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c0webster

Hardened Google Workspace MCP

by c0webster

find_and_replace_doc

Search and replace text in Google Docs to update content efficiently. This tool helps users modify documents by finding specific text and replacing it with new content.

Instructions

Finds and replaces text throughout a Google Doc.

Args: user_google_email: User's Google email address document_id: ID of the document to update find_text: Text to search for replace_text: Text to replace with match_case: Whether to match case exactly

Returns: str: Confirmation message with replacement count

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_google_emailYes
document_idYes
find_textYes
replace_textYes
match_caseNo

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 full burden but only states the operation without disclosing behavioral traits. It doesn't mention permissions needed, whether changes are reversible, rate limits, or error handling, leaving significant gaps for a mutation tool.

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 well-structured with clear sections (purpose, Args, Returns) and uses minimal sentences. Every part adds value, though the 'Args' formatting could be more integrated rather than bullet-like for optimal flow.

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 a mutation tool with no annotations, 0% schema coverage, but an output schema exists, the description is partially complete. It covers parameters and return value basics but lacks behavioral context (e.g., safety, side effects) that annotations would typically provide.

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

Parameters4/5

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

Schema description coverage is 0%, but the description's 'Args' section clearly documents all 5 parameters with meaningful names and brief explanations (e.g., 'Text to search for', 'Whether to match case exactly'). This compensates well for the schema gap, though it lacks format examples.

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 specific action ('Finds and replaces text') and resource ('throughout a Google Doc'), distinguishing it from siblings like 'modify_doc_text' or 'batch_update_doc'. It precisely communicates the tool's function without redundancy.

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?

No guidance is provided on when to use this tool versus alternatives like 'modify_doc_text' or 'batch_update_doc'. The description lacks context about prerequisites, constraints, or typical use cases, offering only basic functional information.

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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