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Openrouter

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 declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds that it makes external calls to LLMs, the default model is free, and using Anthropic requires an API key with direct payment to Anthropic. This provides additional context beyond 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?

Two sentences plus a use-case list. Front-loaded with action and output. Every sentence adds value: one for core functionality and output format, one for model/cost details, one for use cases. No wasted words.

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 having 4 parameters and no output schema, the description explains the return format (per-model {score, confidence, signals, raw_response} + combined view), default behavior, optional API key, and typical use cases. All necessary context is covered.

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%, but the description adds value by explaining the purpose of each parameter: default model is free, _apiKey is needed for Anthropic, context helps disambiguate. This goes beyond the basic schema 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 clearly states the tool probes LLMs for entity knowledge and returns a visibility score (0-100). It specifies the verb ('Probe'), resource ('LLMs'), and the output format. This distinguishes it from siblings like ask_pipeworx (which likely answers questions) and compare_entities (which compares entities).

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 lists explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains when to provide an API key for Anthropic. However, it does not explicitly state when NOT to use this tool or compare it to alternatives like ask_pipeworx or deep_research.

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

Several tools overlap in purpose: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, and the polymarket_edges, polymarket_arbitrage, bet_research, and polymarket_fill_risk tools all orbit market-opportunity analysis from slightly different angles. The descriptions are unusually detailed and often say when to prefer one tool over another, but an agent must read carefully to avoid misselection.

Naming Consistency4/5

Names are consistently snake_case and mostly follow a verb_noun pattern (list_models, get_model, resolve_entity, validate_claim, scan_dependency), with predictable domain prefixes like polymarket_* and pipeworx_*. A few noun-first names like entity_profile, bet_research, and ai_visibility_check deviate slightly, but the overall convention is readable and coherent.

Tool Count2/5

34 tools is well into the 'too many' range, especially for a server named Openrouter that actually spans several unrelated domains: model catalog, Pipeworx data retrieval, Polymarket analysis, memory, subscriptions, and website tooling. Many individual tools are justified, but the set is overstuffed and would be better split into focused servers.

Completeness3/5

Each sub-domain is reasonably covered: model catalog has list/get/compare, memory has remember/recall/forget, subscriptions have subscribe/list/unsubscribe/recent_alerts, and Pipeworx querying has multiple modes plus discovery. However, a server named Openrouter exposes no way to actually run completions or route requests through OpenRouter, and the unrelated bundled domains make the overall surface feel scattered rather than complete.

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