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nazaryanenko

scrivener-mcp

by nazaryanenko

search_project

Find text or regex patterns across all project documents. Returns matching documents with excerpts, supporting case-sensitive searches.

Instructions

Search for text across all documents in the project.

Args: query: Text or regex pattern to search for case_sensitive: Whether to match case (default: False)

Returns: List of matching documents with excerpts showing the matching lines.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
case_sensitiveNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Since no annotations are provided, the description carries the full burden for behavioral disclosure. It mentions the return format ('List of matching documents with excerpts'), which is useful, but it does not disclose potential side effects, performance implications, authentication requirements, or regex flavor details. This is adequate but leaves gaps.

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 one-line summary followed by Args and Returns sections. Every sentence adds value, and the formatting makes it easy to parse. No unnecessary words or repetition.

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?

The tool is simple with only two parameters, and the description covers the core behavior, scope, and return format. It does not mention edge cases or limitations (e.g., supported file types, performance on large projects), but given the tool's simplicity and presence of an output schema, it is largely complete.

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%, so the description must compensate. It adds meaningful semantics beyond the schema: 'query' is described as 'Text or regex pattern', clarifying regex support, and 'case_sensitive' is explained with its default. This goes beyond what the bare schema provides.

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 action ('Search for text') and the resource ('all documents in the project'), which makes the tool's purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'scan_project', so it lacks explicit sibling differentiation.

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 only states what it does and does not mention any exclusions or recommended use cases. The scope 'across all documents' implies a general use case, but no explicit decision-making help is offered.

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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