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Insee

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

A4.2/5.0
Behavior4/5

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

Annotations indicate read-only, idempotent, non-destructive. The description adds that Anthropic probing requires a BYO key and direct payment, which is beyond annotations. 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?

Three well-structured sentences: purpose, key parameters, use cases. No wasted words, front-loaded with main action.

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?

No output schema, but the description details return format (per-model {score, confidence, signals, raw_response} + combined view). Covers essential behavior.

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

Parameters3/5

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

Schema coverage is 100%, so baseline 3. The description adds value by explaining the default model and BYO key mechanism, but does not significantly expand beyond schema.

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') and identifies the resource (LLMs, business/brand/product/topic). It distinguishes from sibling tools by focusing on multi-model visibility scoring, not single answers or comparisons.

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 checks, competitive monitoring). It does not explicitly state when not to use or alternatives, but the context is clear.

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

A3.9/5.0
Disambiguation3/5

Most tools have detailed, carve-out descriptions, but ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same routing core, with the beta version currently identical to the stable one. The Polymarket and company-research clusters are better differentiated, but the number of overlapping research/query entry points still creates real selection risk.

Naming Consistency3/5

The set is consistently snake_case and has coherent prefixes like ask_pipeworx_ and polymarket_, but it mixes verb_noun names (resolve_entity, scan_dependency, discover_tools) with noun-phrase names (entity_profile, bet_research, recent_changes) and one-word verbs (remember, recall, forget). The naming is readable but does not follow one predictable pattern.

Tool Count2/5

With 32 tools, the server exceeds the 25+ threshold for too many tools and feels like a broad platform dump rather than a focused toolkit. Several utility, memory, and meta-discovery tools could reasonably live in separate servers, and the Insee name makes the breadth especially unfocused.

Completeness4/5

For the broad data-research platform it actually exposes, the coverage is strong: general lookup, grounded verification, deep research, entity resolution, company profiles, comparisons, change feeds, subscriptions, and memory all have working lifecycles. The main gap is that some unrelated utilities like scan_dependency and generate_llms_txt feel tacked on rather than part of a missing core workflow.

Resources