Skip to main content
Glama

Netzhandwerker EU Power Dispatch API

flexibility_window

EV, heat-pump, and flexible-load agents buy this endpoint to identify the cheapest upcoming operating hours.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

B3.4/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions 'buy this endpoint' which could imply a purchase or cost, but does not elaborate on costs, side effects, authentication, rate limits, or response format. This is a significant gap for a tool with no annotation support.

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 a single, concise sentence that fronts the key information (who uses it and what it does). It contains no fluff or redundant content, making it clear and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description should explain return values. It states the endpoint identifies 'cheapest upcoming operating hours,' giving a basic sense of the output, but lacks details on time horizon, granularity, or any constraints. For a simple parameterless tool, this is adequate but not fully complete.

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?

The tool has zero parameters, and the schema is empty (coverage 100%). Per the rubric, the baseline for 0 parameters is 4. The description adds no parameter-specific information, but it is not needed since there are no inputs to clarify.

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 clearly states the tool's purpose: identify the cheapest upcoming operating hours, with specific target users (EV, heat-pump, and flexible-load agents). It uses a specific verb-resource combination, but does not explicitly distinguish from the sibling tool 'optimizer_cheapest_window', so it misses the top score.

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?

The description implies usage for certain agent types and a specific need (finding cheap hours), but provides no explicit guidance on when to prefer this over alternatives, nor does it mention any exclusions. It gives context but no direct comparison with sibling tools.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.4/5.0
Disambiguation2/5

Multiple tools overlap significantly: buy_dispatch_plan, flexibility_window, optimizer_cheapest_window, and energy_decision all help schedule or choose an energy window, while price_forecast, price_spot, and buy_market_brief provide pricing context. The paired GET-fallback tools (articles_id vs articles_id_post, demand_submit vs demand_submit_post, etc.) create further ambiguity.

Naming Consistency2/5

Naming is inconsistent: some tools use a verb prefix (buy_, predict_, subscribe_), others start with a noun (price_, grid_, carbon_), and some have non-verb suffixes (_post, _quick). Related tools vary in style, e.g., price_forecast vs predict_negative_price and demand_submit vs demand_submit_post.

Tool Count2/5

With 31 tools, the server feels heavy. While many are distinct paid endpoints, the high number—including near-duplicate variants—exceeds the 25-tool threshold for comfort and suggests an over-sized surface.

Completeness4/5

The energy domain is well covered: real-time and historical prices, forecasts, negative-price prediction, dispatch/flexibility optimization, CO2, renewables, load, subscriptions, and research. Minor gaps exist (e.g., historical CO2, user account handling), but core agent workflows are supported.

Resources