DeskCrew

search_tickets

Read-only full-text search over the current tenant's support tickets, matching the query against each ticket's subject and first customer message (ranked, with a literal subject-substring fallback for partial words). Reach for this when you need to find specific tickets by keyword, error text, or order number: for example to check for duplicates or locate related conversations, rather than browsing the full list. Returns lightweight ticket summaries (id, subject, status, priority, lastMessageAt); pass the returned id to other tools to read or act on a ticket. Results are always scoped to your authenticated tenant. [free]

Input Schema

TableJSON Schema

Name	Required	Description
`limit`	No	Optional maximum number of tickets to return, an integer from 1 to 50. Defaults to 20 when omitted.
`query`	Yes	The text to search for. Matched against each ticket's subject and first customer message using ranked full-text search; if no full-text matches are found it falls back to a literal subject substring match (so partial words like "logi" and punctuation like "%" still work). Pass keywords, an error message, an order number, or any phrase the customer might have used.
`status`	No	Optional filter: only return tickets currently in this status. Must be one of 'active', 'pending', 'snoozed', or 'closed'. Omit to search tickets in any status.

TDQS

A4.4/5.0

Behavior4/5

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

Without annotations, the description carries full transparency burden. It discloses read-only nature, search algorithm (ranked full-text with substring fallback), return structure (lightweight summaries), and scoping. It could mention idempotency or lack of side effects more explicitly, but 'read-only' suffices.

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?

The description is concise (3 sentences), front-loaded with purpose, then usage, then output and scoping. Every sentence adds value with no redundancy.

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 no output schema, the description adequately explains return fields (id, subject, status, priority, lastMessageAt) and how to use the id. It covers search behavior and scoping. Could mention pagination or error handling, but not critical for a search tool.

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% with good parameter descriptions. The description adds value by explaining the query's matching behavior and fallback mechanism, and clarifies the default limit. This goes beyond what the schema alone provides.

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 it is a read-only full-text search over support tickets, specifying matched fields (subject and first customer message) and tenant scoping. It distinguishes itself from sibling tools like list_tickets (browsing full list) and search_kb (knowledge base) through explicit use-case guidance.

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 explicit when-to-use guidance ('Reach for this when you need to find specific tickets by keyword... rather than browsing the full list') and gives concrete examples (check for duplicates, locate related conversations). It does not explicitly state when not to use, but the context is clear enough.

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

A4.4/5.0

Disambiguation4/5

Most tools have distinct purposes, but the presence of three drafting tools (draft_reply, draft_support_reply, propose_resolution) with overlapping functionality may cause confusion. Descriptions help differentiate them, but an agent could still misselect.

Naming Consistency4/5

Tool names predominantly follow a verb_noun pattern in snake_case (e.g., create_ticket, list_issues). However, a few tools like 'assign' and 'triage' are single verbs, and 'draft_support_reply' breaks the pattern slightly. Overall consistent but not perfect.

Tool Count5/5

16 tools is well-scoped for a customer support server covering ticket management, issue tracking, knowledge base, and changelog. Each tool serves a clear purpose without bloat.

Completeness4/5

The tool surface covers the core ticket lifecycle (creation, triage, assignment, context, drafting, sending, resolving) and supports issue linking and knowledge base search. Minor gaps like updating ticket details or bulk actions exist but are not critical.

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