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EV charging prices in Lithuania

EV charging price changes

price_changes
Read-onlyIdempotent

Significant changes of public EV charging prices in Lithuania since a date, per charging network and price class (ac, dc, hpc), newest first: from/to EUR/kWh incl. VAT and the change in %. "median" means the network's median station price moved; "partial" means many stations were repriced but the median held. History goes back at most 365 days.

Input Schema

NameRequiredDescriptionDefault
operatorNoCharging network name or slug, to list one network only.
since_dateYesEarliest date to include, YYYY-MM-DD (Europe/Vilnius). At most 365 days back.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds real value beyond that: newest-first ordering, the 365-day history ceiling, VAT-inclusive units, and the definitions of 'median' vs 'partial' repricing events. It still says nothing about volume of results, pagination, or how 'significant' is thresholded.

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?

Front-loaded with the core purpose, followed by one clarifying sentence for the two event types; every clause carries information. It is dense and quote-heavy, which slightly slows parsing, but nothing is redundant.

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?

With no output schema, the description carries the burden of explaining returns and does so: field set, units, VAT inclusion, sort order, and the meaning of the two change categories. Combined with the history limit and the simple two-parameter schema, an agent has everything needed to call and interpret it.

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%, so both parameters are already documented, including the YYYY-MM-DD format and the 365-day limit. The description's mention of network and price class does not add syntax or constraints beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific resource (public EV charging price changes in Lithuania) with a temporal filter and enumerates the returned fields (from/to EUR/kWh incl. VAT, change in %), so the agent knows exactly what it retrieves. It does not explicitly name how it differs from operator_tariffs or electricity_price, leaving the level-vs-change distinction to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the 'since a date' framing and the 365-day cap, but there is no explicit statement of when to prefer this over operator_tariffs or charging_cost, and no when-not guidance. An agent can guess the context but is not routed.

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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