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draft_outreach

Produce two outreach draft variants (A/B) for a lead given an angle. Both drafts are warm and kind by design (P10) — variants differ in angle of helpfulness (subject hook, opening framing, call-to-action) not in tone. Drafts are written to lead_drafts as pending_review. Returns IDs + previews. Use after synthesize_lead_hypothesis to draft initial 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
angleYesThe outreach angle to use (e.g., 'deeper_lp3_discovery', 'lighter_touch_different_hook', 'jurisdiction_clarification', 'kind_check_in'). Take from synthesize_lead_hypothesis.suggested_angle if unsure.
lead_idYesUUID of the lead.
reply_toNoOptional Reply-To address to carry on the eventual send (CONTRACT-1 agent thread address). Stamped into both drafts' metadata (best-effort — the metadata column is additive); send_lead_draft reads it at send time and passes it to send_email. Never changes what is drafted.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
from_nameNoOptional sender display name (e.g., 'Acme Team', 'Alex at Acme'). Used in draft signature. When omitted, the CONTRACT-5 chain resolves it: the company's mcp_connections.resend.auth_config.from_name, else a generic 'Team'. Sequence callers pass the sequence's owning agent's name here (the top of the chain).
eligible_atNoOptional ISO timestamp — the earliest real time this draft may be sent (2026-07-13 send-timing gate). For a sequence step, pass now + that step's delay_hours (an ESTIMATE; the send-gate re-stamps it to the real value once the prior step actually resolves). Omitted → eligible immediately (the correct default for step 1 and for manual one-off drafts).
sequence_idNoOptional. The outreach_sequences.id the step belongs to. Pass it together with sequence_step_id to enable the A/B prior-stats bias — step ids repeat across sequences (step1…stepN), so stats are only comparable within one sequence. Also persisted on the draft row so the send-gate can resolve "the next step's draft" by an exact join instead of guessing. Omitted → no bias, no sequence linkage (manual one-off draft).
company_contextNoOptional short summary of the company the lead arrived at (e.g., 'Acme Health — pharmacy compounding compliance consulting'). Helps the model pitch correctly.
journey_summaryYesShort prose summary of what we know about this lead (their state, recent activity, what they engaged with). Used as context for the draft. Synthesis.intent_summary + 1-2 notes works well.
sequence_step_idNoOptional. If this draft is part of an auto-mode sequence step, pass the step_id from outreach_sequences. Otherwise omit (manual one-off draft).
regenerated_reasonNoOptional (regenerate-on-signal, 2026-07-15). When the sequencer re-drafts a not-yet-sent step after a meaningful lead signal (temperature flip to hot, a click), it passes a short human-readable reason (e.g. 'redrafted after they clicked'). Stamped into both drafts' metadata.regenerated_reason so the review card can show WHY the copy was refreshed. Never changes drafting logic — provenance only.
variant_b_guidanceNoOptional (CONTRACT-3). Sequence-designed seed for the B variant — a distilled subject+body angle persisted on the sequence step (steps jsonb, additive variant_b_guidance key). When present, variant_b is grounded in this guidance while variant_a stays the model's best independent take on the main angle. Omitted → both variants generated exactly as before.

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries full burden. It discloses that drafts are written to lead_drafts as pending_review, returns IDs+previews, and mentions the write-tier approval requirement. But it lacks details on side effects, idempotency, or error conditions.

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 concise, front-loaded with the core action, and no wasted words. The second paragraph adds important behavioral context efficiently.

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?

For a 12-parameter tool with no output schema, the description covers purpose, usage context, tone, variant details, and approval requirement. It could be more complete by explaining failure modes or relationship to other tools beyond synthesis.

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 coverage is 100%, so the baseline is 3. The description does not add parameter-level detail beyond the schema; it focuses on overall tool behavior.

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 tool produces two outreach draft variants (A/B) for a lead given an angle, specifying the resource, action, and output. It distinguishes from sibling tools like synthesize_lead_hypothesis and send_lead_draft.

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?

It explicitly advises using this tool after synthesize_lead_hypothesis, providing clear context. However, it does not mention when not to use or list alternatives beyond this sequence.

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.

Resources