signals_buy_sell
Trading bots buy this endpoint to receive a multi-factor buy/sell signal with RSI, Z-score, trend, and confidence.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Netzhandwerker EU Power Dispatch API
TDQS
B3/5.0
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 behavioral disclosure. It mentions the output components (RSI, Z-score, trend, confidence) but does not describe whether the operation is read-only, how the signal is computed, what data source it uses, or any limitations. This is insufficient 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise and front-loaded with the key purpose. It could be slightly more polished ('buy' is a colloquial usage), but every word serves a purpose and it is not verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 clearly convey what the response looks like. It lists the signal components but does not specify their types, ranges, or example values. The tool is simple (0 params) but the output is multi-factor, so more detail would improve completeness.
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 input schema has zero parameters, so the baseline is 4. The description does not attempt to explain parameters because there are none. It does not mislead or create confusion about inputs.
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 states that the tool provides a multi-factor buy/sell signal with RSI, Z-score, trend, and confidence. This clearly identifies the tool's purpose and output, though it does not explicitly differentiate from sibling tools. The phrasing 'Trading bots buy this endpoint' is a bit awkward but does not obscure the meaning.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. It implies the tool is for trading bots ('Trading bots buy this endpoint'), but does not mention any exclusions, prerequisites, or comparison to sibling tools. The intended usage context is vague.
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.