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Leadconnector

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

A4.8/5.0
Behavior5/5

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

Annotations indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable context: the default free model, the need for an API key for Anthropic, cost implications, and a summary of the return structure (score, confidence, signals, raw_response). No contradiction 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?

The description is four sentences, front-loaded with the core purpose, and every sentence adds value. No redundancy or unnecessary details.

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 summarizes the return structure per model and combined view. For a tool with 4 parameters and optional configurations, the description is fully adequate.

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?

Schema coverage is 100%, but the description goes beyond: it gives examples for 'entity', explains default and options for 'models', describes the key format and pass-through for '_apiKey', and clarifies disambiguation for 'context'. This is highly informative.

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's purpose: to probe LLMs for knowledge about an entity and score visibility from 0-100. It uses specific verbs ('probe', 'score') and distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on per-model scoring.

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 concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model and BYO key option. However, it does not explicitly mention when to avoid using this tool or differentiate from very similar siblings like 'scan_competitor_ai_presence'.

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

A3.5/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx and ask_pipeworx_grounded, or bet_research and polymarket_edges. The mix of CRM, data, and betting tools creates confusion about which tool to use for a given task.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ai_visibility_check, ask_pipeworx) with prefixed names (leadconnector_*) and no clear pattern. Some names are vague (forget, remember) while others are specific.

Tool Count2/5

With 32 tools, the set feels overstuffed for a server named 'Leadconnector'. Many tools are unrelated to CRM (e.g., Polymarket, Pipeworx), and the CRM subset is too small to justify the count.

Completeness2/5

For the Leadconnector CRM domain, only basic read operations are provided; missing create, update, delete for contacts, campaigns, and opportunities. The non-CRM tools are comprehensive but don't match the server's name.

Resources