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, and the description confirms destructive action (delete). While it adds the 'by key' specificity, it does not disclose additional behavioral details like irreversibility or side effects.
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?
Extremely concise: one sentence for purpose, one for usage, one for pairing. Front-loaded with no wasted 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?
Given the simple one-parameter tool with no output schema, the description covers purpose, usage, and pairing adequately. No gaps.
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%, and the description does not add meaning beyond the schema for the single parameter 'key'. Baseline score of 3 applies.
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 verb (Delete) and resource (memory by key), distinguishing it from sibling tools 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?
Provides explicit when-to-use scenarios (stale context, task done, clear sensitive data) and suggests pairing with remember and recall, offering strong guidance.
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.
Many tools are clearly distinct, but the ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded trio are near-identical routers (beta currently matches stable exactly), and several prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage) have overlapping use cases. Detailed descriptions mitigate but don't fully eliminate the risk of an agent picking the wrong member of a cluster.
Names are consistently snake_case and mostly follow a verb_noun pattern (ask_pipeworx, compare_entities, resolve_entity, subscribe). Minor deviations like bare nouns (datasets, metadata) and bare verbs (remember, recall, forget), plus the branded ask_pipeworx variants, keep it from a perfect pattern but the style remains predictable.
34 tools is well above the 25+ threshold and feels heavy even for a broad research platform; many are meta/utility tools (pipeworx_feedback, pipeworx_trending, suggest_questions, discover_tools) tangential to the core data-access purpose. The server name suggests a small Baton Rouge Open Data server, making the actual count a poor match for that name.
Within its actual scope, coverage is strong: universal query, grounded mode, deep research, entity/comparison profiles, entity resolution, claim validation, prediction-market edge/arbitrage/fill-risk, memory, and subscriptions all have lifecycle-appropriate tool sets. Obvious gaps are hard to find; the main issue is that the set is over-inclusive rather than incomplete.