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Vortiago

MCP Outline Server

by Vortiago
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

ask_ai_about_documents

Read-only

Ask natural language questions about your documents to get direct answers with sources. Find information across multiple documents or within a specific collection or document.

Instructions

    Queries document content using natural language questions.

    Use this tool when you need to:
    - Find specific information across multiple documents
    - Get direct answers to questions about document content
    - Extract insights from your knowledge base
    - Answer questions like "What is our vacation policy?"
    - Answer "How do we onboard new clients?" and similar queries

    Args:
        question: The natural language question to ask
        collection_id: Optional collection to limit the search to
        document_id: Optional document to limit the search to

    Returns:
        AI-generated answer based on document content with sources

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes
document_idNo
collection_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.5/5.0
Behavior4/5

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

Description adds context beyond annotations by stating the answer is AI-generated and includes sources. Annotations already declare readOnlyHint=true, so no contradiction. However, it does not clarify openWorldHint behavior (potential use of external knowledge).

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?

Well-structured with bullet points, separate Args and Returns sections. Every sentence is informative and earns its place. No 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?

Covers purpose, usage, parameters, and return type. Could mention potential latency or limitations (e.g., token limits), but overall complete for a read-only query tool with good annotations.

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% (schema only has titles), but the description provides detailed explanations for all parameters (e.g., 'The natural language question to ask'). This adds significant value beyond the schema.

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 that the tool queries document content using natural language questions, and provides specific use cases (e.g., find information across documents, get answers). This distinguishes it from sibling tools like search_document_content or read_document.

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

Usage Guidelines4/5

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

The bulleted list explicitly states when to use the tool (find info, get answers, extract insights). However, it does not explicitly say when not to use it or provide alternatives, which would strengthen guidance.

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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