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awslabs
by awslabs

text_search_oscal

Full-text search across all OSCAL documents and child elements. Filter results by model type and paginate with relevance ranking.

Instructions

Full-text search across all OSCAL documents and child elements.

Searches titles, descriptions, and other indexed text fields using SQLite FTS5. Results are ranked by relevance. Optionally scope the search to a single OSCAL model type.

Args: ctx: MCP server context (injected automatically). query_text: The search string. oscal_model_type: Optional model type value to scope results (e.g. "catalog", "system-security-plan"). When omitted, all model types are searched. offset: Zero-based pagination offset (default 0). limit: Maximum items to return, 1-100 (default 10).

Returns: Page_Response dict with keys: items, total, offset, limit, hasMore. Each item contains: entity_type, entity_id, title, description, model_type.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_textNo
oscal_model_typeNo
offsetNo
limitNo
Behavior4/5

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

With no annotations provided, the description carries full burden. It discloses that the tool uses SQLite FTS5, ranks by relevance, and supports optional model type scoping and pagination. However, it does not explicitly state that the operation is read-only, though it is implied by the nature of search.

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 a summary paragraph followed by Args and Returns sections. It is clear but slightly verbose (e.g., repeating 'Default' values already in schema). Every sentence adds value, but some consolidation could improve conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description adequately covers the return structure (Page_Response with keys: items, total, offset, limit, hasMore) and item fields (entity_type, entity_id, title, description, model_type). The tool's complexity (4 parameters, no annotations) is well-served by the description, making inputs and outputs fully understandable.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must provide meaning. It does so thoroughly: query_text is 'the search string', oscal_model_type is 'Optional model type value to scope results' with examples, offset is 'Zero-based pagination offset', limit is 'Maximum items to return, 1-100'. This adds essential context beyond the schema's types and defaults.

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 'Full-text search across all OSCAL documents and child elements,' using a specific verb and resource. It explains what fields are searched (titles, descriptions) and distinguishes from sibling list/query tools by indicating it searches across all documents, not just a single model type.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives like list_* or query_* tools. It implies usage for cross-document text search but lacks guidance on when not to use it or which sibling tools to prefer in specific scenarios.

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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