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Opentopography

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. Description adds behavioral details: probes each entity, ranks by score, returns score/confidence/signal density. No contradictions. Good 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: first states core action, second adds use case and return info. No redundant information, front-loaded with key action. Efficient and clear.

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?

With no output schema and 4 parameters, description covers purpose, process, and return contents. Missing details on output structure (e.g., exact fields, types) and constraints (e.g., entity count range). However, sufficient for agent to use tool 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 description coverage is 100%, but the description adds extra meaning: entities first entry treated as 'subject', rest as competitors. Also notes models default and context usage. Adds value beyond schema alone.

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?

Description clearly states the tool compares AI visibility across multiple entities side-by-side, using ai_visibility_check, ranking by score, and surfacing most/least recognized. It distinguishes from sibling tools like ai_visibility_check by specifying multiple entities and competitive audit use case. Example question adds concrete context.

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?

Description explicitly states 'useful for competitive AI-marketing audits' and implies when to use. However, it does not explicitly state when NOT to use this tool over alternatives (e.g., single entity comparison), though sibling list suggests ai_visibility_check exists for single probes.

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

B3.4/5.0
Disambiguation2/5

The tool set has several near-duplicate entries (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; five polymarket_* tools), and the server name implies topography while most tools serve unrelated data lookups, making it hard to select the right tool for a task.

Naming Consistency3/5

Most tool names use snake_case and a verb-first style, but there are notable exceptions like 'datasets', 'dem', and 'forget', and the 'pipeworx' prefix is applied inconsistently (pipeworx_feedback, pipeworx_trending vs. ask_pipeworx). The pattern is readable but not fully uniform.

Tool Count2/5

With 34 tools, the count is excessive for a server named Opentopography, especially since only 3 tools (datasets, dem, point_elevation) relate to the implied domain. The bulk of tools belong to a general-purpose data and prediction-market service, creating a severe scope mismatch.

Completeness2/5

For the implied topography domain, the surface is severely incomplete: only dataset listing, a raster fetch, and a point elevation lookup are present, missing expected operations like elevation profiles, point cloud access, or data processing. For the broader Pipeworx domain, coverage is broad but this does not match the server's stated focus.

Resources