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Expression Atlas

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds value by detailing the return structure (per-model {score, confidence, signals, raw_response} + combined view) and the fact that the default model is free while Anthropic requires a personal key.

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?

Two well-structured sentences that front-load the purpose, provide parameter guidance, and mention use cases. No unnecessary words.

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?

Given the tool's moderate complexity (4 parameters, no output schema), the description covers the return structure and model behavior. It could optionally mention pagination or rate limits, but the annotations cover safety.

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 description coverage is 100%, so baseline is 3. The description adds meaning beyond schema: it clarifies that 'models' defaults to workers-ai, explains that _apiKey is only needed for anthropic, and that context disambiguates entities.

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 starts with a specific verb 'Probe' and clearly identifies the resource: one or more LLMs for visibility scoring. It distinguishes this tool from siblings like 'scan_competitor_ai_presence' and 'deep_research' by focusing on brand/product/topic visibility audits.

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 states use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and gives guidance on default model vs. BYO Anthropic API key. However, it does not explicitly mention when not to use this tool versus alternatives.

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

A3.7/5.0
Disambiguation3/5

Many tools have overlapping purposes (e.g., multiple Polymarket tools, multiple company information tools), but descriptions help distinguish them. However, the variety of domains (brand monitoring, betting, package scanning, memory, etc.) can confuse an agent.

Naming Consistency2/5

Tool names have no consistent naming pattern: some are verb_noun (validate_claim), some are noun_verb (bet_research), some are compound (ai_visibility_check), and some are single word (forget). This makes it hard to predict tool names.

Tool Count2/5

28 tools is high, and the set spans many unrelated domains (brand visibility, SEC/FDA data, Polymarket, npm packages, memory, etc.) while the server is named 'Expression Atlas' with only 2 tools related to that purpose. The tool count feels excessive and unfocused.

Completeness1/5

For a server named 'Expression Atlas', only two tools (get_experiment, search_experiments) cover the domain. There are no tools for submitting, updating, or deleting experiments, nor any for data visualization. The surface is severely incomplete.

Resources