FitMyCall — Answering Service Prices

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds valuable behavioral context beyond these: it 'returns the cheapest verified plans that actually answer phone calls, sorted by monthly price, excluding zero-minute teaser plans and add-ons', and notes that prices are human-verified with dates. This helps the agent set expectations about filtering and quality without being unnecessarily verbose.

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 primary purpose and uses three concise sentences. The list of example queries is slightly long but each item is specific and aids the agent in recognizing matching user intents. No fluff; every sentence contributes useful 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 no output schema, the description provides a clear sense of return behavior: cheapest verified plans sorted by monthly price, excluding teaser plans. It also covers typical use cases and quality filters. It does not detail the exact fields in the response, but for a comparison-list tool with 3 optional params, it is reasonably complete.

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% (all 3 parameters have descriptions), so the baseline is 3. The description reinforces that budgeting and type are core filter dimensions but does not add extra detail about parameter syntax or value interpretation. It adequately aligns with the schema, but adds no additional semantic value beyond what's already present.

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 a specific verb 'Compare' with resource 'answering services and AI receptionists' and specifies dimensions (budget, type). It differentiates from siblings by focusing on comparing costs and plans, while 'get_answering_service_pricing' likely retrieves raw pricing and 'recommend_answering_service_for_industry' is industry-specific. The use-case list further clarifies the tool's unique intent.

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 provides numerous 'when to use' triggers: questions about cost, cheapest service, AI receptionist under $100, comparison with in-house staff, etc. This gives clear context but does not explicitly mention when *not* to use the tool or name alternative sibling tools, so it does not earn a 5.

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 cover read-only/idempotent/closed-world, and the description adds genuinely useful context beyond them: 22 services covered, weekly refresh cadence, pricing verified against the vendor's own site, and that the response includes a last-verified date. It stops short of describing pagination or response structure depth, but the freshness/coverage notes are substantive.

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 the capability, immediately followed by return contents and then trigger phrases. Slightly redundant between 'human-verified' and the later 'date the pricing was last verified', but no sentence is wasted.

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?

With no output schema, the description fully carries the burden by enumerating returned fields (plans, monthly price, included minutes, setup fees, overage, free trial, verification date). For a one-parameter read-only lookup, nothing an agent needs to call it correctly 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?

Schema coverage is 100% and the single 'service' parameter already carries example values, so the baseline is 3. The description reinforces accepted naming by listing the same example vendors and noting the 22-service universe, but adds no guidance on aliases or handling of unmatched names.

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?

Clear specific verb+resource ('Get current, human-verified pricing for a specific answering service') with concrete example vendors. It implicitly carves out a niche versus compare_answering_services by framing itself as a single-service lookup, but never names that sibling, so an agent must infer the boundary.

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?

Strong trigger phrasing is quoted verbatim ('how much does X cost', 'X pricing', 'is X worth it'), which maps directly to user intent. It does not state when not to use it (e.g. multi-service comparisons) or that compare_answering_services should be used instead for side-by-side analysis.

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 idempotentHint=true, so the agent knows it is safe. The description adds that the tool produces both monthly and yearly estimates and evaluates price tiers, which is behavioral context beyond the schema.

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 three sentences with each serving a distinct purpose: stating the purpose, giving usage triggers, and listing inputs. It is concise with no redundant wording or repetition of structured data.

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 a moderate parameter count and no output schema, the description adequately covers inputs, decision context, and output types (monthly/yearly loss and price tier). The usage triggers and input list make it sufficient for agent invocation.

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 lists input names and notes 'optional booking rate,' which marginally reinforces schema optionality, but it does not add substantive semantics beyond what the schema already provides.

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 ('calculate') and clearly names the resource (missed phone calls) and outputs (monthly/yearly loss and recommended price tier). It distinguishes itself from sibling tools by focusing on loss calculation rather than service comparison or pricing lookup.

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 trigger scenarios ('when someone mentions missing calls, going to voicemail, losing leads after hours, or asks whether an answering service is worth it'). It does not name alternative tools or exclusion conditions, but the usage context is clear.

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 read-only and idempotent behavior. The description adds rich detail about the output: the missed-call problem, human/AI/hybrid winner, matching plans with prices, and a vendor question. This goes beyond the annotations and sets expectations for what the recommendation contains.

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, front-loaded with purpose immediately. The industry list adds specificity without bloat, and the usage trigger is compact. Every word 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 tool with no output schema, the description explains return values well (problem, winner, plans/prices, question). It covers the key user intent and usage scenario. Minor gap: no mention of how budget affects output, but overall complete for its 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 coverage is 100%, with both parameters already described. The description adds industry examples but does not mention the optional budget parameter or how it affects results. It provides marginal value beyond the schema, so a baseline of 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 action (recommend) and the resource (answering service or AI receptionist) for a specific type of business. It lists concrete industry examples and differentiates from siblings by focusing on business type rather than comparison, pricing, or ROI.

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 'Use when someone says what business they run and asks what to get,' providing a clear trigger. However, it does not mention exclusions or alternative tools, so it stops short of offering 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.