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Country State City

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 β€” 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

A3.9/5.0
Behavior3/5

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 probes each entity with another tool and returns ranked output, but no additional behavioral details (e.g., rate limits, cost, or time impact) beyond what annotations cover.

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 a single paragraph of four sentences, front-loaded with the core purpose. It is concise but could be slightly more structured (e.g., separate output details). No unnecessary words.

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 no output schema, the description adequately explains the return values (ranked list with score, confidence, signal density). It does not cover edge cases like error handling or parameter interactions, but overall it is sufficient for this tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not elaborate on parameter details beyond what the schema already provides; it only mentions entities and context in passing.

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, ranks, and returns a ranked list with scores. It distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the AI-marketing audit context and use of ai_visibility_check.

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 clear use case ('competitive AI-marketing audits') and an example. It implies usage for multi-entity comparison but does not explicitly state when not to use or mention alternatives like ai_visibility_check for single-entity checks.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap enough to cause misselection: ask_pipeworx and ask_pipeworx_beta are functionally identical right now, ask_pipeworx_grounded and validate_claim both handle factual lookup/verification, and ai_visibility_check/scan_competitor_ai_presence are near duplicates in scope. The country/state/city tools are distinct but sit in a pile of unrelated Pipeworx tools, adding confusion.

Naming Consistency3/5

All names are lower_snake_case and families like polymarket_* and ask_pipeworx* help group tools, but the verb_noun convention is inconsistent: entity_profile, deep_research, recent_alerts, and pipeworx_trending are noun phrases, while remember/forget/subscribe are bare verbs. It is readable but not a predictable pattern.

Tool Count2/5

34 tools is above the practical ceiling for a focused MCP server, and the mismatch is severe: only 3 tools match the 'Country State City' name while 31 belong to a broad Pipeworx platform. A geographic server would need roughly 3-6 focused tools; this surface is bloted.

Completeness2/5

For the stated Country State City domain, list_countries/get_states/get_cities provide the basic hierarchy, but there is no city search, country/state detail lookup, or attribute discovery beyond the three list endpoints, making the useful geographic surface thin. For the Pipeworx domain the coverage is broader, but that confirms the identity mismatch and obscures the server's actual purpose.

Resources