Onsa

Send outreach message

send_outreach

Destructive

Queues one already-approved outreach draft for delivery to a real person on LinkedIn. It requires confirmText, the draft body character-for-character as stored, and confirmName, the recipient's name: drafts are often near-identical between people, so matching the body alone does not identify which one was meant. A mismatch is refused without returning the stored text, which list_pending_outreach supplies. The server additionally requires a confirmation from the person at the keyboard, rendered by the MCP client and quoting the draft as stored; that approval is single-use and bound to one recipient and one draft. A client that cannot render such a confirmation receives a refusal carrying a link to approve inside the Onsa app, and nothing is queued. On success the message is queued rather than delivered: Onsa sends it on its own schedule, subject to daily pacing limits. A follow-up draft (kind: follow_up) goes into the same LinkedIn thread as the first message and is cancelled if the person replies before it is sent.

Input Schema

TableJSON Schema

Name	Required	Description
`leadId`	Yes	Lead to message, from list_pending_outreach
`confirmName`	Yes	The recipient's name as shown to the user, to prove you meant this person
`confirmText`	Yes	The exact draft text you showed the user and they approved, verbatim

Output Schema

TableJSON Schema

Name	Required	Description	Default
`kind`	No
`name`	Yes
`leadId`	Yes
`queued`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 15, 2026
- addedOutput schema / properties / kind
```
Added value: +{
+  "type": "string"
+}
```
First observedSep 8, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations declare destructive/non-idempotent/open-world, and the description adds substantial context beyond them: a single-use, recipient-bound confirmation from the person at the keyboard, refusal-without-echoing-text on mismatch, a fallback refusal link for clients that cannot render the confirmation, and the fact that success means queued (paced) rather than delivered. It even discloses follow-up thread and reply-cancellation behavior.

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?

Purpose and the two confirmation arguments are front-loaded, and each subsequent sentence carries a distinct operational fact (refusal behavior, app-approval fallback, queuing semantics, follow-up threading). It is long but nearly every sentence earns its place; minor density could be trimmed.

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?

For a destructive, open-world mutation with an output schema, the description covers what an agent needs: prerequisites, required confirmation semantics, failure modes, and post-call queuing/pacing behavior. Return values are covered by the output schema, so nothing material is missing.

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%, so the baseline is 3, but the description adds genuine meaning: confirmText must be the body 'character-for-character as stored', confirmName is the recipient's name, and it explains why both are needed (near-identical drafts make body matching insufficient). That rationale is not in the schema.

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?

States a specific verb and resource ('Queues one already-approved outreach draft for delivery to a real person on LinkedIn') and immediately distinguishes itself from siblings by referencing list_pending_outreach as the source of drafts and stored text. An agent can tell this apart from rewrite_outreach or list_pending_outreach without opening another schema.

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 precondition is explicit: the draft must already be approved, and the tool routes the agent to list_pending_outreach when a mismatch occurs. It does not name rewrite_outreach as the path for editing an unapproved draft, so the sibling routing is incomplete, but the when-to-use condition is clear.

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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