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JM-Addington

Autotask Search

by JM-Addington

search_tickets

Search Autotask tickets with semantic and keyword matching that handles typos and vague descriptions. Filter results by sentiment, date, priority, and frustration levels.

Instructions

Search Autotask tickets using advanced semantic and keyword search with sentiment filtering and pagination.

This tool uses a sophisticated multi-method search combining:

  • BM25 full-text search

  • Semantic vector search

  • Fuzzy matching for typos

  • AI-powered reranking for relevance

  • Sentiment analysis filtering

  • Redis caching for fast pagination

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number to retrieve (default: 1, starts at 1)
queryYesSearch query (supports partial company names, keywords, descriptions). Works well with imperfect queries including typos and vague descriptions.
end_dateNoOptional end date filter in YYYY-MM-DD format (e.g., "2024-12-31"). Only tickets created on or before this date will be returned.
per_pageNoResults per page (default: 10, max: 100)
sentimentNoOptional sentiment filter. Valid values: "negative", "neutral", "positive". Only tickets with the specified sentiment will be returned.
start_dateNoOptional start date filter in YYYY-MM-DD format (e.g., "2024-01-01"). Only tickets created on or after this date will be returned.
priority_onlyNoIf True, only return tickets flagged as priority (high negative sentiment + high frustration).
min_frustrationNoOptional minimum frustration score filter (0.0 to 1.0). Only tickets with frustration scores >= this value will be returned.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the underlying search methods (BM25, semantic, fuzzy, reranking), sentiment analysis, and Redis caching behavior. This gives the agent insight into performance and functionality beyond what a simple 'search' would imply, though it omits details like rate limits or authentication.

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 and well-structured, with a clear one-sentence summary followed by a scannable bullet list of search features. It front-loads the core purpose and adds only relevant technical details, with no redundant or filler content.

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?

Given the complexity of 8 parameters and no annotations or output schema, the description provides sufficient context about the tool's behavior, including pagination, caching, and the multi-method search approach. Since an output schema exists, no explanation of return values is needed. The description is complete for a read-only search tool.

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?

The input schema provides 100% parameter coverage with detailed descriptions for every field, including defaults, formats, and valid ranges. The tool description adds no additional parameter semantics beyond the schema, so the baseline score of 3 is appropriate. The description's mention of 'partial company names' is already covered in the query parameter description.

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 the tool searches Autotask tickets using advanced methods, distinguishing it from sibling tools like search_companies and search_contacts. The verb 'search' is explicit and the resource is specific, with additional scope (sentiment filtering, pagination) making the purpose unmistakable.

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 on when to use this tool: for flexible searches with typos, vague descriptions, and partial names. It implies use over simpler search tools due to its advanced capabilities, but does not explicitly name alternatives or state exclusions. This is clear context without formal 'when-not' guidance.

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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