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Data Nashville

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as true/true/true/false, indicating a safe, read-only, idempotent operation. The description adds valuable context: it explains the tool probes each entity using ai_visibility_check and returns a ranked list with score, confidence, and signal density. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, with the first sentence stating the core action and output, and the second providing usage context and output details. It is efficient and front-loaded, though the second sentence is slightly verbose. Every sentence adds value.

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?

Given 4 parameters, 100% schema coverage, no output schema, the description explains the output structure (ranked list with score, confidence, signal density) and the composition (uses ai_visibility_check). It does not elaborate on score ranges or the meaning of 'signal density', but the description is fairly complete for a composite tool.

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 meaning beyond the schema: it explains that the first entity is treated as the 'subject' for narrative, that models default to workers-ai, and that _apiKey is only needed for Anthropic. This helps an agent understand parameter roles and defaults.

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 side-by-side, specifying the verb 'compare', the resource 'AI visibility', and the scope (multiple entities). It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (general comparison) by focusing on AI visibility. The output (ranked list with score, confidence, signal density) is also mentioned.

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 gives an example question ('does Claude know about us as well as our competitors?'). It implies the tool is for multi-entity comparison, suggesting single-entity checks should use ai_visibility_check. However, it does not explicitly state when not to use this tool or list alternative siblings.

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

Several tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical in current behavior, and ask_pipeworx_grounded, validate_claim, and deep_research all overlap with the same underlying routing. The six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) share domain and response fields, making misselection likely despite detailed descriptions.

Naming Consistency3/5

Snake_case is used throughout and prefix families (ask_pipeworx_*, nashville_*, polymarket_*, pipeworx_*) are consistent within themselves. However, the overall set mixes verb-first names (ask, compare, remember, subscribe) with noun/adjective-first names (entity_profile, recent_alerts, recent_changes, bet_research), so no single predictable pattern governs all tools.

Tool Count2/5

At 34 tools, the server exceeds a coherent surface, especially for a server named 'Data Nashville' where only 3 of 34 tools actually serve Nashville data. The count is inflated by several largely unrelated feature families (AI visibility, prediction markets, memory, subscriptions), making the set feel like an aggregation of multiple products rather than one focused server.

Completeness3/5

Individual families are fairly complete: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, and Nashville has discovery/query/recent access. But the overall domain is unclear, and the Nashville-specific surface is thin (no search across datasets, no non-ArcGIS sources), leaving notable gaps relative to the server name.

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