forgetDelete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide destructiveHint=true. Description adds context about clearing sensitive data, but no contradiction. Adds value beyond annotations.
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?
Two sentences, concise, front-loaded with purpose. No unnecessary 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?
Simple tool with one param and clear annotations; description is fully adequate for agent to invoke correctly.
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 covers 100% of parameters with description for 'key'. Description does not add further meaning beyond the schema; baseline 3.
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?
Clearly states the action (delete) and resource (stored memory by key). Differentiates from siblings by mentioning 'pair with remember and recall'.
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?
Explicitly states when to use: context stale, task done, or clear sensitive data. Also suggests pairing with siblings.
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.