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swisstransport

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

The description discloses key behavioral details beyond the annotations: it internally probes each entity with `ai_visibility_check`, treats the first entity as the 'subject' for narrative, and returns a ranked list with score, confidence, and signal density. This adds value beyond the read-only/idempotent hints, though some details like error handling or rate limits are omitted.

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 three sentences, each earning its place: core purpose, mechanism/behavior, and use case with output format. It is front-loaded and free of redundant details, making it easy to parse quickly.

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?

The tool has no output schema, so the description compensates by explicitly stating the return format (ranked list with score, confidence, signal density). It also provides a concrete usage scenario and notes the entity count constraint. Minor gaps remain (e.g., rate limits, error cases), but for a read-only tool with well-documented parameters, it is nearly complete.

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 coverage is 100%, and the schema already describes all parameters, including the first-entity-as-subject behavior for `entities`. The description does not add new parameter-specific meaning beyond what the schema provides, so it remains at the baseline for high schema coverage.

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's function: comparing AI visibility across multiple entities side-by-side. It distinguishes itself from the sibling tool `ai_visibility_check` by explicitly referencing it and emphasizing the multi-entity comparison and ranking. The verb 'compare' and resource 'AI visibility across multiple entities' are specific and unambiguous.

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 context for use ('competitive AI-marketing audits') and an illustrative example question. It implicitly indicates this tool is for multi-entity comparison, but it does not explicitly state when not to use it or name alternatives like `compare_entities`. Thus it has clear context but lacks explicit exclusions.

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
Disambiguation3/5

Most tools have distinct names and purposes, but the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) creates ambiguity as they serve overlapping needs with slight variations. Additionally, the transport tools (get_connections, get_stationboard, search_stations) are clearly distinct from the rest, but the overall set mixes domains, making it harder for an agent to know which tool to pick.

Naming Consistency3/5

All tool names use snake_case, but there is no consistent pattern: some start with verbs (get_, list_, search_, remember, forget), some with nouns (entity_profile, recent_changes, recent_alerts), and others with adjectives (ai_visibility_check, deep_research). This inconsistency, while not chaotic, makes the set less predictable.

Tool Count1/5

The server name 'swisstransport' implies a narrow Swiss transport focus, but with 34 tools, only 3 are transport-related. The count is vastly inappropriate for the suggested purpose. Even considering the actual broad domain (data query, prediction markets, memory), 34 tools is on the high side and likely overwhelming for any single server.

Completeness4/5

Inferring the domain from the tool descriptions, the set covers a wide range of capabilities: data query (ask_pipeworx, deep_research), entity comparison (compare_entities), prediction markets (polymarket_*), memory (remember/recall), subscriptions, and more. There are few obvious gaps given the scope; for example, broader financial data is accessible through ask_pipeworx. The transport subset is minimal but present.

Resources