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?
The annotations already indicate destructiveHint=true and idempotentHint=true, and the description's 'Delete' aligns with these. It adds some context about clearing sensitive data, but does not disclose additional behavioral traits (e.g., permanence, error handling) beyond what annotations provide. No contradiction with 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?
The description is three concise sentences with the action front-loaded. It provides purpose, usage, and tool relationships with zero filler. Every sentence earns its place.
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 one-parameter destructive tool with no output schema, the description covers purpose, usage, and relationship to sibling tools. It leverages annotations for idempotency and destructiveness, making it sufficient. It does not mention error cases, but those are less critical given the tool's simplicity and idempotentHint.
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 the 'key' parameter described as 'Memory key to delete.' The description's phrase 'by key' adds no new meaning beyond the schema. The example in the schema provides a concrete value, so the description does not compensate further.
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 action: 'Delete a previously stored memory by key.' This is a specific verb (delete) and resource (memory), and it distinguishes from sibling tools like 'remember' (store) and 'recall' (retrieve) by specifying deletion. The phrase 'previously stored' adds scope.
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 provides explicit use cases: 'when context is stale, the task is done, or you want to clear sensitive data.' It also mentions pairing with 'remember and recall,' implying the workflow. However, it does not explicitly state when not to use it or compare with alternatives, so it falls short of a 5.
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.
The three ask_pipeworx variants (stable, beta, grounded) plus deep_research and validate_claim create real selection ambiguity — an agent could easily pick the wrong one. Many other tools (entity_profile, bet_research, scan_dependency) are clearly distinct, but the overlapping meta-query tools muddy the boundary.
Naming is a mix of verb-initial (get_data, resolve_entity, generate_llms_txt, scan_dependency) and noun-initial (dataflow_structure, entity_profile, polymarket_edges, pipeworx_trending) conventions. The ask_pipeworx family and Polymarket cluster are internally consistent, but there is no single predictable pattern across the set.
34 tools is heavy, and the server named 'Statec Lu' (Luxembourg statistics) carries 30+ tools for prediction markets, npm dependencies, AI visibility, memory, and subscriptions. It reads as an everything-server rather than a focused statistics integration; most tools have nothing to do with STATEC.
Within the STATEC domain, list_dataflows → dataflow_structure → get_data is a complete browse-and-query workflow. The broader domains also have good coverage (memory save/recall/forget, subscription list/create/cancel, rich Polymarket research tools). Minor gaps like no data-format conversion or direct 'latest value' shortcut exist, but they are workable.