AssistAnt 24/7 pro directory

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context beyond that: each result carries trust tier, distance, phone, profile and Trust Card links, free repair guides are returned first for matching appliance queries, and commercial-only shops are filtered out by default.

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 core purpose is front-loaded in the first clause, followed by the conditional rules. The long inline enumeration of 44 categories overlaps heavily with the schema enum, but it does add human-readable labels the enum lacks, so it mostly earns its space even though the prose is dense.

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 an 8-parameter read-only search with no output schema, the description covers the return payload (trust tier, distance, phone, links), the guide-first behavior, and the commercial filtering rule. Gaps are minor: limit behavior and lang's practical effect are left to the schema.

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?

With 75% schema coverage the baseline is already ~3, and the description goes further by explaining interaction semantics the schema does not: that brand/appliance/problem narrow appliance results and drive guide matching while other trades ignore them, and that segment controls the commercial inclusion rule. It does not explain limit (capped at 10) or lang's effect beyond the schema's own note.

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 opening sentence states a specific verb and resource ('Find local service businesses near a US ZIP code') and enumerates the supported service taxonomy, so the tool's scope is unmistakable. It never names the sibling tools (get_pro_profile, get_town_summary, what_breaks) or contrast itself with them, so it falls short of the 5-level sibling differentiation.

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?

It gives real conditional rules: brand/appliance/problem only narrow appliance-service queries and are ignored by other trades, and commercial-only shops are excluded unless segment is 'commercial'. What is missing is any explicit 'use this instead of X' routing or when-not-to-use guidance relative to the sibling 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 establish readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description goes further by enumerating what the listing contains (services, specialties, trust tier, contact, bookability, nearby alternatives), which is meaningful behavioral context for a read tool.

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?

A single sentence, front-loaded with the purpose before the field enumeration. The colon-list is dense but every item conveys return content, so little 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?

For a simple single-parameter read tool with no output schema, the description compensates by enumerating the returned fields, so an agent knows what it will get back. Only the absence of explicit alternative routing keeps it from being fully 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?

With only one parameter and 0% schema description coverage, the description carries the full burden and does it well: it defines 'slug' as 'the last part of its profile URL'. That format hint is exactly the kind of semantics the bare 'string' schema lacks.

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 a specific verb and resource ('get one business's profile') and pins the lookup key ('by its profile slug'). The singular 'one business' implicitly distinguishes it from the plural find_pros, though no sibling is named outright.

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?

Usage is implied by the required lookup key — you need a profile slug to call it — but there is no explicit when-to-use guidance or reference to find_pros for discovery versus this tool for details. Adequate but leaves the routing decision to inference.

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 cover the safety profile (readOnlyHint=true, openWorldHint=false), so the bar is lower, yet the description still adds real behavioral detail: the 25-mile radius, that counts are computed within that radius, that verified entries carry a date checked, and that results are sorted verified-first. Not stated is pagination or result caps for the list.

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 core operation is front-loaded effectively in the opening clause, but the description then inlines the entire 40-value service enum, which is redundant with the schema and bloats the text. The example-query sentence earns its place; the enum dump does not.

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 carries the burden of explaining returns and does so well: business count, verified count with date, most-named brands, and a verified-first list. An agent knows what it will get back, though pagination/limits on the list remain unspecified.

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% and the service enum is fully self-documenting, so baseline 3 applies. The description clarifies that 'service' accepts any town-page service and that the radius is fixed at 25 miles, but it also re-lists all ~40 enum values verbatim, which adds no meaning the schema does not already carry.

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 — count businesses of one service within 25 miles, count verified ones, surface top brands, and return a list — which is a distinctly richer operation than a plain lookup. However, it never names or contrasts against the sibling find_pros, whose 'list of pros' output overlaps with this tool's final element, so sibling differentiation is left to inference.

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?

It gives concrete routing cues via example questions ('who fixes appliances in Covington, LA?') and a clear selection condition — use this 'when no ZIP is known'. That is a genuine when-to-use signal, but it stops short of naming the ZIP-based alternative or stating when to prefer find_pros 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=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely new behavioral context: the data is derived from real repair records and normalized as a share of diagnosed jobs, and pro_only results carry a safety restriction on DIY advice. It doesn't describe result ordering or how ties/sparse samples are handled.

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 scope and the disclaimers are front-loaded and every clause carries information, but the opening sentence is long and stacks appliance list, brand list, and the share-of-jobs metric into one dense clause. Slightly tighter phrasing would scan faster without losing 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?

With no output schema, the description does the work of explaining what comes back (findings as a share of diagnosed repair jobs) and flags the pro_only safety dimension. That covers the essentials for a read-only lookup; only fine-grained output details like ordering or minimum sample size are absent.

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 67%, and the description meaningfully extends it: it enumerates the accepted brands (samsung, lg, whirlpool, ge, frigidaire, maytag, kenmore, hisense) that the schema only illustrates with one example, and explains that problem takes the customer's own phrasing and scopes results to that complaint.

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 precise verb+resource: it returns what technicians actually found when diagnosing specific appliances, expressed as a share of diagnosed repair jobs from real repair records. It names the exact appliance set and brand set, and explicitly disclaims what it is not ("It is not a reliability ranking"), which separates it from the pros-oriented siblings.

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?

It gives explicit triggering questions ("what usually goes wrong with my Samsung fridge?") and shows the alternative invocation shape when the problem parameter is supplied ("my Samsung fridge is not making ice, what is it usually?"). It also states a clear usage boundary: pro_only findings are gas/240V/sealed-refrigerant jobs and must not be offered as DIY.

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