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Geonet Nz

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

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, so the safety profile is clear. The description adds valuable behavioral context: it probes each entity, ranks by score, and returns a ranked list with score, confidence, and signal density. 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 two sentences: the first states the core action, the second provides use context and output details. Every sentence is informative and there is no fluff.

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?

Even without an output schema, the description describes the output (ranked list with score, confidence, signal density). It also specifies constraints like 2-8 entities and first as subject. Given the complexity, the description is complete.

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 description coverage is 100%, so baseline is 3. The description adds meaning: it explains that the first entity is treated as the subject, that 'models' defaults to workers-ai and requires an API key for anthropic, and that 'context' disambiguates common names. This adds value beyond the 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 clearly states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, and ranks by score. It distinguishes itself from sibling tools like ai_visibility_check (single probe) and compare_entities (general comparison).

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 a concrete use case ('competitive AI-marketing audits') and an example query. It implies when to use this tool over ai_visibility_check (single vs. multiple entities), but does not explicitly state when not to use or list 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

A3.9/5.0
Disambiguation2/5

Several tools are deliberately near-identical variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) or have overlapping routing/query purposes (deep_research, validate_claim, discover_tools, suggest_questions). The Polymarket cluster also has five tools that all surface 'edges' or 'arbitrage' with only subtle differences. Only the three GeoNet tools and the memory trio are cleanly distinct.

Naming Consistency3/5

The dominant style is snake_case, and clusters like ask_pipeworx_* and polymarket_* are internally consistent. However, verb/noun patterns vary widely across the set: some tools begin with verbs (get_quake, scan_dependency, generate_llms_txt), some are noun phrases (entity_profile, volcano_alerts, recent_changes), and some are plain nouns (polymarket_arbitrage). Readable but not a single predictable convention.

Tool Count2/5

34 tools is well above the 'heavy' threshold, and the server is named 'Geonet Nz' while only 3 of its 34 tools relate to GeoNet. The overwhelming majority are Pipeworx/data/prediction-market tools, making the server's scope massively broader than its name implies. The count itself is not unreasonable for the actual feature sprawl, but it is inappropriate for the apparent purpose.

Completeness4/5

Taking the real scope as 'general authoritative data research + memory + subscriptions + a little GeoNet', the surface is quite complete: query entry points, grounded verification, deep research, entity resolution, comparisons, claim validation, monitoring subscriptions, memory persistence, and feedback are all present. The GeoNet-specific subset is also adequate (get one, list recent, volcano alerts). Minor gaps exist, like no general GeoNet station/well data or subscription editing, but nothing causes dead ends.

Resources