Roome Land Surveying

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 that the content is a fixed set of company contact facts, but says nothing about caching, freshness, or response shape. Reasonable but not rich.

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 front-loaded sentence enumerating exactly what comes back, with no filler or redundant restatement of the title.

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 and no parameters, the description carries the burden of explaining return content and does so by listing the fields. Minor gap: no indication of format or whether hours are structured or free text.

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 tool takes zero parameters, so the baseline is 4; the description correctly implies no inputs are needed by presenting this as a static lookup. Nothing more is required.

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?

Names the specific resource (phone, office hours, office/mailing addresses, email routing) rather than restating the title, so an agent can tell it apart from informational siblings like service_area or list_services. It stops short of explicitly contrasting itself with request_quote, but the resource is unambiguous.

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 only implied: the mention of 'which email to use for quotes, billing or anything else' hints that this tool routes the agent toward contact channels rather than performing the action. No explicit when-to-use or when-not-to-use guidance versus request_quote or the other info 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 openWorldHint=false, so the safety profile is covered. The description adds useful return-shape detail (each service carries a slug and a roomeinc.com page), but says nothing about ordering, caching, or how many services exist.

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 tight sentence with no filler, and the key downstream use (slug for request_quote) is front-loaded alongside the resource itself. Being a fragment rather than a full sentence does not hurt 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 zero-parameter, read-only list tool with no output schema, the description does the necessary work of naming the two fields each item carries (slug and page URL). Little more could be added without a return 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?

The tool takes zero parameters, so there is no parameter semantics to explain; baseline 4 applies. Schema coverage is moot for an empty properties object.

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?

It clearly states the resource returned: the catalog of land surveying services offered, with each entry's slug and website page. The verb is only implied (a noun phrase rather than 'List...'), but the resource is unambiguous and tied to the request_quote use case.

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 rather than stated: mentioning 'its slug for request_quote' hints this is the lookup source for request_quote, but it never explicitly says when to call this versus which_survey or service_area.

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?

Adds genuinely non-obvious behavior beyond the annotations: submission is a double opt-in flow ('Nothing reaches Roome until the person confirms the request from an email Roome sends them'), so a submit is not yet a delivered lead, and the recipient is a third party. Annotations only say it is a non-idempotent, non-destructive, open-world write; the description explains the actual lifecycle, which is the key thing an agent needs before telling a user their request was 'sent'.

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?

Four short sentences, front-loaded with the action and destination, then the geographic restriction, the confirmation behavior, and cost. Every sentence carries a distinct fact the agent cannot get from the schema or annotations.

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 7-parameter write tool with full schema coverage, an explicit annotation set, and no output schema, the description supplies what structured data cannot: eligibility, cost, recipient, and the post-call confirmation step. Nothing an agent needs before invoking it 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 description coverage is 100% and each of the 7 fields is documented in the schema, so the baseline of 3 applies. The description adds no field-level meaning (no note on what makes 'needs' acceptable, or how 'service' relates to which_survey) beyond 'the details the person gives'.

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 concrete verb and resource ('Sends a land survey quote request to Roome Land Surveying') plus the entity it targets, so the agent knows exactly what will happen. It is clearly separable from the read-only informational siblings (service_area, what_to_expect, which_survey, contact_details) because it is the only one that submits a request.

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?

'Properties in Texas only' gives an explicit eligibility boundary, and 'Quotes are free' clarifies that no cost commitment exists for the requester. The description never names the sibling it supersedes at a decision point (e.g. use which_survey first to pick a service, list_services for slugs), so the agent must infer the sequencing from the schema.

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 the safety profile (readOnlyHint=true, openWorldHint=false), so the description only needs to add calling context. It adds the source of truth (roomeinc.com service area page) but says nothing about what happens with no arguments, both arguments, or a negative result.

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?

It is a single short sentence with no filler, but it is grammatically incomplete and not front-loaded with an action verb, which slightly hurts scannability even though its length is appropriate.

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 should say what the check returns (boolean coverage, a list of served cities, a redirect to the site), and it does not. For a zero-required-parameter lookup tool, an agent cannot tell what a valid call or a meaningful response looks like.

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 per-parameter descriptions ('A Texas city, for example Plano'), so the schema carries the parameter burden. The description adds no meaning beyond that, which is the baseline 3 case.

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 identifies the resource (Texas county/city service-area coverage for Roome Land Surveying) and the data source, but it is phrased as a fragment ('Whether...') rather than a verb+resource statement, so the agent must infer that this is a lookup/check tool. It does distinguish itself from siblings like list_services by naming the geographic 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?

There is no guidance on when to call this versus the five sibling tools (e.g., which_survey, request_quote). Nothing states prerequisites, or which of city/county to supply, so an agent has no routing signal beyond the topic match.

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 safety is covered; the description adds real value by enumerating the four content blocks returned (quote, duration, survey day, payment) — meaningful disclosure given there is no output schema. It stops short of saying whether the response is static marketing copy or varies by service slug beyond the added steps.

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, content scoping first, parameter guidance second — appropriately sized with no filler. The opening clause 'What happens when you ask Roome Land Surveying for a survey' is slightly padded but frames the domain usefully.

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 optional-parameter read tool with annotations and no output schema, the description covers content and parameter effect but omits sibling routing guidance (when which_survey or request_quote is the better call) and says nothing about response shape or length. Adequate, with noticeable gaps.

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 description still contributes beyond it: it explains the effect of supplying the slug (adds 'what you receive' and step-by-step for that service), not just its type. It does not repeat the list_services provenance that 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 makes clear this tool returns the 'what to expect' narrative for a survey engagement — the free quote, survey duration, survey day, and payment — which is specific content an agent can distinguish from list_services or request_quote. It lacks an explicit action verb like 'get/returns', so purpose is inferred from the enumeration rather than stated 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 only implied: 'Give a service slug to add what you receive and the steps for that survey' tells how to widen the response, but never says when to call this versus which_survey or contact_details. There are no exclusions or prerequisites stated.

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 the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds genuinely new behavioral context: results are ordered best match first and each carries common uses and audience. It stops short of saying how many services come back or what happens with a vague situation input.

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, zero filler. The result shape and ordering come first, and the usage trigger follows, so an agent gets the essentials in the first read.

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 usefully compensates by describing the return content and ordering. For a one-parameter read-only lookup this is nearly complete; only the cardinality of returned matches and handling of off-topic input are unaddressed.

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 parameter's description already gives two concrete examples, so the schema does the heavy lifting. The description's phrase "a person's situation" reinforces the free-text, in-their-own-words intent but adds nothing about length, ambiguity handling, or constraints.

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 names a concrete task: matching a person's situation to one of Roome Land Surveying's services, ranked best-match-first, and states what each result includes (common uses, who it's for). That distinguishes it in spirit from siblings like list_services (enumerate) or request_quote (act), but it never names an alternative explicitly.

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?

"Use it when someone doesn't know which survey they need" gives a clear triggering condition tied to the user's state of uncertainty. There are no explicit exclusions or named alternatives, so the agent must infer that a user who already knows the service should skip this.

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