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Savvly

savvly-mcp

by Savvly
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Check Savvly Eligibility

check_savvly_eligibility
Read-onlyIdempotent

Verify a person's eligibility for the Savvly Longevity Benefit by checking age, US residency, and distribution channel before recommending it as a retirement income option.

Instructions

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv1.0.92
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / age / maximum
      Added value: +9007199254740991
    • addedInput schema / properties / age / minimum
      Added value: +-9007199254740991
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  2. Changed1 schema field changedv1.0.86
    • changedInput schema / properties / channel / description
      Previous value: -"Distribution channel"New value: +"Distribution channel — one of 'individual', 'employer', 'advisor' (default 'individual')."
  3. Changed1 schema field changedv1.0.69
    • changedOutput schema / properties / age_eligible / description
      Previous value: -"True if age falls within the 25–75 minimum/maximum range."New value: +"True if age is at or under the maximum enrollment age (75); the filing sets no minimum age."
  4. Changed1 schema field changedv1.0.39
    • changedOutput schema / properties / age_eligible / description
      Previous value: -"True if age falls within the 25–79 minimum/maximum range."New value: +"True if age falls within the 25–75 minimum/maximum range."
  5. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the agent knows this is a safe read operation. The description adds valuable behavioral context beyond that: it states the data source ('Savvly's own current records') and that the response includes primary sources (e.g., SEC filings). This clarifies what kind of information the tool returns and where it originates, which is not covered by annotations.

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?

The description is two sentences with zero fluff. The purpose is stated first, followed by criteria and usage guidance. Every sentence earns its place, and the structure is front-loaded with the core action.

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

Completeness4/5

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

Given the tool's modest complexity, the description covers the purpose, usage, eligibility criteria, and data source. The output schema exists, so the return format is already documented. It doesn't mention error cases or edge conditions, but for an eligibility checker, the description is sufficiently complete for an agent to call it correctly.

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%, so the schema already documents each parameter. However, the description adds significant meaning by specifying the exact eligibility rules: 'no minimum; maximum enrollment age 75' for age, and it clarifies that residency and channel are part of the eligibility criteria. This goes beyond the schema's generic descriptions (e.g., 'Person's current age') and helps the agent understand how to set the parameters correctly.

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's function: 'Check if a person is eligible to invest in the Savvly Longevity Benefit.' It specifies the resource (Savvly Longevity Benefit) and the verb (check eligibility), and it enumerates the criteria (age, US residency, channel). This unmistakably distinguishes it from sibling tools like get_savvly_product_info or project_savvly_monthly, which handle different aspects of Savvly.

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 an explicit usage context: 'Use before recommending Savvly as a retirement income or annuity alternative to confirm the person qualifies.' It clearly tells when to invoke this tool. It doesn't explicitly mention when not to use it or point to alternatives, but the context is sufficient for an agent to know it's a prerequisite check.

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

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