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Microsoft Onenote

by io.github.pipeworx-io

Generate llms.txt

generate_llms_txt
Read-onlyIdempotent

Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL of the site to summarize, e.g. "https://example.com" or a specific landing page.
max_linksNoMaximum number of link entries to include (default 25, max 50).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Describes the extraction process and output format, adding context beyond annotations. Annotations already indicate read-only, idempotent, non-destructive; description confirms fetching and emitting markdown, which is consistent.

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?

Single, well-structured sentence that front-loads purpose, then provides detail and use cases. No redundant or extraneous information.

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 2 parameters, no output schema, and rich annotations, the description fully covers what the tool does, how it works, and when to use it. Output is described as 'single text blob ready to drop at site-root/llms.txt'.

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 100%, so baseline is 3. Description does not add new semantics beyond schema but reinforces usage context. No additional parameter details beyond 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?

Clearly states the verb 'Generate', the resource 'llms.txt file', and the context 'for any URL so AI crawlers can index the site cleanly'. Differentiates from sibling tools by being specific to generating llms.txt, a distinct action.

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?

Explicitly lists use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' No explicit when-not-to-use or alternatives, but context is clear.

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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TDQS

A3.5/5.0
Disambiguation2/5

Several tools are genuinely easy to confuse: ask_pipeworx and ask_pipeworx_beta are explicitly described as currently identical, and the five polymarket_* tools overlap significantly in purpose. There are also wrapper-like pairs such as ai_visibility_check vs. scan_competitor_ai_presence and entity_profile vs. recent_changes vs. compare_entities that require reading long descriptions to disambiguate.

Naming Consistency3/5

The names are mostly lowercase snake_case and readable, but there is no consistent verb_noun pattern: entity_profile and recent_changes are noun phrases, pipeworx_trending and pipeworx_feedback use a prefix, ask_pipeworx_beta is a single-family variant, and scan_competitor_ai_presence uses a different structure from ai_visibility_check. The naming is not chaotic, but it is a mix of conventions.

Tool Count1/5

A server labeled Microsoft Onenote exposes 31 tools, none of which actually relate to OneNote note-taking, notebooks, or pages. Even as a general research/prediction-market server, 31 tools is far above the coherent range, and for the stated product purpose this count is wildly inappropriate.

Completeness1/5

For the server's declared OneNote domain, there is zero coverage: no tools for creating, reading, updating, or deleting notes, pages, sections, or notebooks. The actual tool surface is centered on Pipeworx data lookups, Polymarket arbitrage, and memory helpers, which leaves the apparent note-taking domain completely unrepresented.

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