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onenote-mcp-python

onenote-mcp-python

by eddyficial
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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onenote_knowledge_digest

Read-only

Generate a source-grounded executive or detailed digest from OneNote pages, sections, or notebooks, returning key points, action items, decisions, risks, and questions for synthesis.

Instructions

Build a source-grounded executive or detailed digest across a page, section, notebook, or all open notebooks. Returns per-page key points plus action items, decisions, risks, and questions for the client to synthesize.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "onenote_knowledge_digest"
  }
}

Input Schema

NameRequiredDescriptionDefault
modeNo
start_idNoOptional notebook, section group, or section ID. Empty means all open notebooks.
max_pagesNoSafety cap from 1 to 500. Default 100.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context: it is source-grounded and produces per-page key points plus specific item categories. This goes beyond the annotations. No contradictions.

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?

Two sentences, front-loaded with the purpose, then output specification. No filler or repetition. Every word earns its place.

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?

For a read-only tool with no output schema, the description adequately covers the return format (per-page key points, action items, etc.) and the scope options. It does not explain mode differences, but the enum covers that. The safety cap on max_pages is in the schema. Overall complete for the tool's complexity.

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 description coverage is 67% (start_id and max_pages are described, mode only has an enum). The description doesn't add any parameter-level detail beyond what the schema provides. It indirectly clarifies start_id's role by listing scopes, but that's already implied. Meets baseline but doesn't elevate.

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 opens with a specific verb ('Build') and a clear resource ('source-grounded... digest'), and specifies the scope (page, section, notebook, all open notebooks). It also states the returned content types (key points, action items, decisions, risks, questions), which fully distinguishes it from sibling tools like search or hierarchy.

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 description gives clear context on when to use the tool: when a digest is needed across any of those scopes, for the client to synthesize. It doesn't explicitly name alternatives or exclusions, but the purpose is clear enough that an agent can infer when to invoke it. It lacks an explicit 'when not to use' statement, so not a 5.

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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