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leanzero-srl

MCP Document Processor

Official
by leanzero-srl

blueprint

Manage structural blueprints extracted from real documents. Learn from DOCX or PDF, list stored blueprints, delete by name, and use to validate paragraph structure when creating documents.

Instructions

Manage structural blueprints — section/heading templates extracted from real documents. Actions: 'learn' (extract from a DOCX or PDF you already have), 'list' (show stored blueprints), 'delete' (remove by name). Blueprints are also auto-learned during 'dna evolve' when recurring structures are detected. Use a blueprint by passing { blueprint: '' } to create-doc — the tool will validate that your paragraphs match the structure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoBlueprint name (required for learn and delete).
actionYesBlueprint action.
filePathNoPath to source DOCX/PDF (REQUIRED for 'learn'; ignored otherwise).
descriptionNoOptional description (learn only).
Behavior3/5

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

No annotations are provided, so the description must fully cover behavior. It explains the actions and that learn extracts from existing documents, but does not discuss error scenarios (e.g., missing file, duplicate names) or side effects when deleting a blueprint that is in use. More behavioral details would improve transparency.

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 three sentences long, with no wasted words. The first sentence provides the core purpose, the second enumerates actions compactly, and the third gives essential context about integration with create-doc. Information is front-loaded and easy to parse.

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 description covers the main usage scenarios and connects to a sibling tool (dna) for auto-learning. It explains the purpose of each action and parameter. Minor gaps include lack of information on naming constraints, error handling, and whether blueprints are global or workspace-specific. Nonetheless, the description is sufficient for most use cases.

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 coverage is 100%, so baseline is 3. The description adds minimal value beyond the schema; it repeats the required conditions for parameters (e.g., name required for learn/delete, filePath required for learn). However, it does provide context like 'extract from a DOCX or PDF you already have', which clarifies the filePath parameter's intent.

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 defines the tool's purpose: managing structural blueprints extracted from documents. It lists the specific actions (learn, list, delete) and differentiates itself from sibling tools by explaining how blueprints are used with create-doc via the 'blueprint' parameter.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use each action: learn for extracting from DOCX/PDF, list for showing stored blueprints, delete for removal. It also notes that blueprints are auto-learned during 'dna evolve', providing a clear alternative and context for use.

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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