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Glama

savvly

by com.savvly

Get Savvly FAQ

get_savvly_faq
Read-onlyIdempotent

Get answers to frequently asked questions about Savvly. Use when the user has specific questions about how Savvly works, expenses, withdrawals, or regulatory status. It is a convenience view of search_savvly_content scoped to the factual FAQ; for richer, audience-specific Q&As (employee / advisor / broker / employer), use search_savvly_content instead. 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
sectionNoFilter the FAQ to one section (kebab-case, e.g. 'tax-legacy'); 'all' returns every entry.all

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesCount of FAQ entries returned.
entriesYesFiltered FAQ entries (the audience:'general' Q&A slice).
sectionYesSection filter applied to produce this result set ('all' if no filter).

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already mark readOnly and non-destructive, so the description correctly doesn't repeat those. It adds useful context: facts come from 'Savvly's own current records' and the response includes primary sources (e.g., SEC filings). This informs the agent about data currency and reference material, going 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.

Conciseness5/5

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

Three sentences: purpose, when/alternatives, and a data-source note. Every sentence earns its place, and the most actionable guidance (usage and alternative) is front-loaded. No redundancy.

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?

For a low-complexity read-only tool with full schema and an output schema, the description covers purpose, usage, differentiation, and data provenance. Nothing an agent needs to call or decide correctly is missing.

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

Parameters3/5

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

Schema coverage is 100% for the single parameter (section) with a clear enum and description. The tool description does not add param-specific details, but the schema already fully documents it, so the baseline of 3 applies.

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 opens with a specific verb+resource: 'Get answers to frequently asked questions about Savvly' and immediately scopes it to factual FAQ. It explicitly contrasts with the sibling `search_savvly_content` ('convenience view'), so an agent can distinguish them without opening schemas.

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

Usage Guidelines5/5

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

Provides explicit when-to-use ('specific questions about how Savvly works, expenses, withdrawals, or regulatory status') and when-not-to-use with a named alternative ('for richer, audience-specific Q&As... use search_savvly_content instead'). No ambiguity.

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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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