Skip to main content
Glama

search_docs

Find Google Docs by name in Drive using search queries to locate specific documents quickly.

Instructions

Searches for Google Docs by name using Drive API (mimeType filter).

Returns: str: A formatted list of Google Docs matching the search query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_google_emailYes
queryYes
page_sizeNo

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 API used ('Drive API') and a filter ('mimeType filter'), but fails to cover critical aspects like whether this is a read-only operation, potential rate limits, authentication requirements, or error handling. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.

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 concise and front-loaded, with the core purpose stated in the first sentence and return information in the second. There's no wasted text, and the structure is clear. However, the return statement could be more integrated, and the parenthetical note about 'mimeType filter' is slightly awkward, preventing a perfect score.

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 (3 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for detailed output explanation. However, the description lacks crucial context about parameters, behavioral traits, and usage compared to siblings, making it incomplete for optimal agent understanding.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, meaning none of the three parameters (user_google_email, query, page_size) are documented in the schema. The description adds minimal value by implying a 'search query' parameter and mentioning 'Google Docs', but it doesn't explain what user_google_email is for, the format of the query, or the purpose of page_size. This insufficiently compensates for the lack of schema documentation.

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: 'Searches for Google Docs by name using Drive API (mimeType filter).' It specifies the verb ('Searches'), resource ('Google Docs'), and method ('using Drive API'), distinguishing it from generic search tools. However, it doesn't explicitly differentiate from sibling tools like 'search_drive_files' or 'list_docs_in_folder', which is why it doesn't reach a perfect 5.

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 mentions a 'mimeType filter' but doesn't explain its significance or compare it to other search/list tools in the sibling set (e.g., 'search_drive_files', 'list_docs_in_folder'). This lack of contextual guidance leaves the agent to infer usage scenarios independently.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/HuntsDesk/ve-gws'

If you have feedback or need assistance with the MCP directory API, please join our Discord server