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Tankerkoenig

by io.github.pipeworx-io

Tankerkoenig Station Details

tankerkoenig_station_details
Read-onlyIdempotent

Full detail for one German gas station by its Tankerkoenig station id (UUID from tankerkoenig_stations_nearby): opening times, current E5 / E10 / diesel prices in EUR per liter, brand, address, state, and open status. MTS-K real-time data, Germany only. Example: tankerkoenig_station_details({ station_id: "94e70fc4-b22f-4e5a-877f-bc1082cdae81" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_apiKeyNoOptional: your own free Tankerkoenig API key (creativecommons.tankerkoenig.de)
station_idYesStation UUID, e.g. "94e70fc4-b22f-4e5a-877f-bc1082cdae81" (from tankerkoenig_stations_nearby)

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the tool as readOnly, idempotent, and non-destructive. The description adds behavioral context by listing the specific data returned (prices in EUR per liter, opening times, brand, etc.) and noting real-time MTS-K data and regional restriction. It could mention behavior for invalid station IDs.

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 brief—two sentences plus an example—without redundancy. It front-loads the purpose and key deliverables. Every sentence adds value, and the example clarifies usage.

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 the simple parameter set (2 params, 100% schema coverage), rich annotations, and no output schema, the description is largely complete. It covers what data is returned and context (Germany-only, real-time). Missing error handling details but adequate for the 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 coverage is 100% with descriptions for both parameters. The description reinforces the station_id as a UUID and provides an example, but adds no new semantic detail beyond what the schema already conveys. 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 clearly states the tool retrieves full details for one German gas station by its Tankerkoenig station ID, including specific fields like opening times, prices, brand, address, state, and open status. It distinguishes itself from siblings by focusing on a single station's comprehensive data, unlike 'tankerkoenig_stations_nearby' which lists stations.

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 indicates the tool should be used with a station UUID obtained from 'tankerkoenig_stations_nearby' and restricts usage to Germany. It implicitly guides the workflow, but does not explicitly state when to avoid using this tool in favor of alternatives like 'tankerkoenig_prices'.

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

All Tankerkoenig toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools occupy nearly identical roles: ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), ask_pipeworx_grounded, and deep_research all answer research questions; polymarket_edges, bet_research, and polymarket_arbitrage overlap heavily on prediction-market opportunities; entity_profile, compare_entities, and recent_changes overlap on company research. The detailed descriptions help, but the clusters create real misselection risk.

Naming Consistency4/5

Nearly all tools follow a readable snake_case convention, many with verb_noun structure (resolve_entity, list_subscriptions, validate_claim, scan_dependency). Minor deviations exist: tankerkoenig_stations_nearby plural vs tankerkoenig_station_details/prices singular, plus noun-style names like pipeworx_feedback and pipeworx_trending, but the overall pattern is predictable.

Tool Count2/5

34 tools is heavy, and the problem is compounded by the server being named Tankerkoenig: only 3 of the 34 tools relate to German fuel prices while the other 31 are an unrelated Pipeworx/Polymarket/memory/subscription toolkit. This is a sprawling, unfocused surface rather than a well-scoped set.

Completeness3/5

For the nominal Tankerkoenig domain, stations_nearby + station_details + prices cover core lookups, though station search by name and price history are missing. For the broader bundled data/prediction-market domain, coverage is extensive but has notable gaps such as no trade execution, no general web search, and several redundant access paths that complicate the surface.

Resources