grid_supply_pointMap a UK postcode to its GSP (Grid Supply Point) group "_A".."_P". The group_id is the region suffix used in tariff codes and in product_details regional breakdowns. Returns {count, results:[{group_id}]}.
| Name | Required | Description | Default |
|---|---|---|---|
| postcode | Yes | UK postcode, e.g. "SW1A 1AA" (spaces optional). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "postcode": "SW1A1AA"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, open-world, idempotent, and non-destructive. The description adds the output format ({count, results:[{group_id}]}) but does not disclose additional behaviors beyond what annotations imply.
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?
The description is two sentences, front-loads the primary action, and contains no redundant information. Every sentence adds value.
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 simple one-parameter input and complete annotations, the description adequately covers the purpose, output structure, and real-world significance. It lacks error handling or edge cases but is sufficient for a straightforward mapping tool.
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?
The input schema fully describes the postcode parameter (type, example, spaces optional). The description adds context about the output group_id range but does not enhance parameter understanding beyond the schema.
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 the tool maps a UK postcode to a GSP group, explaining the group_id suffix and its relevance to tariff codes and regional breakdowns. This is specific and distinguishes the tool's core function.
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 guidance is provided on when to use this tool versus alternatives like tariff_unit_rates or product_details. There is no mention of prerequisites or when not to use it.
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.
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 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.
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.
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.