savvly-mcp

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Search Savvly Q&A Content Library

search_savvly_content

Read-onlyIdempotent

Search Savvly's Q&A Content Library by audience and section to find positioning, value props, talking points, and factual FAQ answers with source citations.

Instructions

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

Name	Required	Description
`limit`	No	Cap on matched entries returned. Default 20, max 50.
`query`	No	Free-text search over questions, answers, and footnotes (case-insensitive). Multi-word queries match entries containing ANY of the words, ranked by relevance.
`section`	No	Substring match against section slugs (e.g. 'tax', 'retention', 'eligibility'). Case-insensitive.
`audience`	No	Restrict 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

Name	Required	Description
`entries`	Yes	Matched Q&A entries.
`matched`	Yes	Count of entries matching the supplied filters.
`filter_applied`	Yes	Echo of the filters that produced this result set.
`total_in_library`	Yes	Total Q&A entry count in the library across all audiences.
`available_sections`	Yes	Section slugs available within the (optionally) selected audience.

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv1.0.102Oct 24, 2026
- removedOutput schema / properties / filter_applied / properties / audience / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / filter_applied / properties / audience / type
```
Added value: +[
+  "string",
+  "null"
+]
```
- removedOutput schema / properties / filter_applied / properties / query / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / filter_applied / properties / query / type
```
Added value: +[
+  "string",
+  "null"
+]
```
- removedOutput schema / properties / filter_applied / properties / section / anyOf
```
Removed value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]
```
- addedOutput schema / properties / filter_applied / properties / section / type
```
Added value: +[
+  "string",
+  "null"
+]
```
Changed15 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 / filter_applied / properties / audience / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / filter_applied / properties / audience / type
```
Removed value: -[
-  "string",
-  "null"
-]
```
- addedOutput schema / properties / filter_applied / properties / limit / maximum
```
Added value: +9007199254740991
```
- addedOutput schema / properties / filter_applied / properties / limit / minimum
```
Added value: +-9007199254740991
```
- addedOutput schema / properties / filter_applied / properties / query / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / filter_applied / properties / query / type
```
Removed value: -[
-  "string",
-  "null"
-]
```
- addedOutput schema / properties / filter_applied / properties / section / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- removedOutput schema / properties / filter_applied / properties / section / type
```
Removed value: -[
-  "string",
-  "null"
-]
```
- addedOutput schema / properties / matched / maximum
```
Added value: +9007199254740991
```
- addedOutput schema / properties / matched / minimum
```
Added value: +-9007199254740991
```
- addedOutput schema / properties / total_in_library / maximum
```
Added value: +9007199254740991
```
- addedOutput schema / properties / total_in_library / minimum
```
Added value: +-9007199254740991
```

Changed1 schema field changedv1.0.86Jul 29, 2026

changedInput schema / properties / audience / description

Previous value: -"Restrict to one stakeholder audience ('general' is the factual FAQ). Use the exact lowercase token (the enum is case-sensitive). Omit to search across all audiences."New value: +"Restrict 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."

Changed1 schema field changedv1.0.69Jul 8, 2026

changedInput schema / properties / audience / description

Previous value: -"Restrict to one stakeholder audience ('general' is the factual FAQ). Omit to search across all audiences."New value: +"Restrict to one stakeholder audience ('general' is the factual FAQ). Use the exact lowercase token (the enum is case-sensitive). Omit to search across all audiences."

Changed14 schema fields changedv1.0.68Jun 30, 2026

changedInput schema / properties / audience / description

Previous value: -"Restrict to one stakeholder audience. Omit to search across all audiences."New value: +"Restrict to one stakeholder audience ('general' is the factual FAQ). Omit to search across all audiences."

changedInput schema / properties / audience / enum

Previous value: -[
-  "employee",
-  "advisor",
-  "broker",
-  "employer",
-  "universal"
-]New value: +[
+  "employee",
+  "advisor",
+  "broker",
+  "employer",
+  "universal",
+  "general"
+]

addedInput schema / properties / section

Added value: +{
+  "description": "Substring match against section slugs (e.g. 'tax', 'retention', 'eligibility'). Case-insensitive.",
+  "type": "string"
+}

removedInput schema / properties / subsection

Removed value: -{
-  "description": "Substring match against subsection labels (e.g. 'Tax', 'Retention', 'Eligibility'). Case-insensitive.",
-  "type": "string"
-}

addedOutput schema / properties / available_sections

Added value: +{
+  "description": "Section slugs available within the (optionally) selected audience.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}

removedOutput schema / properties / available_subsections

Removed value: -{
-  "description": "Subsection labels available within the (optionally) selected audience.",
-  "items": {
-    "type": "string"
-  },
-  "type": "array"
-}

changedOutput schema / properties / entries / items / properties / audience / enum

Previous value: -[
-  "employee",
-  "advisor",
-  "broker",
-  "employer",
-  "universal"
-]New value: +[
+  "employee",
+  "advisor",
+  "broker",
+  "employer",
+  "universal",
+  "general"
+]

addedOutput schema / properties / entries / items / properties / keywords

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

removedOutput schema / properties / entries / items / properties / subsection
```
Removed value: -{
-  "type": "string"
-}
```

changedOutput schema / properties / entries / items / required

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

addedOutput schema / properties / filter_applied / properties / section
```
Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}
```
removedOutput schema / properties / filter_applied / properties / subsection
```
Removed value: -{
-  "type": [
-    "string",
-    "null"
-  ]
-}
```

changedOutput schema / properties / filter_applied / required

Previous value: -[
-  "audience",
-  "subsection",
-  "query",
-  "limit"
-]New value: +[
+  "audience",
+  "section",
+  "query",
+  "limit"
+]

changedOutput schema / required

Previous value: -[
-  "total_in_library",
-  "matched",
-  "filter_applied",
-  "available_subsections",
-  "entries"
-]New value: +[
+  "total_in_library",
+  "matched",
+  "filter_applied",
+  "available_sections",
+  "entries"
+]

Changed1 schema field changedv1.0.67Jun 25, 2026

changedInput schema / properties / query / description

Previous value: -"Free-text substring filter applied to questions, answers, and footnotes. Case-insensitive."New value: +"Free-text search over questions, answers, and footnotes (case-insensitive). Multi-word queries match entries containing ANY of the words, ranked by relevance."

First observedv0.1.0Jun 7, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: each entry carries the verbatim answer with disclaimer footnotes, and responses include primary sources such as SEC filings. It doesn't contradict any annotation.

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?

Front-loads what the library is, then when to use it, then what an entry contains. It is somewhat dense with parentheticals (audience list, slug examples), but every sentence carries information and none is filler.

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 an output schema present, return shape needn't be explained, and the description still covers provenance, entry contents, and the presence of source citations. An agent has enough to call it correctly and interpret results.

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 all four parameters including the audience enum and section substring matching are fully documented in the schema. The description restates the audience list and gives kebab-case section slug examples, which is helpful color but largely duplicative of structured fields, so baseline 3 applies.

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?

States a specific verb and resource (search the Savvly Q&A Content Library) and describes the corpus composition and its two organizing axes (audience and section). It doesn't explicitly distinguish itself from close siblings like get_savvly_faq, and in fact mentions 'the factual FAQ' as part of this library, which could cause some ambiguity about where FAQ content lives.

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?

Gives a concrete trigger: use it for questions about Savvly's positioning, value props, audience-specific talking points, or Q&A-style messaging. That is clear situational guidance, but there are no explicit exclusions or named alternatives for overlapping needs (e.g. FAQ lookups vs. this library).

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

Deploy Server

Search Savvly Q&A Content Library

Instructions

Input Schema

Output Schema

Schema Changelog

TDQS

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