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Energy Charts

by io.github.pipeworx-io

Generation Mix

generation_mix
Read-onlyIdempotent

The CURRENT ELECTRICITY GENERATION MIX BY FUEL for a European country — how much power is coming from solar, wind, nuclear, gas, coal, hydro, biomass right now, with each fuel's MW and percentage share. Answers "what is Germany's generation mix", "how much of France's electricity is nuclear", "Germany power generation by source", "what fuels are generating electricity in ". Accepts a country NAME ("Germany", "France") or 2-letter code. Also returns renewable vs fossil vs nuclear totals. Source: Fraunhofer ISE energy-charts, keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
atNoOptional date YYYY-MM-DD for a historical mix (defaults to the latest available data).
countryYesCountry name ("Germany", "France", "Spain", "Poland") or 2-letter code ("de", "fr"). Use "eu" for the EU aggregate.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds value by disclosing the data source ('Fraunhofer ISE energy-charts, keyless') and the return structure (MW and percentage shares, totals by category), which goes beyond annotations.

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 front-loaded with the key purpose, includes example queries, and provides parameter details in a logical flow. It is slightly lengthy but every sentence adds value, making it efficient for its content.

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?

Given 2 parameters with full schema coverage, no output schema, and the tool's complexity (moderate), the description is comprehensive. It explains the output (MW, percentage, category totals), input options, and data source, leaving no major gaps for typical use cases.

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

Parameters4/5

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

Schema coverage is 100%, describing both parameters. The description adds meaningful clarity: it notes that 'at' is optional and defaults to latest, and emphasizes that 'country' accepts names, 2-letter codes, and 'eu' for the aggregate. This enriches the schema definitions.

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 uses a specific verb ('shows') and clearly identifies the resource ('current electricity generation mix by fuel for a European country'), with concrete examples of queries. It distinguishes itself from siblings like 'electricity_price' or 'installed_power' by focusing on generation mix.

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 use cases ('answers "what is Germany's generation mix"') and specifies input formats (name or 2-letter code, including 'eu'). It does not explicitly state when not to use this tool or mention alternatives, but the context is sufficient.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_grounded, and deep_research, which all perform similar data retrieval. The multiple Polymarket tools also overlap in focus, making it unclear which to use for a given task.

Naming Consistency2/5

Tool names are inconsistent: some use 'ask_', 'polymarket_', 'pipeworx_', while others like 'electricity_price', 'installed_power', and 'remember' follow no coherent pattern. Conventions are mixed and unpredictable.

Tool Count2/5

With 35 tools, the server is over-scoped for an 'Energy Charts' purpose. Only 5-6 tools are directly energy-related; the rest are a miscellany of data services, prediction markets, and memory functions, which is excessive and unfocused.

Completeness2/5

The server lacks essential energy analysis tools like forecast, emission factors, or capacity utilization, yet includes many unrelated tools (e.g., betting, memory). This creates significant gaps for the stated domain.

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