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?

While annotations already declare readOnlyHint and idempotentHint, the description adds valuable context beyond them: data is 'human-verified', 'updated weekly', and includes the 'date the pricing was last verified'. It also enumerates the exact return fields (monthly price, included minutes, setup fees, overage rates, free trial), giving the agent a clear picture of the output.

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 and well-structured: it states the core function, lists return fields, provides usage triggers, and mentions coverage/frequency in four efficient sentences. 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?

Given a simple one-parameter tool with no output schema, the description covers the essential context: what it does, what it returns, when to use it, and data freshness. Annotations cover safety, so nothing critical 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% for the single 'service' parameter, with clear examples in the schema itself. The description provides examples too, but does not add new semantic details beyond what the schema already offers, so the 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 opens with a specific verb ('Get') and resource ('current, human-verified pricing for a specific answering service'), directly distinguishing it from siblings like compare_answering_services. It also cites concrete service examples, making the tool's scope unmistakable.

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 trigger phrases ('how much does X cost', 'X pricing', 'is X worth it') that signal when to use this tool. However, 'or asks for alternatives to a named service' is slightly ambiguous because this tool returns pricing for one specific service, not alternative recommendations—the sibling compare tool might be more appropriate for that use case.

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.