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 declare destructiveHint=true, idempotentHint=true, and readOnlyHint=false, which convey the safety profile. The description adds the rationale for deleting (sensitive data, stale context) but doesn't disclose additional behavioral details such as irreversibility or behavior on missing keys. This is adequate given annotation coverage.
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 concise sentences: the first states the core action, the second provides usage guidance and sibling relationships. No filler or repetition.
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 single-parameter mutation tool with rich annotations, the description is complete. It covers what it does, when to use it, and how it relates to sibling tools. No output schema is needed, so missing return details are not a concern.
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 is fully described (100% coverage) with the 'key' parameter clearly documented as 'Memory key to delete.' The description adds no extra parameter semantics beyond what the schema provides, so the 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?
The description uses the specific verb 'Delete' and identifies the resource as 'a previously stored memory by key,' clearly distinguishing it from sibling memory tools like remember (store) and recall (retrieve).
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?
It states explicit usage triggers: 'when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also mentions pairing with remember and recall, which provides context, though it doesn't explicitly state when not to use this tool versus an alternative.
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.
ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, creating a true duplicate. The six-tool Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) plus discover_tools vs suggest_questions give agents overlapping entry points that require deep reading to disambiguate.
Sub-families are internally consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the server mixes verb_noun, domain_noun, and bare-verb styles across tools. bet_research breaks the polymarket_ prefix pattern, and ai_visibility_check vs scan_competitor_ai_presence use different words for the same underlying concept.
33 tools is heavy and spans at least six unrelated domains: a data-gateway, prediction markets, key-value memory, subscription management, PRIDE proteomics, and standalone utilities (generate_llms_txt, scan_dependency). The scope is so broad that it feels like multiple servers merged into one, making the surface hard to navigate.
The dominant data-query domain is well covered: query, grounded query, deep research, profiles, comparison, change feeds, validation, entity resolution, and discovery are all present. Subscription and memory lifecycles are complete, and the prediction-market research surface is thorough; minor gaps exist only in peripheral areas like PRIDE project download/file details.