Alishahi Insurance

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

Annotations already declare the write/safe/idempotent profile, and the description adds genuinely new behavioral info: that it returns the updated application including rejected answers and the reason for rejection. That is valuable because there is no output schema. It omits auth/token requirements, but the annotation bar is met.

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?

Three short sentences, front-loaded with the action and followed by the content constraint and the return behavior. No filler.

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 5-parameter mutating tool with no output schema, the description covers purpose, answer-selection guidance, and return content, which is close to sufficient. The gap is the unaddressed token/application_id parameters and the submit-vs-save distinction.

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 60%; the schema itself documents 'answers', 'submit', and 'notes', while 'token' and 'application_id' are undocumented. The description reinforces answer semantics ('only send confirmed, leave unknown out') but adds nothing about application_id, token, or the submit flag.

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 and resource: sending a person's answers to an application that Sam Alishahi started. It is clearly distinguishable from get_application and submit_quote_request, though it does not explicitly name siblings or contrast with them.

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 a content rule ('only send what the person told you or confirmed; leave unknown fields out') but says nothing about when to use this versus submit_quote_request or when the 'submit' flag should be set. Usage is implied rather than 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 declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false. The description adds real context beyond that: the data provenance (state regulators' public company lists) and an important completeness caveat that premium rank is only available 'where states publish every company'. It also notes the citable page.

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 with a colon-delimited list of returned data points. Dense but every clause carries information; no filler.

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?

There is no output schema, so the description must describe returns, and it does enumerate them (admission, surplus-lines eligibility, licensed lines, premium rank, cite page). Data provenance is included. Minor gap: no indication of matching behavior when a partial name is ambiguous.

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 query parameter ('Company name (part is fine) or 5-digit NAIC number') is already fully documented. The description repeats the name/NAIC duality without adding syntax or format detail beyond the schema, so baseline 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?

States a specific verb (look up) and resource (U.S. P&C insurance company) plus the two lookup keys (name or NAIC number). The scope is company-level, which distinguishes it from state-level siblings like insurance_companies_in_state and who_writes_line_in_state.

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 'look up ... by name or NAIC number', but there is no explicit when-to-use guidance and no named alternatives, even though several siblings (insurance_companies_in_state, who_writes_line_in_state) overlap in domain. The agent must infer routing.

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, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds real context beyond that: it states the credential requirement (token from the reply email) and characterizes the response as full field-by-field state including allowed choices, which helps the agent know what it will get back.

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, no filler, front-loaded with the core action before the credential-finding detail. Every clause carries information the agent needs.

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 correctly carries the burden of describing the return: fields, allowed choices, and completion state. For a two-parameter read tool with safety annotations already in place, that covers what an agent needs, with only token format/error behavior left implicit.

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 0%, so the two parameters are documented only in prose. The description does tie application_id to the 'Reference APP-<id>' in the email and places the token in the same message, which is genuinely useful, but it never explains what the token is or any format constraint, so it only partially compensates for the coverage gap.

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 gives a specific verb and resource ('Read a quote application') and even enumerates the payload (every field, allowed choices, filled vs. required), so the intent is unmistakable. It never names a sibling tool, but the read semantics contrast clearly with complete_application and submit_quote_request, so only the explicit differentiation is missing.

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: you call this to inspect an application's state before acting on it, and the description supplies the prerequisite that application_id and token come from Sam's reply email. There is no explicit when-to-use/when-not statement and no pointer to complete_application or the other siblings, so guidance stays implicit.

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?

The readOnlyHint/destructiveHint annotations already establish the safety profile, so the bar is lower. The description does add something beyond them by sketching the returned content and flagging optionality, but 'All are optional' is ambiguous — it is unclear whether it refers to the questions, the answers, or the returned fields, which muddies rather than clarifies behavior.

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 short sentences with the core purpose front-loaded and no filler. The trailing sentence 'All are optional' is terse and cheap to read, though its ambiguity costs it the top score.

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 one-parameter read tool with annotations covering safety, the description is adequate: it says what is fetched and roughly what comes back. With no output schema, however, the return contract stays vague and the 'All are optional' claim is never resolved, so an agent cannot fully anticipate the response.

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 there is a single required parameter whose schema text already explains that the value comes from list_coverages with examples. The description's 'for one coverage' is a weaker restatement of that, so the baseline 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?

States a specific verb+resource (get quote questions) scoped to a single coverage, and even sketches the return shape (names, types, allowed choices). It does not name or contrast with siblings, but none of the listed siblings (list_coverages, submit_quote_request, etc.) overlap in purpose, so the agent can still disambiguate.

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 explicit when-to-use statement, no mention of the ordering relative to list_coverages or submit_quote_request, and no exclusions. Usage is only implied by the phrase 'for one coverage', leaving the agent to infer the workflow context.

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 destructiveHint=false, so the safety profile is covered, and the description's 'on a state's regulator lists' is consistent with openWorldHint=false. Beyond that it adds genuine domain context: what the two list types mean (admitted/licensed vs. eligible surplus lines) and which fields come back (NAIC numbers, licensed lines), which matters since there is no output 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?

Two sentences, no filler: the first establishes scope and returned content, the second lists the optional filters. Everything is front-loaded and each clause 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?

With no output schema, the description usefully names the returned fields (NAIC numbers, licensed lines) and clarifies the two registry types, which is the main thing an agent needs. Minor gaps remain around result size, pagination, and whether multiple states can be queried, but nothing essential is missing for a simple read-only lookup.

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 each parameter documented and the status enum explained inline, so the schema does the heavy lifting. The description restates the same filter semantics ('ad'/'sl', line of business) without adding syntax or behavior beyond what the schema already provides, which is the baseline 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 gives a specific verb and resource ('List the property and casualty insurers on a state's regulator lists') and even names the two registry categories it draws from (admitted and surplus lines). It does not, however, differentiate itself from the sibling who_writes_line_in_state, which overlaps heavily given the shared line-of-business filter.

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: an agent can infer this is for looking up carriers in a state, and the optional status/line filters suggest narrowing. There is no statement of when to prefer this over who_writes_line_in_state or find_insurance_company, and no prerequisites or exclusions are given.

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, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds useful value beyond that by disclosing what the call returns — each coverage paired with a link to its page — which matters since there is no output 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?

One sentence, front-loaded with the resource being listed and ending with the return-shape detail. No filler, no 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 zero-parameter, read-only listing tool with full annotation coverage and no output schema, the description supplies the essential missing piece (what is returned). It could be stronger by noting it is the entry point for the quote flow, but nothing required to call it correctly is 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?

The tool takes zero parameters, so the baseline is 4; there are no parameter semantics for the description to clarify or omit.

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 gives a specific verb and resource ('List the insurance coverages Alishahi Insurance quotes') plus the returned payload ('a link to each coverage's page'). It is clearly distinct from the sibling quote-application tools in practice, though it does not explicitly name or contrast them.

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: an agent can infer this is the discovery/browse step that precedes a quote flow, but the description never states when to call it, whether it should precede complete_application or get_quote_questions, or any prerequisites. No alternatives or exclusions are named.

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=false, openWorldHint=true, and non-idempotent, so the write/external-party profile is covered. The description adds valuable behavior beyond that: a real-world human broker will follow up to contact the person, and the data-quality rule 'use real values; if a field is unknown, leave it out.'

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?

Three short sentences, front-loaded with the action and recipient, then the consent precondition, then the data-quality rule. No filler; every sentence carries distinct 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?

For an 8-parameter write tool with a nested answers object and no output schema, the description covers purpose, consent gating, and value sourcing. It relies on schema descriptions to reference get_quote_questions and list_coverages for the nested answers/coverage fields, which is a minor gap but reasonable given full schema coverage.

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%, so the baseline is 3 and the schema documents each field. The description adds a real semantic rule not present in the schema – supply only genuine values and omit unknown fields – which affects how the agent populates the 8 parameters.

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 (send a quote request) and names the recipient, Sam Alishahi, as a licensed independent insurance broker. This is clearly distinct from siblings like complete_application or get_quote_questions, which are retrieval/completion tools rather than submission tools.

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?

Gives an explicit gating precondition: 'Only call after the person has confirmed they want to be contacted.' This is a strong when-to-use signal. It doesn't explicitly name alternative tools, but the siblings are not substitutes, so the guidance is essentially complete.

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?

Beyond readOnlyHint/destructiveHint=false, it discloses the evidentiary basis (licensing, premium rank, published appetite rules) and two important caveats: results are not a price or guarantee, and a listed company isn't necessarily one Sam is appointed with. That is meaningful context the annotations do not carry; only rate/latency or result-size behavior is unaddressed.

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 function and output shape are front-loaded in the first sentence, with caveats and next step after. It is a dense run-on paragraph, and the Alishahi/Sam branding detail is slightly heavier than needed, but every clause carries usable information.

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 specifies the return shape: ordered tiers, a one-line justification per company, and a closing next-step recommendation. An agent can both call it correctly and interpret the result without further documentation.

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%, so the baseline is 3, but the description adds real meaning by enumerating the kinds of facts to pack into the scenario string (claims, cancellations, home/roof age, business type, hazards) and by noting state is optional ('if known'). That exceeds what the schema text conveys.

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 transformation: takes a plain-English risk description and returns an ordered list of likely insurers (admitted, then surplus/specialty, then market of last resort) with a cited reason each. The ordering logic and per-item evidence make it clearly distinct from flat-listing siblings such as insurance_companies_in_state or who_writes_line_in_state.

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?

Makes the input conditions explicit (what, where, and underwriter-relevant facts: claims, cancellations, roof age, business type, hazards) and sets a clear boundary with 'Never a price or a guarantee.' It does not name an alternative sibling to use instead when the agent just wants a raw company list, so it falls short of 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 the read-only, non-destructive, closed-world profile. The description adds real value beyond them by disclosing that the data source varies by state (regulator-published premium data in CA/TX/IL/NC/SD/WI versus a licensed-company list elsewhere) and that the state/line directory page is the citable source.

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?

Purpose is front-loaded and the two sentences carry distinct jobs (what it returns, when to use it). The first sentence is dense with a parenthetical state list, 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?

For a two-parameter read tool with no output schema and full annotation coverage, the definition supplies the ranking logic, the source variance, and the citation guidance. A brief note on result ordering or coverage gaps for the non-publishing states would make it 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%, with both 'state' and 'line' documented including line-of-business examples, so the baseline is 3. The description reinforces the state+line pairing but adds no syntax, format, or valid-value detail beyond the schema.

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 resource (insurers writing a given line in a given state) plus the ranking mechanism (direct premium written, falling back to licensed companies), which cleanly separates it from the sibling insurance_companies_in_state.

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 frames the use case: 'tell someone which insurers are real markets for their need in their state before sending a quote request.' It gives clear context but never names or excludes the nearest alternative (insurance_companies_in_state), so the routing is inferred rather than stated.

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