system_demandGreat Britain half-hourly electricity system demand outturn in MW (Elexon BMRS ITSDO dataset) over a required ISO-datetime window; returns one row per settlement period with settlementDate, settlementPeriod, and demand fields.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | End, ISO datetime, e.g. "2024-06-01T01:00Z". | |
| from | Yes | Start, ISO datetime, e.g. "2024-06-01T00:00Z". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "from": "2024-06-01T00:00Z",
+ "to": "2024-06-01T01:00Z"
+ },
+ {
+ "from": "2024-06-15T06:00Z",
+ "to": "2024-06-15T12:00Z"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is clear. The description adds the return structure (one row per settlement period) but does not disclose rate limits or authentication needs.
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?
A single sentence with all essential information, front-loaded with the core purpose. No wasted words.
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?
For a simple data retrieval tool with two parameters and no output schema, the description adequately explains the return format. Missing details like pagination or limits are minor given the tool's simplicity.
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?
Schema coverage is 100% with clear descriptions for both parameters (from and to). The description adds no new semantic information beyond confirming the window requirement, so baseline 3 is appropriate.
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?
The description clearly states that the tool returns Great Britain half-hourly electricity system demand data over a specified datetime window, with specific fields. This verb+resource combination distinguishes it from siblings like generation_by_fuel.
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?
The description specifies the required datetime window, making it clear when to use the tool (for demand data in a time range). However, it does not provide exclusions or alternative tools for other contexts.
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.
Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx_grounded/deep_research heavily overlap as high-level routing entry points, and the five Polymarket tools (edges, arbitrage, edge_tracker, fill_risk, bet_research) cover closely related concerns. The descriptions are detailed, but an agent can easily select the wrong entry point.
All names are snake_case and readable, but conventions are mixed: verb-first names (query_dataset, validate_claim, discover_tools) coexist with noun-phrase names (system_demand, entity_profile, recent_changes), and prefix families are applied inconsistently (elexon_* and polymarket_* exist, but bet_research, generation_by_fuel, and system_demand have no prefix). The pattern is understandable but not predictable enough to be considered consistent.
36 tools is well above the 25-tool threshold for a heavy surface, and many tools are orthogonal to the nominal Elexon scope: memory (remember/recall/forget), subscriptions, npm dependency scanning, and llms.txt generation. The count forces significant discovery overhead and makes the set feel bloated rather than well-scoped.
The Elexon core is solid: elexon_list_datasets plus query_dataset covers all 84 BMRS datasets, with direct shortcuts for system prices, generation by fuel, and system demand. The broader Pipeworx side also covers research, entity resolution, prediction-market analysis, memory, and subscriptions without obvious dead ends, though a few minor gaps exist such as limited non-npm dependency scanning and no direct Elexon-specific tools for every dataset family.