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 provide destructiveHint=true and idempotentHint=true. The description adds context about use cases but does not elaborate on behavior beyond deletion (e.g., error handling, irreversibility).
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 sentences, front-loaded with the core action, and contains no unnecessary words. Every sentence adds value.
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 tool with one required parameter and no output schema, the description explains what it does and when to use it. It is mostly complete, though it could mention idempotency or error behavior more explicitly.
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 description 'Memory key to delete'. The tool description does not add additional meaning beyond what the schema already provides, so baseline 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 clearly states the action (Delete) and resource (previously stored memory by key). It is specific and distinct from siblings like remember and recall, which are mentioned for pairing.
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 explicitly states when to use the tool: when context is stale, task is done, or to clear sensitive data. It mentions pairing with remember and recall, but does not explicitly state when not to use it.
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.
Several tools occupy adjacent territory: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions with only subtle differences in grounding and breadth, and discover_tools overlaps with suggest_questions. The prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, bet_research) each have distinct mechanics but a less careful agent could easily misfire between them.
Most tools follow a verb_noun pattern (ask_pipeworx, compare_entities, generate_llms_txt, list_subscriptions), but there are notable exceptions: performance_review_generate reverses the order, entity_profile is noun_noun, and recall/remember/forget are bare verbs. The pipeworx_ and polymarket_ prefixes help group families, but the mixed styles prevent a higher score.
At 32 tools this is a heavy surface for one MCP server, exceeding the 25+ threshold where agents struggle to discover and choose among options. The breadth is understandable given the many subdomains (data lookups, prediction markets, memory, subscriptions, feedback), but the count still feels like tool sprawl rather than a tightly scoped set.
As a data/research/prediction-market platform the surface is remarkably complete: query, grounded verification, deep research, entity resolution, comparisons, claim validation, visibility audits, subscriptions, memory, and feedback loops are all covered. Minor gaps exist—there is no direct tool for bulk exports or for editing subscriptions, and some meta-tools duplicate discovery—but agents can accomplish the core workflows without dead ends.