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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide destructiveHint=true and idempotentHint=true. Description adds context about clearing sensitive data and the purpose of deletion, which goes 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, no fluff. First sentence states action, second provides usage guidance and sibling references. 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 tool with no output schema, the description is complete. It covers purpose, usage, and sibling relationships, complementing annotations.
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 a clear description and example for the 'key' parameter. Tool description does not add extra meaning beyond the schema, so baseline of 3 is appropriate.
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?
Description clearly states 'delete a previously stored memory by key', specifying verb and resource. It distinguishes from sibling tools 'remember' and 'recall' by explicitly pairing them.
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?
Provides explicit use cases: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' Lacks explicit when-not-to-use but gives sufficient context.
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 legislation-specific tools are distinct, but the server bundles dozens of unrelated Pipeworx/prediction-market/AI-visibility tools, making the set's purpose unclear. Several near-identical pairs exist: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ai_visibility_check overlaps heavily with scan_competitor_ai_presence. An agent would struggle to know which tool is the right entry point.
Naming is mixed: snake_case dominates, but camelCase appears in ask_pipeworx, ask_pipeworx_grounded, generate_llms_txt, and pipeworx_feedback. There is also inconsistency in verb style — get_/search_/list_ coexist with bare verbs like remember, recall, forget, and subscribe. The legislation tools themselves follow a clean get_legislation* pattern, but the wider set does not.
35 tools is too many for a server named 'Legislation Uk' where only 4 tools actually relate to UK legislation. The remaining 31 tools appear to belong to a broader data/prediction-market platform, which suggests severe scope creep or a mislabeled assembly. This bloats the surface area and makes the server harder for an agent to navigate.
For the stated UK-legislation purpose, the core read-and-search workflow is covered: search_legislation, get_legislation, get_legislation_section, and get_legislation_text together support discovery, metadata, targeted section lookup, and full-text retrieval with version selection. Obvious gaps remain, such as full-text content search and amendment/change history, but agents can complete the primary task of finding and reading legislation. The unrelated tools neither help nor complete this domain.