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send_lead_draft

Send an approved outreach draft to its lead via the company's Resend connection, then mark the draft 'sent'. This is the manual human-in-the-loop send: it delivers exactly one lead_drafts row (by id) to the lead's email and records sent_at + resend_message_id. Honors the do_not_contact suppression list (the send is refused if the lead is suppressed). Use after an operator approves a draft in the Leads tab.

Routing: Operator approved an outreach draft and wants to send it → use this

[outbound-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
draft_idYesUUID of the lead_drafts row to send.
reply_toNoOptional Reply-To address for the outbound email.
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool sends via Resend, marks sent, records sent_at and resend_message_id, and honors suppression lists. It also mentions outbound-tier approval requirements. However, it does not describe error behavior, idempotency, or what happens if the send fails, which would be valuable for a mutation tool.

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 compact with six sentences plus a routing section and a note. It is front-loaded with the core action. The additional information (suppression list, approval note) is valuable and not redundant. A slight reduction in length could be possible, but it remains focused.

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 that there is no output schema and no annotations, the description covers the tool's purpose, when to use it, approval context, suppression list, and required parameters. It lacks details about the response or error states, but for a straightforward send action, it provides sufficient context for an AI agent to use it correctly.

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%, with each parameter already having a clear description in the schema. The tool description reinforces that draft_id is for a single lead_drafts row but does not add significant new meaning beyond the schema. 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 action ('Send'), the resource ('approved outreach draft'), the target ('to its lead via the company's Resend connection'), and the additional effect ('mark the draft sent'). It also distinguishes from siblings like 'send_email' by specifying it operates on a single lead_drafts row and is a human-in-the-loop operation.

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 explicitly states when to use the tool: 'Use after an operator approves a draft in the Leads tab' and includes a routing section that says 'Operator approved an outreach draft and wants to send it → use this'. It also notes the do_not_contact suppression list and the outbound-tier approval requirement, providing clear context. However, it does not explicitly list alternative tools or when not to use it beyond suppressed leads.

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