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Gmail

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.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive; the description adds scoring range (0-100), per-model return structure, and cost implications for Anthropic API key, providing useful context beyond 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?

Single dense paragraph with all key information front-loaded, no waste.

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?

No output schema, but description clearly details return values (per-model score, confidence, signals, raw_response) and combined view, fully covering what the agent needs to know.

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?

With 100% schema coverage, baseline is 3; the description adds value by explaining default model, when _apiKey is needed, and how context disambiguates, 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?

The description explicitly states it probes multiple LLMs for visibility scoring of a business/brand/product/topic, distinguishing it from sibling tools like compare_entities or scan_competitor_ai_presence.

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?

Clear use cases are given (AI-marketing audits, pre-launch brand checks, competitive monitoring) and default model behavior is explained, though alternatives like compare_entities are not explicitly excluded.

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

B3.4/5.0
Disambiguation2/5

There is significant overlap among tools, particularly within the Pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket family (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). These tools have similar purposes, making it hard for an agent to distinguish them at a glance. The Gmail tools are a small, distinct cluster, but the overall set is confusing.

Naming Consistency3/5

All tool names use snake_case, but the verb_noun pattern is inconsistent. Many start with verbs (ask_pipeworx, compare_entities, discover_tools, etc.), but some use noun_verb (bet_research), noun_noun (entity_profile), or adjective_noun (deep_research). This mixed pattern reduces predictability.

Tool Count2/5

With 36 tools, the count is high, but the server name 'Gmail' suggests a focused email service. Only 5 tools are Gmail-related, while the rest cover a vast, unrelated domain (Pipeworx, Polymarket, etc.). This mismatch makes the tool count inappropriate for the server's apparent purpose.

Completeness2/5

For the Gmail domain, the tool surface is incomplete (e.g., missing delete, archive, modify labels). For the broader Pipeworx/Polymarket domain, the tools are extensive but lack clarity in coverage. The server attempts to cover too many domains without sufficient depth in any, leading to notable gaps.

Resources