schema, and sibling tools, description fully covers usage, return details, and caveats. No gaps for this simple 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%, but description adds concrete examples ('CA', 'Tennessee') illustrating the parameter, enhancing clarity beyond the schema description.

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 'event staffing compliance summary for a US state' with specific outputs (minimum wage, overtime rules, quirks), distinguishing it from siblings like 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?

Explicitly states when to use (questions about W-2/1099, minimum wage, compliance gotchas), when NOT to use ('DO NOT use for rates'), and directs to alternative get_role_pricing. Also includes legal disclaimer.

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, but the description adds meaningful context: the plan is non-PII, expires after 30 days, and a not-found result indicates expiration, storage unavailability, or a wrong ID. It also instructs reviewing the restored plan with the user before request_quote, enriching behavioral understanding beyond the annotation hints.

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 and front-loaded with the core purpose, followed by usage scenarios, a critical constraint, an example, and error-handling hints. Every sentence serves a purpose, and the use of `<examples>` and `<hints>` tags makes it scannable without unnecessary verbosity.

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 single parameter, read-only annotation, and presence of an output schema, the description is contextually complete. It covers when to use the tool, what happens on failure, the plan's 30-day validity, and the recommended workflow step before quoting, leaving no significant gaps for an agent.

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 provides 100% coverage for plan_id with a clear description and pattern. The description goes further by adding a practical guardrail ('DO NOT guess or enumerate plan IDs') and an example, which reinforces proper usage and adds value beyond the schema's technical specification.

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 restores a complete non-PII staffing plan created by plan_staffing or save_staffing_plan within the last 30 days. This specific verb-resource pair (restore + plan) distinguishes it from sibling get_* tools that handle different domains such as availability, cities, or policies.

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 scenarios are provided: buyer starts a new conversation, changes agent platforms, or wants to continue a saved plan before requesting a quote. It also includes a do-not instruction (never guess or enumerate plan IDs) and an explicit alternative (re-run plan_staffing on not-found), giving clear guidance on when to use this tool versus 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?

Beyond the readOnlyHint annotation, the description discloses important behavioral quirks: 'Values not supported by canonical copy are explicitly marked confirm_with_coordinator with TODO-for-Megan; never infer a missing number' and 'unknown topics return the current available-topic list.' It also clarifies that the output is 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 lead sentence, usage guidance, a caveat, and organized examples/hints tags. It is slightly longer than necessary but every sentence contributes useful information, and the structure improves readability.

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 one optional parameter and an output schema, the description covers all essential aspects: real-world usage, missing-value handling, alias resolution, and unknown-topic behavior. The presence of an output schema reduces the need to explain return values, and the description completes the picture.

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 fully documents the topic parameter with an enum and description (100% coverage). The description adds extra semantics such as narrow lexical aliases being accepted and unknown topics returning the available-topic list, providing useful edge-case behavior 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 states 'Get TempGuru's published booking and procurement policies, plus any currently published public offer' with a clear verb and resource. It distinguishes itself from sibling tools like get_plan, get_roles, and get_rate_benchmark by focusing specifically on policy content.

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 for real booking questions that otherwise require an email,' giving a clear when-to-use context. It also provides hints about topic selection and behavior for unknown topics, but it does not explicitly name when-not-to-use 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 declare readOnlyHint=true, and description adds limitations: only reports received/queued, not yet quote_sent/won. Also notes 90-day retention and that not-found doesn't mean CRM lead is absent. 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?

Concise yet comprehensive: first sentence states purpose, then usage constraints, followed by examples and hints in structured sections. No wasted 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 an output schema, the description doesn't need to explain return values. It covers purpose, usage, limitations, and edge cases (not-found fallback), making it complete for this simple 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% with a single parameter, so baseline is 3. Description adds context about when the reference is obtained and includes an example, going beyond the schema description. Minor improvement but not fully compensating for absent shared understanding.

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 checks if a TempGuru quote request reference was received or queued, with specific context (CRM/queue). It distinguishes from siblings by noting the authless handoff does not create a reference, providing clear scope.

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 (after buyer submits form and gets TG reference, or for historical reference) and when not to use (authless MCP handoff). Hints provide follow-up for not-found results.

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 behavioral specifics: returns national typical + range, methodology, citation, and that brand ambassadors are shown by market tier. This adds useful context beyond 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 well-structured with clear sections: purpose, use cases, exclusions, examples, and hints. Every sentence adds value, no 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 covers the tool's core functionality thoroughly: what it returns, methodology, citation format, use cases, and alternatives. An output schema exists, so return details are not needed. Complete for a national benchmark 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% with descriptions. The description adds value by explaining that passing tier adds each role's measured span within that tier (tier_usd), and examples show usage. This clarifies the parameter's effect 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 the tool returns all-inclusive W-2 hourly bill rates by role from 407 markets, with typical rate, national range, methodology, and citation. It distinguishes from siblings by explicitly saying not to use for one city's price or building an event budget.

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 perfect use cases (e.g., 'What do event staff cost in 2026?') and exclusions (e.g., 'DO NOT use for one city's price (use get_role_pricing) or to build an event budget (use plan_staffing)'). Also includes hints directing to get_role_pricing for one city.

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 valuable context: rates include W-2 pay, workers comp, liability, and payroll taxes. It also warns against presenting as binding quote, which is beyond what annotations cover.

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 examples, and hints. It is somewhat long but every sentence adds value. The use of <examples> and <hints> tags helps parsing.

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 explains the return (hourly rate range) and includes important caveats (all-inclusive, non-binding). It covers all necessary aspects for the 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 describes both parameters (role, city) with examples. The description adds meaningful context: examples of valid inputs, hints that both accept names or slugs, and the special floor value for Brand Ambassadors. This goes 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 'Get the all-inclusive hourly rate range for a specific role in a specific city.' It provides example questions that directly illustrate the tool's function, and it distinguishes itself 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 tells when to use ('What does it cost...' questions) and when not to (DO NOT use for availability or dates; never present as binding quote). Directs to alternative tools: check_availability for dates and plan_staffing for multi-role budget.

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. Description adds that returned slugs are the exact values other tools accept, which is useful context beyond 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?

Two sentences plus <examples> and <hints>. Front-loaded with purpose, then usage guidance and practical hints. Zero waste.

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 parameters, annotations covering safety, and output schema present, the description fully equips an agent to select and invoke this 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?

Input schema has 0 parameters and coverage is 100%, so description need not add param details. No gaps.

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?

Description clearly states 'List event staffing roles TempGuru provides, with descriptions and skill tiers.' It gives explicit question examples ('What kinds of event workers can I hire?') and differentiates from 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?

Explicitly says 'DO NOT use for what a role costs, use get_role_pricing with a city.' Also hints that returned slugs are used by other 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 are all false (readOnly, openWorld, idempotent, destructive), so they don't disclose side effects. The description compensates by disclosing that it 'may save a non-contact snapshot for 30 days and return plan_id,' that it does not confirm coverage or inventory, and that totals are estimates not quotes. It also explains status branches (plan, needs_roles, roles_not_found, city_not_found). This is rich behavioral context beyond annotations, and no contradiction exists.

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?

While lengthy, every sentence earns its place. The description is front-loaded with 'CALL THIS FIRST' and then enumerates return types, exclusions, alternatives, persistence rules, and status handling without redundancy. It is dense but structured, with no filler or repeated schema 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 complex tool with 6 parameters, an output schema, and 11 siblings, the description covers all important operational context: what the tool returns, what it doesn't do, when to call alternatives, side-effect behavior, and branching logic. The output schema exists, so return values need not be fully repeated, but the description still provides a high-level overview and action guidance on statuses.

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 baseline is 3. The description adds minimal parameter semantics beyond the schema; it mentions 'Omit roles for the catalog and a suggested mix,' but the schema's roles property already says 'Omit to receive the role catalog and a suggested mix.' Other parameter details (city, date, headcount) are already fully documented in the schema, so the description doesn't add meaning beyond what structured fields provide.

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 what the tool does: it returns a configured-market catalog match, role-level W-2 rate math, estimated totals, lead-time guidance, and compliance flags. It uses the specific verb 'plan' and resource 'staffing,' and distinguishes itself from siblings by stating 'CALL THIS FIRST for event staffing requests' and 'Use granular tools only for a single rate, date, or state fact.'

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 is provided: 'CALL THIS FIRST' establishes when to use, and 'Use granular tools only for a single rate, date, or state fact' names the alternative. It also gives a conditional instruction for persisting: 'If it returns no plan_id and the buyer needs persistence, call save_staffing_plan once; never save a plan that already has an ID.' Exclusions ('never accepts or submits contact details, reserves staff, or creates a quote request') further clarify boundaries.

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 readOnly/idempotent/non-destructive, and the description adds substantial non-obvious behavior: it never accepts or transmits contact details, never creates a CRM lead or quote reference, demands buyer-side form submission for lead creation, and states it is not a reservation with no pricing/availability guarantee. 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?

Purpose is front-loaded in the first sentence and each subsequent sentence adds a distinct compliance or edge-case fact. The description is dense but justified for a PII-sensitive tool; the final sentence packs a privacy instruction that partially restates sentence two's contact-detail boundary, and mixes two unrelated directives, costing a point.

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 rich output schema (return shape need not be explained), the description is complete for its complexity tier: preconditions, exact side-effect boundary, buyer's required action, non-guarantees, and two edge cases (continuation URL without plan_id, expired plans). It fully supports correct agent invocation for a compliance-sensitive flow.

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%, setting a baseline of 3. The description adds value for plan_id by reinforcing the non-PII constraint, tying it to specific producing tools, and adding lifecycle semantics ('re-run an expired plan') plus the explicit instruction to never collect contact details for the call. The three optional attribution params are left to the schema, which already describes them adequately.

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 verb+resource: it 'returns a prefilled TempGuru-owned review form' for a saved non-PII plan_id, and explicitly contrasts with what it does not do (never creates a CRM lead or quote reference). This clearly distinguishes it from siblings like get_quote_status (status checking) and plan_staffing/save_staffing_plan (plan production).

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 an explicit precondition ('Use only after the buyer reviews a staffing plan and asks to proceed'), required input provenance (plan_id from plan_staffing or save_staffing_plan), and explicit when-not branches: give continuation.form_url directly when no plan_id exists, and re-run an expired plan. Concrete alternative actions are given for the key edge cases.

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?

Beyond minimal annotations, the description discloses that the server recomputes rates/totals/lead time/compliance, rejects caller-supplied pricing, and does not reserve staff or submit contact details. It also explains status branching (saved, plan_incomplete, rate_limited, storage_unavailable) and that only 'saved' yields a durable plan_id. This significantly exceeds 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?

Every sentence earns its place and is front-loaded with the core purpose. Although long, the structure flows from purpose to usage conditions to expected inputs/behaviors to status hints, making it dense and valuable 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?

The description is complete for a complex save operation: it explains prerequisites, what the server does, what it does not do, and how to handle each possible outcome via hints. The output schema and sibling context are well supported, leaving minimal ambiguity.

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 each parameter described, so baseline is 3. The description adds important contextual meaning: all inputs must match the previously confirmed plan_staffing result, and the server does not accept pricing or totals. This elevates the score above baseline but doesn't fully redefine parameters 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 the tool saves a complete non-contact staffing plan for 30 days, with the explicit verb 'save' and resource 'staffing plan'. It distinguishes itself from siblings by noting it is used only when plan_staffing returned plan_complete:true without a plan_id, and contrasts with request_quote.

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 and when-not-to-use guidance: 'Use only when plan_staffing returned plan_complete:true without a plan_id' and 'Never call this tool when plan_staffing already returned a plan_id.' It also gives alternative handling for rate_limited or storage_unavailable via continuation.form_url.

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