freedom-mcp

query_sme

Query an external Subject Matter Expert (SME) AI for verified domain knowledge. The SME's answers are grounded in verified rules and go through a rigorous verification pipeline — this is NOT a general search, it's consulting a domain expert.

Use this when:

You need factual, verified information for content creation (social media, blog posts, newsletters)
You want to fact-check a claim before publishing
You need talking points grounded in domain expertise
You're creating content about a domain the SME covers

Available SME sources:

"conduit" — Pharmaceutical compounding compliance expert (USP 795/797/800, state regulations)

Routing: pharma / USP 795·797·800 / sterile·non-sterile compounding / BUD / board-of-pharmacy compliance fact you must get right → call query_sme (the verified Conduit SME) to fact-check it BEFORE escalating to a human or deriving the rule yourself; cite its sources

Input Schema

TableJSON Schema

Name	Required	Description
`context`	No	Optional context about why you're asking — helps the SME give a more relevant answer. E.g., "I'm creating a social media post about cleanroom best practices"
`question`	Yes	The question to ask the subject matter expert. Be specific and clear.
`companyId`	Yes	FreedomOS company id to act within (you must be a member). Required for company-scoped tools.
`sme_source`	No	Which Expert to consult, by key. "conduit" (pharmaceutical compounding compliance) is always available; your company may have additional Experts configured. Defaults to "conduit".

TDQS

A4/5.0

Behavior3/5

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

With no annotations, the description must fully disclose behavior. It mentions the verification pipeline and that it's not general search, but omits details like rate limits, cost, error handling, or what happens if the SME cannot answer. It claims answers are cited, but doesn't specify format.

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 somewhat lengthy but well-structured with sections for use cases, sources, and routing. It front-loads the purpose effectively. A few minor redundancies could be trimmed, but overall it is well-organized.

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

Completeness3/5

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

Given 4 parameters, no output schema, and the complexity of an external expert tool, the description covers purpose, usage, sources, and param guidance. However, it does not describe the output format or what constitutes a successful response, leaving a gap in completeness.

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%, and the description adds context beyond the schema: it explains when to use the optional 'context' field, advises on specificity for 'question', clarifies 'companyId' scoping, and describes the default 'sme_source' with available options.

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 'Query an external Subject Matter Expert (SME) AI for verified domain knowledge' with a specific verb and resource. It distinguishes itself from general search and sibling tools like query_lead_journey by emphasizing verified, expert-backed answers.

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 use cases (content creation, fact-checking, talking points) and a routing example for pharma compliance. It lacks explicit 'when not to use' or named alternatives, but the positive guidance is strong enough to direct correct usage.

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

A3.6/5.0

Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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