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savvly

Search Savvly Q&A Content Library

search_savvly_content
Read-onlyIdempotent

Search the Savvly Q&A Content Library — audience-tagged questions and answers compiled from Savvly's marketing collateral plus the factual FAQ, organized by stakeholder (employee, advisor, broker, employer, universal, general) and section (kebab-case slugs, e.g. 'tax-legacy', 'retention-talent-strategy', 'implementation'). Use this when the user asks about Savvly's positioning, value props, audience-specific talking points, or Q&A-style messaging. Each entry carries the verbatim answer plus any disclaimer footnotes attached to it in the source. 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
limitNoCap on matched entries returned. Default 20, max 50.
queryNoFree-text search over questions, answers, and footnotes (case-insensitive). Multi-word queries match entries containing ANY of the words, ranked by relevance.
sectionNoSubstring match against section slugs (e.g. 'tax', 'retention', 'eligibility'). Case-insensitive.
audienceNoRestrict to one stakeholder audience. Use EXACTLY one of: 'employee', 'advisor', 'broker', 'employer', 'universal', or 'general' ('general' is the factual FAQ). Omit to search across all audiences.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
entriesYesMatched Q&A entries.
matchedYesCount of entries matching the supplied filters.
filter_appliedYesEcho of the filters that produced this result set.
total_in_libraryYesTotal Q&A entry count in the library across all audiences.
available_sectionsYesSection slugs available within the (optionally) selected audience.

TDQS

A4/5.0
Behavior4/5

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

Annotations already specify readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering the safety profile. The description adds valuable behavioral context beyond that: it notes each entry contains the verbatim answer plus disclaimer footnotes, that facts come from Savvly's current records, and that the response includes primary sources (e.g., SEC filings). This supplements the annotations with specific detail about the tool's output and provenance, so a 4 is justified.

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

Conciseness4/5

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

The description is a single paragraph, front-loaded with the core purpose, then adding organizational details, usage guidance, content structure, and source provenance. Each sentence contributes value: the kebab-case slug examples and the note about primary sources are helpful but not essential. It is somewhat verbose but well-structured and not redundant, so a 4 is fitting.

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?

The description is comprehensive for this tool's complexity. It explains what the tool searches, how it is organized, when to use it, what each entry contains (verbatim answer + disclaimers), and the source of the data. Since an output schema exists, return values need not be described. The description covers all necessary context for an agent to decide when and how to invoke it correctly, so a 5 is warranted.

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 description coverage is 100%, meaning each parameter (limit, query, section, audience) is fully described in the schema with types, defaults, and helpful notes. The description does not add significant additional meaning beyond the schema—it only reiterates the concept of audience and section slugs. With full schema coverage, the baseline is 3, and the description adds only marginal context (e.g., examples of section slugs), so a 3 is the correct score.

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

Purpose4/5

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

The description clearly states the tool searches a Q&A content library with specific organization (audience, section). It gives a strong verb-resource pairing and specifies the nature of the content (audience-tagged questions/answers, marketing collateral + FAQ). However, it does not explicitly differentiate from the sibling tool get_savvly_faq, which likely overlaps in purpose. Therefore it does not fully distinguish from siblings, warranting a 4 rather than 5.

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 provides explicit usage guidance: 'Use this when the user asks about Savvly's positioning, value props, audience-specific talking points, or Q&A-style messaging.' This tells the agent exactly when to use it. However, it does not mention when NOT to use it or suggest alternatives (like get_savvly_faq) for other cases. It gives clear context without exclusions, so a 4 is appropriate.

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.