actual_loadMeasured electricity consumption per hour for a bidding zone (MW).
| Name | Required | Description | Default |
|---|---|---|---|
| area | Yes | Bidding-zone EIC code | |
| period_end | Yes | ||
| period_start | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Query parameters sent to ENTSO-E API | |
| time_series | Yes | Array of time series data | |
| time_series_count | Yes | Number of time series returned |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, indicating a safe, read-only tool. The description adds that the data is 'measured' consumption per hour, reinforcing the non-mutating nature. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence of 11 words conveys the tool's essence with no redundant information. Every word serves the purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the availability of an output schema and clean annotations, the description is nearly complete. It could mention that the data is historical or real-time, but the core information is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only 33% of parameters have schema descriptions (area). The description does not clarify the format for period_start and period_end, which are dates/times (examples show YYYYMMDDHHmm). It mentions 'per hour' but does not explain expected string formats or behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it provides measured electricity consumption per hour for a bidding zone in MW. This distinguishes it from sibling tools like actual_generation_per_type (generation) and cross_border_flow (flows).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or when-not-to-use guidance. While the purpose is clear, there is no mention of alternatives (e.g., use actual_generation_per_type for generation data). Usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The ENTSO-E energy tools are clearly distinct, but the Pipeworx half contains overlapping query modes: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx/ask_pipeworx_grounded/deep_research/validate_claim all route natural-language questions to the same underlying catalog. The Polymarket tools also blur edge detection, arbitrage, fill-risk, and persistence tracking, so an agent can easily select the wrong one.
The five ENTSO-E tools use a clean snake_case noun pattern, but the rest mix brand verbs (ask_pipeworx, bet_research), bare memory verbs (remember, recall, forget), and polymorphic prefixes (polymarket_*), with inconsistent suffixes like beta, grounded, and kalshi_spread. There is no server-wide predictable naming convention.
36 tools is too many for the apparent scope, and the server name promises ENTSO-E while only 5 of 36 tools serve that domain. Even viewed as a general data utility, the count is heavy and includes duplicate query modes, though individual clusters do have some purpose.
For an ENTSO-E server, the five energy tools cover the basics (generation, load, price, flow, capacity) but omit common datasets like generation forecasts, balancing/imbalance prices, and outages. The unrelated Pipeworx tools add broad research, memory, and subscription coverage, but the overall surface feels like a general-purpose assistant with an energy add-on rather than a complete energy domain.