Skip to main content
Glama

Chainlist

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable context: default model details, cost implications of using Anthropic (BYO key), and the return structure (score, confidence, signals, raw_response). No contradictions.

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?

The description is three sentences, front-loaded with the core action and output. Every sentence provides necessary information without redundancy.

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?

For a tool with 4 parameters and no output schema, the description covers core functionality, use cases, and output format. It lacks explicit mention of idempotency or read-only nature, but annotations fill that gap. Minor omission: no mention of score range or signal types.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, baseline is 3, but the description provides additional meaning: default model for 'models', explanation of '_apiKey' as BYO Anthropic key, and clarification that 'context' disambiguates entities. This exceeds schema info.

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 specifies the action ('probe one or more LLMs'), the target entity (business/brand/product/topic), and the output (visibility score 0-100 per model). It distinguishes from siblings by focusing on AI visibility scoring rather than general search or Q&A.

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 lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'). While it does not explicitly exclude alternatives or mention sibling distinctions, the context is sufficient for usage decisions.

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

All Chainlist toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.2/5.0
Disambiguation4/5

Tools have distinct purposes with clear descriptions, but ask_pipeworx_beta currently duplicates ask_pipeworx, and the multiple prediction market tools could be confusing without careful reading.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun or noun_verb pattern, with no mixing of conventions.

Tool Count3/5

34 tools is high for a single server, covering chain data, Pipeworx research, and prediction markets. While well-organized, the breadth pushes the boundary of manageable scope.

Completeness4/5

The tool set covers major query operations for chains, entities, and data sources, with subscription and memory features. Minor gaps like lack of chain creation are acceptable given the server's focus on data retrieval.

Resources