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Glama

Octopus Energy

by io.github.pipeworx-io

Product Details

product_details
Read-onlyIdempotent

Full detail for one product code, including its tariffs broken out by GSP region "_A".."_P" and payment method (e.g. direct_debit_monthly). Each tariff entry has a "code" (e.g. "E-1R-AGILE-24-10-01-C"), standing charges and unit rates (inc/exc VAT). Use the tariff "code" with tariff_unit_rates. Tariff sets returned: single_register_electricity_tariffs, dual_register_electricity_tariffs, four_rate_ev_electricity_tariffs, single_register_gas_tariffs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
productCodeYesProduct code from list_products, e.g. "AGILE-24-10-01".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "productCode": "AGILE-24-10-01"
+  }
+]
  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 this as read-only, idempotent, non-destructive. The description adds that it returns tariffs by region/payment method, standing charges, unit rates, and lists tariff set categories. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose. No redundant information. Could be slightly tighter but efficient overall.

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

Completeness4/5

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

Without an output schema, the description comprehensively explains return values: tariff breakdown by region and payment method, including codes, standing charges, unit rates, and tariff set categories. It also advises on using the tariff code further.

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 a clear description of the only parameter (productCode from list_products). The description does not add extra semantic meaning beyond providing an example in the schema examples.

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 retrieves 'full detail for one product code' including tariffs by region and payment method. It distinguishes from sibling tools like list_products (which lists product codes) and tariff_unit_rates (which uses a tariff code for rates), providing specific context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when a specific product code is known and comprehensive tariff info is needed. It tells how to further use the tariff code with tariff_unit_rates. It does not explicitly list when not to use, but sibling differentiation is clear enough.

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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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes due to detailed descriptions and specific domains. However, there is potential for confusion between `ask_pipeworx` and `ask_pipeworx_grounded` (both route to data), and among the four polymarket tools, which could cause misselection if not read carefully.

Naming Consistency2/5

Naming is inconsistent: while all use snake_case, they mix verbs (`ask_`, `list_`, `bet_`, `scan_`), noun phrases (`entity_profile`, `product_details`, `recent_alerts`), and imperative verbs (`forget`, `remember`, `recall`). No single pattern is followed throughout, making it harder to predict tool names.

Tool Count3/5

With 31 tools, the server covers multiple domains (energy, general data, betting, memory, subscriptions) which feels heavy for a single server named 'Octopus Energy'. While each tool has a role, the scope is overly broad, and many tools are tangential to energy, suggesting the number could be reduced or better scoped.

Completeness3/5

For the energy domain, tools are present but only cover listing products and tariffs, missing account management or switching. For the broader domains (data, prediction markets), coverage is decent but lacks some expected features like browsing all available data sources or user profile management. The set is not fully comprehensive for any single purpose.

Resources