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Geoapify

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 already indicate readOnly, idempotent, and non-destructive behavior. The description adds value by explaining that using Anthropic requires a BYO API key and incurs direct costs, and by detailing the return structure. No contradictions found.

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 concise, front-loaded with the main action, and includes all necessary information without fluff. Every sentence adds value.

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?

Given 4 parameters and no output schema, the description adequately covers return values (per-model object), behavioral nuances (free vs paid), and intended use cases. It is complete for an agent to select and invoke correctly.

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 enhances meaning by explaining default models, API key usage, and the purpose of context for disambiguation. It adds practical examples and clarifies behavior beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 knowledge about an entity and scores visibility. The verb 'probe' and resource 'LLMs' are specific, and the output format is indicated. However, it does not explicitly differentiate from sibling tools like 'scan_competitor_ai_presence' which may have overlapping functionality.

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 explains default model usage, optional API key for Anthropic, and typical use cases (AI-marketing audits, pre-launch brand checks). It provides clear context but does not specify when not to use this tool versus alternatives.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, entity_profile, compare_entities, and validate_claim, all retrieving entity data. Agents may struggle to choose correctly between them. Likewise, bet_research, polymarket_edges, and polymarket_arbitrage cover similar prediction market territory.

Naming Consistency3/5

All tool names use underscores, but the verb/noun order varies: compare_entities (verb_noun), entity_profile (noun_noun), scan_competitor_ai_presence (verb_noun), bet_research (noun_verb). Some names are overly long (scan_competitor_ai_presence). The pattern is readable but not fully consistent.

Tool Count3/5

With 31 tools, the count is high but not extreme. However, the set covers geospatial, data retrieval, prediction markets, npm scanning, and memory/feedback - too broad for a single server. Many tools feel added without clear justification, making the set feel bloated.

Completeness2/5

The geospatial subset is incomplete (missing elevation, distance matrix, isochrones). The data access tools overlap rather than form a complete API surface (e.g., no entity update/delete). Prediction market tools are numerous but redundant. The server tries to do too much and lacks depth in any area.

Resources