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Send That Email

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

A4.1/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, and non-destructive behavior. The description adds value by explaining the probe nature, return structure (per-model scores, confidence, signals, raw_response), and cost details (free default, BYO key for Anthropic). No contradictions 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured paragraph that front-loads the purpose and key details. It is concise with no fluff, though a bulleted list could improve scannability.

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?

Despite lacking an output schema, the description explicitly states the return structure (per-model fields + combined view). It covers all parameters and provides sufficient context for a probe tool with good annotations. Minor missing details like rate limits or error handling, but overall adequate.

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. The description adds some context beyond schema (e.g., default model, apiKey passthrough to Anthropic), but the schema already thoroughly describes each parameter. The added value is marginal.

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 clearly states the tool probes LLMs for knowledge about a business and scores visibility 0-100 per model. It uses specific verb 'probe' and resource 'LLMs' with a clear output. It distinguishes from sibling tools like scan_competitor_ai_presence by focusing on scoring visibility.

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 provides explicit context on when to use (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains model choice with cost implications (free default vs. BYO key for Anthropic). However, it does not state when not to use the tool or mention alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Send That Email toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer queries with subtly different guarantees. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_kalshi_spread, polymarket_edge_tracker) also has fuzzy boundaries. Despite detailed descriptions, an agent could easily pick the wrong tool.

Naming Consistency2/5

Naming conventions are inconsistent: there are verb_noun names (discover_tools, resolve_entity), noun-based names (polymarket_edges, pipeworx_trending), single verbs (remember, recall, forget), and odd constructions like send_that_email_analyze. No clear pattern dominates, making it hard to predict tool names.

Tool Count2/5

32 tools is heavy for a focused server, and most tools are unrelated to the server's apparent email-sending purpose. The count feels bloated and the scope mismatched, though it is not extreme enough for a 1.

Completeness1/5

Given the server name 'Send That Email', the tool surface is severely incomplete: there is only an email analysis tool and no actual sending, drafting, or mailbox management. The bulk of the tools address data lookup and research, leaving the core email workflow entirely unimplemented.

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