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Glama

Microsoft Onenote

by io.github.pipeworx-io

Remember

remember
Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

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

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations, description reveals key behavioral traits: key-value pair scoped by identifier, persistence based on authentication (24 hours for anonymous), and that it pairs with recall/forget. No contradiction with annotations.

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?

Four sentences, each earning its place: purpose, usage guidance, behavioral details, and relationship to siblings. No redundancy, perfectly front-loaded.

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 simple 2-parameter tool with no output schema and supportive annotations, the description covers all necessary context: purpose, usage, persistence, and pairing with other tools.

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

Parameters4/5

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

Schema already describes both parameters clearly (100% coverage). Description adds context with inline examples for key patterns and clarifies value as free text. Adds moderate value 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?

Clear verb 'save' and resource 'data to reuse later'. Distinguishes from sibling tools 'recall' and 'forget' by naming them and describing how they pair.

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

Usage Guidelines5/5

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

Explicitly specifies when to use: 'discover something worth carrying forward'. Provides examples of data types and contrasts with anonymous vs authenticated sessions. References sibling tools for retrieval and deletion.

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.

Resources