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Eol

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 indicate readOnlyHint, destructiveHint, etc. The description adds valuable behavioral context: default free model, BYO key for Anthropic with direct billing, and the structure of return data. 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.

Conciseness5/5

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

Three well-structured sentences that front-load the action and cover purpose, usage, and behavior without wasted words. Every sentence earns its place.

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 details return fields (score, confidence, signals, raw_response, combined view), making the tool's behavior fully understandable. Annotations, sibling list, and schema are well complemented.

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 meaning by explaining the default model, requiring _apiKey only for anthropic models, and clarifying billing responsibility, which goes beyond the schema's parameter descriptions.

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 uses specific verbs ('Probe', 'score visibility') and clearly identifies the resource (LLMs for a given entity). It distinguishes the tool from siblings like 'ask_pipeworx' and 'scan_competitor_ai_presence' by focusing on AI visibility scoring for brand 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?

The description explicitly states use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' While it doesn't list when not to use or name alternative siblings, the context is clear and actionable.

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

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, with detailed descriptions that eliminate ambiguity. Even similar tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by use case (casual, high-stakes, multi-faceted). The Polymarket and EOL tool suites are internally distinct.

Naming Consistency4/5

Naming mostly follows snake_case with verb_noun or prefix patterns, but there is inconsistency: e.g., 'ask_pipeworx' vs 'bet_research' vs 'deep_research'. The Polymarket and memory tool groups are internally consistent, but overall the server mixes conventions across sub-domains.

Tool Count3/5

34 tools is on the high side, but the server covers multiple domains (EOL taxonomy, Pipeworx data, Polymarket betting, memory, subscriptions). The count is borderline excessive for a focused server; meta-tools like discover_tools and suggest_questions help, but the sheer number can overwhelm an agent.

Completeness3/5

The tool set covers many data sources and analysis tasks well, but the server name 'Eol' implies a biological taxonomy focus, which is underserved (only 4 tools). For the broader implicit purpose of a research assistant, there are notable gaps like open-web search, image analysis, or document management.

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