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Neso

by io.github.pipeworx-io
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Neso Search Datasets

neso_search_datasets
Read-onlyIdempotent

Search NESO (National Energy System Operator, ex National Grid ESO) open data for UK electricity grid datasets — GB power demand forecasts, wind generation forecasts, carbon intensity, balancing costs, historic demand, energy system data. Returns dataset slug/id, title, summary, and resource count. Example: neso_search_datasets({ query: "wind forecast" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax datasets to return, 1-50 (default 10).
queryNoKeyword(s), e.g. "demand forecast", "wind", "carbon intensity". Blank lists all datasets.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "wind forecast"
+  },
+  {
+    "limit": 5,
+    "query": "demand"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive behavior. The description adds value by detailing the return fields (slug/id, title, summary, resource count), which is not in annotations.

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 paragraph that front-loads the tool's action, provides examples, and avoids redundancy. Every sentence earns its place.

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 the tool's low complexity (2 optional params, no output schema, rich annotations), the description fully covers its purpose, usage, and return format, leaving no significant gaps.

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

Parameters3/5

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

Schema coverage is 100% with both parameters described. The description adds an example usage but does not significantly enhance parameter semantics beyond what the schema provides, so baseline score of 3 is appropriate.

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 clearly states the tool serves to search NESO open data for UK electricity grid datasets, listing specific data types and the returned fields. It differentiates from sibling NESO tools like neso_query_data or neso_demand_forecast by being the general search entry point.

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 when one needs to find datasets via keywords, and provides example queries, but it does not explicitly state when not to use it or compare to alternatives like neso_dataset_resources or neso_demand_forecast.

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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