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Sequenzy

Sequenzy MCP Server

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reply_to_conversation

Reply to a conversation with an outbound email or add an internal note. Replying to a closed conversation reopens it.

Instructions

Send a reply in a conversation, or add an internal note. type 'outbound' (default) emails the subscriber and requires bodyText or bodyHtml; type 'note' adds a private team-only note. Replying to a closed conversation reopens it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyIdNoCompany ID. If not provided, uses the currently selected company.
conversationIdYesConversation ID to reply in.
typeNoMessage type: outbound emails the subscriber, note is internal-only. Defaults to outbound.
subjectNoOptional subject override. Defaults to the conversation subject.
bodyTextNoPlain-text message body.
bodyHtmlNoHTML message body.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoWhether the Sequenzy operation succeeded.
messageNoThe conversation message record returned by Sequenzy.
noteNoAdditional context about the result.
itemsNoFallback array wrapper used only when a tool returns a bare array.
valueNoFallback object wrapper used only when a tool returns a non-object value.
conversationNoThe inbox conversation record returned by Sequenzy.
Behavior4/5

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

Beyond annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true), the description adds that replying reopens a closed conversation, which is a behavioral side effect. No contradiction with annotations.

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?

Two concise sentences front-load the main purpose and key details (types, reopening). Every sentence adds value with no wasted words.

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 tool with 6 parameters (1 required), 100% schema coverage, and an output schema (not shown), the description covers the main purpose, parameter distinctions, and the important side effect of reopening conversations, making it fully complete.

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 all parameters described. The description adds context about type behavior (outbound requires bodyText or bodyHtml) but does not significantly extend beyond the schema, earning the baseline score.

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 explicitly states the tool sends a reply or adds an internal note, distinguishing between 'outbound' and 'note' types. It also mentions reopening closed conversations, which clearly identifies the tool's resource and scope, differentiating it from sibling tools like mark_conversation_read or update_conversation_status.

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 clear context for when to use each type: 'outbound' emails the subscriber, 'note' is private. It also notes that replying reopens a closed conversation. However, it doesn't explicitly state when not to use or mention alternatives, though the context is sufficient.

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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