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Glama

Microsoft Onenote

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Discloses default model (Workers AI) and optional Anthropic probing with BYO key, payment implications, and return structure. Complements annotations (readOnly, idempotent, openWorld) with rich operational detail.

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?

Three concise sentences that front-load the primary purpose and add key details without redundancy.

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?

Despite no output schema, the description clearly defines the return structure (per-model object with score, confidence, signals, raw_response plus combined view). Covers all needed context for a 4-parameter tool.

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 coverage is 100% with good descriptions. Description adds value by explaining the default model behavior and clarifying that _apiKey is only needed for Anthropic, going 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?

Clearly states the tool probes LLMs for brand visibility and returns a score 0-100 per model. Distinguishes itself from siblings like scan_competitor_ai_presence by focusing on visibility scoring.

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 (AI-marketing audits, pre-launch checks, competitive monitoring). Provides context for when to use, though does not explicitly contrast with sibling tools or state when not to use.

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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