FitMyCall — Answering Service Prices

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

The annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is known. The description adds valuable behavioral context: results are sorted by monthly price, verified plans that actually answer phone calls, excludes zero-minute teaser plans, and all prices are human-verified with dates. This goes beyond the annotations without contradicting them.

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 concise sentences. The first sentence states the core purpose and behavior, the second provides usage triggers, and the third confirms verification. Every sentence earns its place, with no wasted words or repetition of schema/annotation 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?

For a comparison tool with 3 optional parameters and no output schema, the description covers the essential aspects: what it returns (cheapest verified plans), how it sorts (by monthly price), and the filter dimensions (budget and type). It also mentions human verification and dates. It doesn't describe result limit or pagination, but for this relatively simple tool, the description is sufficiently 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?

The schema descriptions cover all 3 parameters (100% coverage), so the baseline is 3. The description adds a bit of context by mentioning 'budget and type' which maps to maxMonthlyBudget and type, but doesn't add additional syntax or format details beyond the schema. It adequately compensates without going into great depth.

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 function: comparing answering services and AI receptionists by budget and type, and returning the cheapest verified plans. It uses specific verbs ('compare', 'returns') and identifies the resource (answering services) and scope (budget, type), distinguishing it from sibling tools like get_answering_service_pricing which likely provide individual 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?

The description explicitly tells when to use the tool: 'Use when someone asks "what does an answering service cost", "cheapest answering service", "AI receptionist under $100" or wants options for a small business.' It also excludes zero-minute teaser plans and add-ons from results, but doesn't explicitly name alternatives for when not to use it or what sibling tools to use instead.

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, openWorldHint, and idempotentHint, establishing the safety profile. The description adds meaningful behavioral context: pricing is 'human-verified', includes 'date the pricing was last verified on the vendor’s own site', and is 'updated weekly', which helps the agent trust freshness and coverage. It also discloses the return payload details.

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, front-loaded with the primary action, followed by concrete use cases and scope. Every sentence contributes 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?

For a single-parameter lookup with no output schema, the description explains the return payload (plans with price, minutes, fees, trial, verification date) and coverage scope (21 services, weekly updates). It does not specify behavior for services outside the 21, but given the low complexity, the description is still sufficiently 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?

The only parameter, service, has complete schema coverage with examples. The description reinforces this with its own examples but adds no additional semantic constraints beyond the schema, so the baseline score of 3 applies.

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-resource pair: 'Get current, human-verified pricing for a specific answering service', clearly identifying the action and target. It distinguishes itself from siblings like compare_answering_services by emphasizing 'specific' rather than comparative analysis, and provides concrete service examples.

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 gives explicit trigger phrases: 'Use when someone asks "how much does X cost", "X pricing" or "is X worth it"'. It clearly limits to single-service pricing, implying compare_answering_services for comparisons, though it does not name the alternative directly.

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.