Skip to main content
Glama

search_text

Search for text across the entire document including body, footnotes, and comments. Supports case-insensitive or regex queries to locate specific content with paragraph IDs and context.

Instructions

Search for text across the document body, footnotes, and comments.

Args: query: Text to search for (case-insensitive), or a regex pattern. regex: If true, treat query as a Python regular expression.

Returns matching paragraphs with their paraId, source part, and context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
regexNo
document_handleNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the search is case-insensitive and supports regex, and that results include paraId, source part, and context. However, it does not explicitly state that the operation is read-only, nor does it mention any potential side effects, permissions, or limits. This is adequate but not comprehensive.

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 extremely concise: a single sentence for purpose, a brief list of parameters, and a one-line return description. Every sentence adds value with no redundancy or fluff.

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 the presence of an output schema and the simple nature of the tool, the description is mostly complete. It specifies the scope (body, footnotes, comments) and the return fields. It could mention whether headers/footers are included or any search limits, but these are minor omissions. Overall, it provides sufficient context for usage.

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 explains query (case-insensitive, regex) and regex (Python regular expression flag) well, adding meaning beyond the schema types. However, the third parameter 'document_handle' is not mentioned at all, leaving its purpose unclear. This partial coverage earns a score of 3.

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 searches for text across the document body, footnotes, and comments. This specifies the verb (search) and the resource (text in specific document parts), making its purpose distinct from many sibling tools, though it doesn't explicitly differentiate from similar tools like find_replace_formatted.

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 such as replace_text or find_replace_formatted. It lacks any 'when to use' or 'when not to use' context, which is insufficient given the large number of sibling tools.

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/SecurityRonin/docx-mcp'

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