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Epochal-dev

Open Notebook MCP Server

by Epochal-dev
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

ask_question

Ask targeted questions to extract answers from your notebook content. Specify different AI models for strategy, answer, and final synthesis to get precise, sourced responses.

Instructions

Ask a question about your content with detailed control.

Args:
    question: Question to ask
    strategy_model: Model ID for strategy generation
    answer_model: Model ID for answering
    final_answer_model: Model ID for final answer synthesis
    notebook_id: Optional notebook ID to limit context

Returns:
    Answer with sources and reasoning

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYes
strategy_modelYes
answer_modelYes
final_answer_modelYes
notebook_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

C2.9/5.0
Behavior3/5

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

With no annotations, the description must carry the full burden. It mentions the multi-stage process (strategy, answer, final) and return type, but does not disclose side effects, authentication needs, or rate limits.

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?

Concise with clear Args/Returns structure. The purpose is front-loaded. Every sentence is relevant, though formatting could be more formal.

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

Completeness2/5

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

Given the complexity of 5 parameters and a multi-step process, the description is incomplete. It lacks elaboration on the model parameters and does not fully explain the returned 'sources and reasoning'.

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

Parameters2/5

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

Schema description coverage is 0%, so the description is crucial. It lists parameter names but only explains 'question' briefly. The three model parameters are not differentiated, and 'notebook_id' gets minimal explanation.

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 verb 'ask' and resource 'question about your content'. It hints at 'detailed control' but does not explicitly differentiate from the sibling tool 'ask_simple'.

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 on when to use this tool versus alternatives like 'ask_simple' or 'search'. Lacks context about prerequisites or exclusionary conditions.

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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