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.
Input schema / examplesAdded value: +[
+ {
+ "key": "user_research_topic"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey destructiveHint=true and idempotentHint=true, so the description's job is lighter. It adds context about what is deleted (previously stored memory) and a typical use case (clearing sensitive data), but does not disclose other behavioral traits like irreversibility or effects on related data. This is adequate but not rich.
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?
Three sentences, each earning its place: purpose, usage scenarios, and sibling pairing. It is front-loaded with the main action, uses no filler, and remains compact while covering key guidance.
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 delete tool with strong annotations (destructive, idempotent) and 100% schema coverage, the description is nearly complete. It explains what, when, and related tools. It could mention that deletion is permanent or what happens if the key does not exist, but those gaps are minor given the simple scope.
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 covers the only parameter 'key' with 'Memory key to delete' at 100% coverage. The description merely says 'by key' without adding syntax, constraints, or examples beyond the schema. Baseline of 3 applies because the schema already documents the parameter sufficiently.
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 opens with a specific verb and resource: 'Delete a previously stored memory by key.' It clearly differentiates the tool from siblings like 'remember' and 'recall' by framing it as the deletion counterpart and explicitly pairing with 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?
The description provides explicit when-to-use guidance: 'Use when context is stale, the task is done, or you want to clear sensitive data.' It also mentions pairing with remember and recall, though it does not explicitly state when not to use or name alternatives in a contrastive way.
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.
Most tools have clearly distinct purposes (e.g., ask_pipeworx for general queries, ask_pipeworx_grounded for high-stakes verification, deep_research for multi-faceted research). However, some overlap exists among the ask_* variants and the prediction-market tools (bet_research vs. polymarket_edges vs. polymarket_arbitrage), which could cause misselection without careful reading of the detailed descriptions.
Tool names consistently use snake_case and mostly follow the verb_noun pattern (e.g., list_subscriptions, resolve_entity, validate_claim). Minor deviations like random_fact and today_fact (adjective_noun) and pipeworx_feedback (noun_noun) introduce slight inconsistency, but the overall pattern is predictable.
With 33 tools, the count exceeds the typical 3-15 range and even the 16-25 'heavy' threshold. While the server covers an unusually broad domain (data retrieval, prediction markets, memory, subscriptions, AI visibility), several tools could be consolidated (e.g., the six polymarket tools, trivial random_fact/today_fact). The scope partially justifies the count, but it feels over-provisioned.
The tool surface is remarkably comprehensive for a data platform: it covers querying, entity resolution, comparison, change feeds, memory persistence, subscription management, validation, and even meta-tool discovery. Minor gaps exist (e.g., no explicit update/delete for external data, but that is not the service's purpose). Overall, no obvious dead ends.