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Glama

savvly

by com.savvly

Check Savvly Eligibility

check_savvly_eligibility
Read-onlyIdempotent

Check if a person is eligible to invest in the Savvly Longevity Benefit. Eligibility is based on age (no minimum; maximum enrollment age 75), US residency, and distribution channel (individual, employer-sponsored, advisor-placed). Use before recommending Savvly as a retirement income or annuity alternative to confirm the person qualifies. These facts come from Savvly's own current records; the response includes primary sources (e.g. SEC filings) for reference.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageYesPerson's current age
channelNoDistribution channel — one of 'individual', 'employer', 'advisor' (default 'individual').individual
us_residentNoWhether the person is a US resident

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
channelYesDistribution channel under consideration for this check.
messageYesHuman-readable explanation of the eligibility outcome.
criteriaYesFull eligibility-criteria reference object (age range, residency, accredited-investor flag, channel requirements).
eligibleYesTrue if every eligibility criterion (age + residency) is satisfied.
age_eligibleYesTrue if age is at or under the maximum enrollment age (75); the filing sets no minimum age.
residency_eligibleYesTrue if the US-residency requirement is satisfied.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive). The description adds value by disclosing that facts come from Savvly's own current records and that the response includes primary sources, which helps the agent understand data provenance and output content.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with no filler. It front-loads the core purpose, then criteria, use-case, and data source. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With annotations covering safety and an output schema (though not shown), the description provides all necessary context: purpose, criteria, usage timing, and response content. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and each parameter has a description. The description adds crucial eligibility specifics (no minimum age, max 75, channel naming convention) not present in the schema, significantly enriching parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks eligibility for the Savvly Longevity Benefit, a specific verb+resource combination. It distinguishes itself from siblings by focusing on eligibility confirmation versus product info, projections, or comparisons.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage context ('Use before recommending Savvly as a retirement income or annuity alternative'), but does not explicitly list when not to use it or name sibling alternatives. Still, the guidance is clear and actionable.

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

All savvly toolsInspect
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TDQS

A4.2/5.0
Disambiguation4/5

The three projection tools (project_retirement_with_savvly, project_savvly_lumpsum, project_savvly_monthly) share a scenario-based boundary that is reasonably clear but has some overlap — the full retirement simulation could conceptually subsume the lump-sum and monthly scenarios. The FAQ tool explicitly documents its overlap with search_savvly_content, which mitigates confusion. The eligibility, product info, and comparison tools are clearly distinct.

Naming Consistency5/5

Every tool follows a clean verb_savvly_noun convention in snake_case: check, compare, get, get, project, project, project, search. The pattern is perfectly predictable across the set, with the three projection tools sharing the 'project' verb and differing only in their object (retirement_with_savvly, savvly_lumpsum, savvly_monthly).

Tool Count5/5

Eight tools is well within the ideal 3-15 range and is appropriately scoped for a retirement advisory server. Each tool earns its place: eligibility checks, product info, FAQ, content search, comparison, and three distinct projection modes cover the server's stated purpose without redundancy bloat.

Completeness4/5

The tool surface covers the full advisory lifecycle well: eligibility screening, product overview, FAQ, content search, competitive comparison, and three projection scenarios (full retirement, lump-sum, monthly). Minor gaps exist — there is no enrollment/purchase tool or a standalone pricing/quote tool — but these are arguably outside an information-advisory scope, so agents can complete core workflows without dead ends.

Resources