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freedom-mcp

by com.getfreedomos

synthesize_lead_hypothesis

Given a lead journey (from query_lead_journey), produce a structured hypothesis: intent score, conversion-failure mode, suggested outreach angle, and notes for drafting. Writes the synthesis back to leads.synopsis_jsonb so the Leads tab UI sees it. Use this after journey reconstruction, before draft_outreach.

[write-tier — first use may require a manager's approval; a from-now-on approval makes future calls seamless, a just-once approval re-asks next time.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lead_idYesUUID of the lead. Used to persist synthesis back to leads.synopsis_jsonb.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
journey_jsonYesJSON-encoded journey object returned by query_lead_journey. Caller should JSON.stringify the journey output before passing.
company_contextNoOptional short summary of the company the lead arrived at (e.g., 'Acme Health — pharmacy compounding compliance consulting for US pharmacies'). Helps the model evaluate fit.

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses that the tool writes back to leads.synopsis_jsonb, and the write-tier note explains approval behavior. No destructive or side effects are hidden, though error handling is not mentioned.

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 front-loaded with core functionality, followed by a brief approval note. Every sentence serves a purpose, though the approval note could be more concise. No unnecessary information.

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

Completeness4/5

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

Given no output schema, the description adequately describes the output structure (intent score, conversion-failure mode, etc.). It explains inputs, the write effect, and workflow ordering. Lacks details on error states or exact field names, but sufficient for a structured tool.

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% (baseline 3). The description adds context: journey_json must be stringified output from query_lead_journey, lead_id is for persistence, companyId for scoping, and company_context as optional fit context. This adds value beyond the schema descriptions.

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 ('synthesize') and resource ('lead hypothesis'), listing specific outputs (intent score, conversion-failure mode, etc.). It distinguishes itself from siblings by naming query_lead_journey as input and draft_outreach as subsequent step.

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?

Explicitly positions the tool in the workflow: 'Use this after journey reconstruction, before draft_outreach.' It also mentions the approval requirement for the write tier, which guides the agent on potential authorization needs.

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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