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words

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/idempotent/destructive hints, so the bar is to add context beyond that. The description adds meaningful behavioral details: the default free model, and that providing _apiKey triggers external calls to Anthropic with direct billing ('you pay Anthropic directly'). This enriches the agent's understanding of side effects and cost.

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, front-loaded with the core function and scoring output, then return format and use cases. Every sentence adds value: purpose, default/cost, return shape, and applicability. No 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?

The description fully explains the tool's behavior, return shape, defaults, costs, and use cases. Annotations cover safety. No output schema exists, but the per-model return structure is explicitly described. This is more than sufficient for an agent to decide when and how to invoke it.

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%, so baseline is 3. The description adds practical semantics beyond schema: it explains the default model behavior, clarifies that _apiKey enables Anthropic probing and costs the user, and states that context 'helps disambiguate common names'. This is a useful supplement.

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 opens with a specific verb ('Probe') and resource ('one or more LLMs'), then clarifies the output ('score visibility (0-100) per model'). It clearly differentiates from sibling tools by focusing on AI-model knowledge about an entity, distinct from entity_profile or compare_entities.

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?

Provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and defaults ('Default model is Workers AI Llama-3.3-70b (free)'). However, it doesn't mention when not to use it or contrast with similar siblings like scan_competitor_ai_presence, so it falls short of full exclusions.

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

A3.6/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is overlap between query tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research, and between prediction market tools like bet_research and polymarket_edges. Descriptions help differentiate, but the boundaries are not always clear.

Naming Consistency2/5

Tool names are all snake_case but lack a consistent pattern. Some start with verbs (ask, compare, find), others are nouns (autocomplete, entity_profile), and many are long phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). The naming feels ad-hoc and not easy to predict.

Tool Count2/5

At 35 tools, this server is over-packed for a server named 'words'. Many tools are unrelated to words (e.g., prediction markets, subscriptions, entity profiles). The scope is too broad, making it feel like a catch-all rather than a coherent set.

Completeness3/5

The word tools are limited (only 6), leaving obvious gaps for a word-focused server (e.g., no dictionary lookup, no word definitions). However, the server covers a wide range of data domains through meta-tools like ask_pipeworx, which compensates but makes the purpose unclear.

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