Skip to main content
Glama

DeskCrew - AI Chatbot, Helpdesk & Knowledge base

by io.deskcrew
OverviewDiscussionsAdmin

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
NameRequiredDescriptionDefault
limitNoOptional maximum number of tickets to return, an integer from 1 to 50. Defaults to 20 when omitted. A number sent as a string is read as a number, and anything above 50 is treated as 50.
queryYesThe 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.
statusNoOptional filter: only return tickets currently in this status. Must be one of 'active', 'pending', 'snoozed', or 'closed'. Omit to search tickets in any status.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / limit / description
      Previous value: -"Optional maximum number of tickets to return, an integer from 1 to 50. Defaults to 20 when omitted."New value: +"Optional maximum number of tickets to return, an integer from 1 to 50. Defaults to 20 when omitted. A number sent as a string is read as a number, and anything above 50 is treated as 50."
  2. Changed3 schema fields changed
    • addedInput schema / properties / limit / description
      Added value: +"Optional maximum number of tickets to return, an integer from 1 to 50. Defaults to 20 when omitted."
    • addedInput schema / properties / query / description
      Added value: +"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."
    • addedInput schema / properties / status / description
      Added value: +"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."
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden, and it does so well: it declares read-only behavior, tenant scoping, ranked matching with a literal substring fallback, and the shape of results. It stops short of disclosing pagination semantics or any rate/permission constraints, which keeps it from a 5.

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?

Front-loaded with the core operation and scope, then usage routing, then return shape. Dense and mostly waste-free, though the four-sentence block repeats some matching detail already present in the schema.

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?

With no output schema and no annotations, the description fills both gaps: it names the returned summary fields and explains that the id feeds other tools, and it covers scoping and matching. Nothing essential for correct invocation is missing.

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 limit, query, and status are already fully documented in the schema. The description restates the query semantics but adds no meaning beyond what the schema fields already provide, so the baseline of 3 applies.

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 ("full-text search over the current tenant's support tickets") plus the exact fields matched (subject and first customer message). An agent can distinguish this from list_tickets without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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

Explicitly says when to reach for it (keywords, error text, order number; checking duplicates or locating related conversations) and contrasts it with the alternative ("rather than browsing the full list"), routing the agent away from list_tickets. Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All DeskCrew - AI Chatbot, Helpdesk & Knowledge base toolsInspect
Try in Browser
Context All Your Agents Can ShareAgents share what they learn, hand off work, and hold it when you switch models. Start with 5 GB free.fulcradynamics.comAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources