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search_documents

Find LibreOffice documents containing specific text by searching within directories or default document locations to locate relevant files quickly.

Instructions

Search for documents containing specific text

Args:
    query: Text to search for
    search_path: Directory to search in (default: common document locations)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
search_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the search functionality but doesn't describe what 'search' entails—whether it's case-sensitive, supports regex, returns partial matches, or includes metadata in results. It also omits performance characteristics like speed, rate limits, or error conditions, which are critical for a search operation.

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 is appropriately sized and front-loaded, with the core purpose in the first sentence and parameter details following. There's no wasted text, but the structure could be slightly improved by integrating parameter explanations more seamlessly rather than a separate 'Args:' section, though this is minor.

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?

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for result explanation. However, for a search tool, it should better address behavioral aspects like search scope and limitations, which are missing, making it incomplete for optimal agent use.

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 0%, so the description must compensate. It adds basic semantics for both parameters: 'query' as 'Text to search for' and 'search_path' with a default and scope hint ('common document locations'). This clarifies intent beyond the bare schema, but lacks details like format examples, path validation rules, or query syntax, leaving gaps in practical usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Search') and resource ('documents'), and specifies the search criteria ('containing specific text'). It distinguishes itself from siblings like 'read_document_text' or 'get_document_info' by focusing on text-based search rather than direct reading or metadata retrieval. However, it doesn't explicitly differentiate from potential overlapping tools like 'watch_document_changes' in terms of search scope.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when to prefer this over 'read_document_text' for content extraction or 'get_document_info' for metadata-based filtering. There's no context about prerequisites, limitations, or typical use cases, leaving the agent to infer usage from the purpose 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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