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mh-zeynal

gapify-mcp-server

by mh-zeynal

Conversation Assignment: Assign Conversation

gapify_assign_conversation

Assign a conversation to an agent or team to route customer support tickets. Specify assignee or team ID to direct the conversation.

Instructions

Assign a conversation to an agent or a team [Conversation Assignment] POST /api/v1/accounts/{account_id}/conversations/{conversation_id}/assignments Auth: User API key (header api-access-token). Body fields: assignee_id, team_id. Extra fields can be passed via extra_body. Text values may be written in Persian (Farsi) or any other language and are sent unchanged; IDs, numbers and phone numbers may use Persian or Arabic-Indic digits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_idNoId of the team. If the assignee_id is present, this param would be ignored Example: 0
account_idYesThe numeric ID of the account
extra_bodyNoAdditional JSON body fields to merge into the request (for keys not listed above).
assignee_idNoId of the assignee user Example: 0
conversation_idYesThe numeric ID of the conversation

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare mutating (readOnlyHint=false), non-idempotent, non-destructive, open-world behavior. The description usefully adds the auth requirement (User API key header) and language/digit handling context, but does not say what a reassignment replaces, whether the prior assignee is notified, or permission requirements.

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 core purpose is front-loaded, followed by endpoint, auth, and param notes in a compact block. It is somewhat dense (mixing endpoint/auth/body/language into one paragraph) but no sentence is truly wasted.

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

Completeness3/5

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

For a mutation tool with no output schema, the description covers endpoint, auth, body fields, and passthrough but omits success/return behavior and side effects. Adequate to invoke but incomplete on outcome expectations.

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%, so baseline is 3. The description restates body fields (assignee_id, team_id) and the extra_body passthrough that the schema already documents in detail, adding little beyond 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?

The description states a specific verb and resource ("Assign a conversation to an agent or a team") and even includes the exact REST endpoint, making scope unambiguous. It distinguishes itself clearly from siblings like create_conversation or toggle_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 Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It never says when to use this tool versus alternatives, nor does it state prerequisites or exclusions. The only conditional behavior (team_id ignored when assignee_id is present) is in the schema, not framed as guidance in the description.

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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