BackNine Life Insurance

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

Goes well beyond the annotations: it discloses that retries for the same quote reuse the eApp (explaining the idempotentHint), that the returned link is private and must be handed to the user, and critically that the action does NOT complete, submit, or bind coverage. That scope-limitation statement is high-value behavioral context not present in any structured field.

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?

Front-loads purpose in the first sentence, then layers precondition, idempotency, and user-facing instruction in logical order. Slightly long at four sentences, but each carries actionable information rather than 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?

No output schema exists, yet the description explains what is returned (the private apply_link) and what the caller should do with it, and it closes the loop on what the tool does not do. An agent has everything needed to invoke and post-process this call correctly.

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 both parameters are already fully documented in the schema, including the quote_id validity constraints and the user_confirmed meaning. The description echoes the quote_id source without adding format or syntax details beyond the schema, 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?

States a specific verb+resource ('Create an eApp from a quote_id') and names the return value ('private apply_link'), which cleanly separates it from the quote_* siblings that produce quotes and the update_life_eapp_* siblings that mutate an existing eApp.

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 condition: 'Call only after the user selects that quote and explicitly asks to start an application,' and references the public quote tools as the upstream source. It stops short of naming a specific sibling alternative for the update/follow-on scenarios, so context is clear but alternatives are left 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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds genuinely new behavioral context: the operation is anonymous, requires no PII, is restricted to non-annuity product types, and only surfaces enabled products. It does not describe the shape of the response, but the annotation bar is already 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?

Two sentences, tightly front-loaded: scope and returned content first, then the calling constraint. No filler, no repetition of the title 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?

With no parameters and no output schema, the description must describe what comes back, and it does list the return categories (product-specific inputs, solves, health labels, rider filters). It stops short of explaining the structure or how to interpret those categories, a minor gap for a zero-arg discovery tool.

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 is nothing for the description to clarify beyond confirming no input is required, which the anonymity sentence effectively does.

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 ('Get required inputs for ... quotes') and narrows scope precisely ('anonymous US life insurance and long-term-care quotes across enabled non-annuity product types'). An agent can distinguish this requirements-lookup tool from sibling quoting tools like quote_life_insurance without opening a schema.

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 context is clear: this is the prerequisite step that enumerates inputs before quoting, and the negative prerequisites ('no name, email, phone, SSN, account, or application needed') tell the agent when it can safely call it. However, no sibling tool is named as an alternative and there are no explicit exclusions beyond 'non-annuity'.

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 (readOnly, idempotent, openWorld, non-destructive). The description adds meaningful non-annotation context: the $5,000–$100,000,000 coverage range subject to carrier/product limits, the monthly/annual payment constraint, and the caution to never infer eligibility.

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 dense sentences with no filler; the core purpose leads and the coverage/payment constraints follow. Slightly list-like but every sentence carries operational 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?

For an 11-parameter, 7-required quoting tool with no output schema, the description covers scope, constraints, health-value sourcing, and how the returned quote_id flows downstream. It stops short of describing result cardinality or comparative ranking, but is largely 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 64%, and the prose supplements it usefully: it clarifies the coverage range mapped to death_benefit, ties 'mode' to monthly/annual payments, and explains that health is an explicit assumed value sourced from get_life_quote_requirements rather than inferred.

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 final-expense life insurance quotes') with scope detail (burial/funeral coverage). It actively distinguishes itself from siblings by noting 'No term duration', which separates it from quote_term_life/quote_life_insurance.

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 clear downstream context ('each returned quote_id can be used with create_life_eapp after user selection') and points to get_life_quote_requirements for health values. However, it never explicitly states when to prefer this over the sibling quote_life_insurance or quote_term_life beyond the passing 'no term duration' remark.

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 readOnly/idempotent/non-destructive, but the description adds real context: the private-token requirement, the explicit 'does not change the application' guarantee, and the note that returned quote_id selections are bound to this application and its current revision. That revision-binding caveat is non-obvious behavioral detail beyond the annotations.

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 tight sentences, front-loaded with purpose, then auth, then mutation status, then output binding. No filler; each sentence carries a distinct fact, though the density borders on terse.

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 and a large nested `changes` object, the description covers purpose, auth, mutation semantics, and the meaning of returned quote_ids. It omits how saved inputs interact with overrides at field level, but is adequate for correct 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?

With 50% schema description coverage, the schema documents many leaf fields but leaves others bare. The description clarifies the role of the `changes` object as 'optional scenario changes' layered on saved rating inputs, and reinforces that the token is private and not a UUID/eApp ID/completion URL, but adds no field-level syntax or format detail.

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 a specific verb and resource: 'Get fresh non-annuity quotes for the eApp', scoped to eApp-based quoting with saved rating inputs. The 'non-annuity' qualifier implicitly separates it from annuity/final-expense siblings, though no sibling is named explicitly, keeping it short of a 5.

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?

States the auth precondition (requires the private application_token) and that it does not mutate the application, which implies usage. However, it never contrasts with siblings like quote_life_insurance, quote_term_life, or update_life_eapp_quote, so the agent must infer when this eApp-bound re-quote is preferred over a fresh quote.

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, idempotentHint, destructiveHint=false, openWorldHint), and the description adds workflow context beyond them: gating on licensing/enablement, the requirement to fetch requirements first, the prohibition on inferring inputs, and that the call yields signed quote_id selections consumed by create_life_eapp. Failure modes and licensing-error behavior are not described, keeping it below 5.

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?

Front-loaded with scope, then prerequisites, then inputs, then returns — a sensible order. The long product enumeration is justified because it defines coverage, but the paragraph is dense and pushes the returns description into a single run-on clause.

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 45-parameter, deeply conditional tool with no output schema, the description covers scope, prerequisite, input sourcing, and return contents (product details, comparison benefits, exam/application guidance, illustration values, signed quote_id). The remaining gap is that it never explains how to choose between this tool and the overlapping term/final-expense quote siblings.

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 only 40% across 45 parameters, but the description compensates partially: the product list maps natural-language product names to product_type enum values, it flags LTC/chronic-illness rider support (relevant to filter_chronic_illness_and_ltc and ltc_rider_percentage), and it tells the agent to obtain health-value labels from get_life_quote_requirements. It adds no syntax for the many undocumented optional fields.

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 (quote life insurance) and enumerates the exact product set it covers — term, non-med, ROP term, fex, UL, IUL, PIUL, VUL, whole life, linked benefit, LTC, accidental death — so an agent can map a user request to this tool. It also draws a clean scope boundary ('Annuities are excluded'). The product names map directly onto the product_type enum values, which is unusually useful.

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 prerequisite ('Call get_life_quote_requirements first for enabled products and each product's schema/health labels') and an input rule ('Use explicit user inputs'). It also excludes annuities. It does not, however, resolve the overlap with siblings quote_term_life and quote_final_expense, which appear to cover products this tool also lists.

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 carry the safety profile (readOnly, idempotent, non-destructive), and the description adds genuinely new behavior: an opaque quote_id valid for 30 minutes, the shape of returned content (product details, benefit comparisons, application guidance), and a hard instruction not to invent missing inputs or an underwriting class. It does not cover failure modes for out-of-range inputs or rate limits.

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 sentences, each carrying a distinct payload: retrieval semantics, output contents, quote_id lifetime, and the handoff to create_life_eapp. Front-loaded with the core action and free of redundancy, though the closing imperative reads as a rules reminder rather than tool description.

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 11-parameter tool with no output schema, the description usefully enumerates what comes back and flags the 30-minute quote_id constraint that would otherwise trap an agent into reusing stale ids. Optional height/weight behavior and error semantics are left entirely to the schema, a minor gap rather than a blocking one.

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 55%, so roughly half the 11 parameters are undocumented in both places (state, smoker, death_benefit, term_duration carry no prose). The description's contribution is limited to a general 'explicit user-provided' framing plus the no-invention rule; it adds no syntax or format detail beyond what the schema already encodes.

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 specific verb ('Get') and a precisely scoped resource ('term-life quotes'), which separates it from quote_final_expense and quote_life_insurance on product line. It does not name any sibling outright, so an agent must infer the boundary from the product term rather than being told.

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 states the precondition (explicit user-provided rating inputs) and the follow-on action ('After the user selects a quote and requests an application, call create_life_eapp'), which is real routing guidance to a named sibling. What is missing is a when-not case or the contrast against get_life_quote_requirements, which is only implied.

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 only declare readOnly=false, idempotent=false, destructive=false. The description goes well beyond that, disclosing the confirmation requirement, the quote-invalidation side effect, the set of rejected inputs, and an explicit scope boundary (does not submit to carrier or bind coverage). The apply_link hand-off also flags a user-facing follow-up step.

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?

Six sentences, all substantive, with the core action front-loaded before constraints and the closing scope disclaimer. It is denser than strictly necessary - the apply_link sentence and the no-submit disclaimer could be tightened - but nothing is 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 nested-parameter mutation tool with no output schema, the description covers the auth token, confirmation gate, side effects, rejected inputs, and what happens next (hand the user the apply_link). An agent has everything needed to invoke it correctly and set user expectations.

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 75% and the schema already documents most fields, so baseline is 3. The description adds value by naming the accepted field groups (name, email, phone, rating inputs) and enumerating the rejected data classes, and by stressing that the application_token must be the private token rather than an eApp ID/UUID/URL.

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 (Update), a specific resource (the primary or joint insured's name/email/phone/rating inputs), and the target (eApp created through public MCP). It cleanly separates from siblings like quote_life_eapp and create_life_eapp by scoping itself to post-creation insured data edits.

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 names prerequisites (private application_token, explicit user confirmation) and exclusions (SSNs, payment data, medical answers, agent/domain overrides, signed/submitted edits). It also routes downstream behavior: rating changes invalidate the quote and require fresh quotes before selection.

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 declare non-read-only, non-idempotent, non-destructive, but the description goes well beyond them: it discloses that quote history is retained, which inputs are silently applied, the rejection conditions, that the result is a link rather than a completed submission, and that no carrier binding occurs. It also gives explicit hand-off instructions for the private apply_link.

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?

Front-loaded with the core action, then constraints, then the user-facing follow-up. It is on the long side, and the distinction between 'completion link' and 'apply_link' is slightly muddled, but every sentence carries operational value.

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 still explains what is returned (the completion/apply link) and what happens behaviorally on failure. It also clarifies the boundary of responsibility — this action neither submits nor binds — which is exactly the context an agent needs before promising the user anything.

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 supplements it usefully by labeling quote_id as application-bound and application_token as private/required. It reinforces the user_confirmed requirement, though it adds no format or syntax 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 precise verb+resource+scope: select a quote previously returned by quote_life_eapp for the same application, applying its rating inputs. This is clearly distinguishable from sibling tools like quote_life_eapp (produce quotes) and create_life_eapp (create the application).

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 names the prerequisite tool (quote_life_eapp), the required inputs, and the extra condition of explicit user confirmation. It also states when the tool refuses to act (stale selections, locked/submitted applications), which routes the agent away from invalid invocations.

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