savvly-mcp

Overview Schema Source Related Servers Discussions Admin

Get Savvly FAQ

get_savvly_faq

Read-onlyIdempotent

Get direct answers to frequently asked questions about Savvly's features, expenses, withdrawals, and regulatory status, with primary sources for reference.

Instructions

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

Name	Required	Description	Default
`section`	No	Filter the FAQ to one section (kebab-case, e.g. 'tax-legacy'); 'all' returns every entry.	all

Output Schema

TableJSON Schema

Name	Required	Description
`total`	Yes	Count of FAQ entries returned.
`entries`	Yes	Filtered FAQ entries (the audience:'general' Q&A slice).
`section`	Yes	Section filter applied to produce this result set ('all' if no filter).

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedv1.0.92Aug 26, 2026

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
```

changedOutput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

addedOutput schema / properties / total / maximum
```
Added value: +9007199254740991
```
addedOutput schema / properties / total / minimum
```
Added value: +-9007199254740991
```

Changed1 schema field changedv1.0.86Jul 29, 2026

changedInput schema / properties / section / enum

Previous value: -[
-  "all",
-  "understanding-savvly",
-  "investment-custody",
-  "payouts",
-  "withdrawals-estate",
-  "fees-cost",
-  "tax-legacy",
-  "employer-portability",
-  "safety-regulatory"
-]New value: +[
+  "all",
+  "understanding-savvly",
+  "investment-custody",
+  "payouts",
+  "withdrawals-estate",
+  "expenses-cost",
+  "tax-legacy",
+  "employer-portability",
+  "safety-regulatory"
+]

Changed12 schema fields changedv1.0.68Jun 30, 2026

addedInput schema / properties / section

Added value: +{
+  "default": "all",
+  "description": "Filter the FAQ to one section (kebab-case, e.g. 'tax-legacy'); 'all' returns every entry.",
+  "enum": [
+    "all",
+    "understanding-savvly",
+    "investment-custody",
+    "payouts",
+    "withdrawals-estate",
+    "fees-cost",
+    "tax-legacy",
+    "employer-portability",
+    "safety-regulatory"
+  ],
+  "type": "string"
+}

removedInput schema / properties / topic

Removed value: -{
-  "default": "all",
-  "description": "Filter FAQ by topic",
-  "enum": [
-    "general",
-    "investment",
-    "payouts",
-    "withdrawals",
-    "tax",
-    "fees",
-    "employer",
-    "regulatory",
-    "all"
-  ],
-  "type": "string"
-}

changedOutput schema / properties / entries / description

Previous value: -"Filtered FAQ entries."New value: +"Filtered FAQ entries (the audience:'general' Q&A slice)."

addedOutput schema / properties / entries / items / properties / audience

Added value: +{
+  "enum": [
+    "employee",
+    "advisor",
+    "broker",
+    "employer",
+    "universal",
+    "general"
+  ],
+  "type": "string"
+}

removedOutput schema / properties / entries / items / properties / category

Removed value: -{
-  "description": "FAQ category.",
-  "enum": [
-    "general",
-    "investment",
-    "payouts",
-    "withdrawals",
-    "tax",
-    "fees",
-    "employer",
-    "regulatory"
-  ],
-  "type": "string"
-}

addedOutput schema / properties / entries / items / properties / footnotes

Added value: +{
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

removedOutput schema / properties / entries / items / properties / related_ids

Removed value: -{
-  "items": {
-    "type": "string"
-  },
-  "type": "array"
-}

addedOutput schema / properties / entries / items / properties / section
```
Added value: +{
+  "type": "string"
+}
```

changedOutput schema / properties / entries / items / required

Previous value: -[
-  "id",
-  "category",
-  "question",
-  "answer",
-  "related_ids",
-  "keywords"
-]New value: +[
+  "id",
+  "audience",
+  "section",
+  "question",
+  "answer",
+  "footnotes"
+]

addedOutput schema / properties / section

Added value: +{
+  "description": "Section filter applied to produce this result set ('all' if no filter).",
+  "type": "string"
+}

removedOutput schema / properties / topic

Removed value: -{
-  "description": "Topic filter applied to produce this result set ('all' if no filter).",
-  "type": "string"
-}

changedOutput schema / required

Previous value: -[
-  "topic",
-  "total",
-  "entries"
-]New value: +[
+  "section",
+  "total",
+  "entries"
+]

First observedv0.1.0Jun 7, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare this is a read-only, idempotent, non-destructive operation, so the bar is lower. The description adds useful behavioral context beyond annotations: facts come from Savvly's own current records and the response includes primary sources (e.g. SEC filings) for reference. This informs the agent about data provenance and response content without contradicting 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?

The description is three sentences with no filler. It front-loads the core purpose, then gives usage guidance, names the alternative, and finishes with data provenance. 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?

The tool is simple: one optional parameter, fully documented in the schema, with a rich output schema available. The description covers what the tool does, when to use it, how it relates to a sibling, and the nature of the data source. Nothing an agent needs to invoke it 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 description coverage is 100%, so the single `section` parameter is fully documented in the schema with enum values, a default, and a description. The tool description does not add parameter-specific syntax or format details, but it does not need to; the schema carries the full burden. Baseline 3 is appropriate.

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 verb and resource: it gets answers to frequently asked questions about Savvly. It also differentiates itself from the sibling tool `search_savvly_content` by calling itself a convenience view scoped to factual FAQ, so an agent can tell them apart immediately.

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?

Explicitly states when to use it: when the user has specific questions about how Savvly works, expenses, withdrawals, or regulatory status. It also names the alternative and the condition that selects it: for richer audience-specific Q&As, use `search_savvly_content` instead. Nothing is left to inference.

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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