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Space-C0wboy

ReliaQuest GreyMatter MCP Server

by Space-C0wboy

resolve_task

Resolve a task by providing its ID, a close code (cancelled, duplicate, or resolved), and an optional note to update its status.

Instructions

Resolve a task. input: ResolveTaskInput { taskId, closeCode (CANCELLED, DUPLICATE, or RESOLVED), closeNote }. Variables: after, filter, first, order, input. Example variables: {"after":"T18w","filter":{"created":{"earliest":"2026-05-01T00:00:00.000Z","latest":"2026-05-01T00:00:00.000Z"},"types":["PUBLIC"]},"first":10,"order":{"direction":"ASC","orderBy":"CREATED_AT"},"input":{"closeCode":"CANCELLED","closeNote":"","taskId":""}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterNoGraphQL: String
firstNoGraphQL: Int
inputYesGraphQL: ResolveTaskInput!
orderNoGraphQL: TaskCommentOrder
filterNoGraphQL: TaskCommentFilter
customer_slugNoOverride the x-reliaquest-customer (OpCo) header.
Behavior2/5

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

With no annotations, the description must disclose behavioral traits, but it only states the action and input. It does not mention whether resolution is reversible, what side effects occur, required permissions, or what the response looks like. Additionally, the unexplained after/filter/first/order variables add confusion about the tool's actual 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?

The description front-loads the core purpose ('Resolve a task'), then provides the input structure and an illustrative example. It is relatively compact, though the example JSON is somewhat lengthy. The structure is logical and avoids unnecessary fluff.

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

Completeness2/5

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

Given no output schema and no annotations, the description must carry the full contextual burden. It explains the core action and input but fails to clarify the extra query parameters, the return value, or the operational consequences. For a tool with 6 parameters, this is insufficient for reliable invocation.

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%, but the descriptions are minimal (e.g., 'GraphQL: String'). The description adds value by listing the ResolveTaskInput fields (taskId, closeCode, closeNote) and providing example values. However, the after/filter/first/order parameters are only shown in the example without explanation, leaving their purpose unclear.

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 'Resolve a task' with a specific verb and resource, and the closeCode enum values (CANCELLED, DUPLICATE, RESOLVED) add specificity. It is distinct from sibling tools like unresolve_task and bulk_resolve_tasks, which are named differently.

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?

The description does not provide any guidance on when to use this tool versus alternatives such as bulk_resolve_tasks, unresolve_task, or update_task_state. It lacks context about prerequisites (e.g., task must be open) or exclusions, leaving the agent to infer usage from the tool name alone.

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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