Skip to main content
Glama

swisstransport

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

Schema Changelog

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

  1. Changed4 schema fields changed
    • addedInput schema / properties / _apiKey
      Added value: +{
+  "description": "Optional Anthropic API key (sk-ant-...) — only needed if \"anthropic\" is in models. Passed straight through to api.anthropic.com.",
+  "type": "string"
+}
    • changedInput schema / properties / context / description
      Previous value: -"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\", \"Polish painter\"). Helps disambiguate common names."New value: +"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\"). Helps disambiguate common names."
    • changedInput schema / properties / entity / description
      Previous value: -"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\", \"the company behind ChatGPT\"."New value: +"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\"."
    • addedInput schema / properties / models
      Added value: +{
+  "description": "Which models to probe. Supported: \"workers-ai\" (free default), \"anthropic\" (requires _apiKey). Omit for just workers-ai.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. Added

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover read-only, open-world, and idempotent behavior. The description adds valuable context beyond these: the tool makes external LLM calls, the default model is free, and passing _apiKey invokes Anthropic with direct costs to the user. It also discloses the return shape (per-model plus combined view), giving the agent a fuller behavioral picture.

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?

Three sentences, front-loaded with action and output. The first sentence states what it does and the output metric. The second explains model options and cost. The third gives return format and use cases. Every sentence earns its place with no filler.

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?

Despite no output schema, the description explicitly lists the per-model return fields and mentions a combined view. It conveys the tool's scope, default behavior, cost implications, and typical use cases. For a moderate-complexity probe tool with good annotations and full schema coverage, this is 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 description coverage is 100%, so the input schema already fully documents all four parameters. The description reinforces the default model and API key behavior but adds little beyond the schema; for example, the '_apiKey only needed if anthropic is in models' nuance appears in both. Baseline 3 is appropriate.

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 opens with a specific verb and resource: 'Probe one or more LLMs for what they know' and clearly states the scoring output (0-100 per model). It covers general entities (business/brand/product/topic) and returns a structured result, making the tool's role distinct from query-style siblings like ask_pipeworx or deep_research.

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 clear context for use by listing follow-on use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and notes the default free model versus the BYO-key Anthropic option. However, it does not explicitly name alternatives or exclusion criteria, such as 'for competitor-specific analysis use scan_competitor_ai_presence instead'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All swisstransport toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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