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 declare destructiveHint=true and idempotentHint=true, so the description doesn't need to restate that. It adds marginal context ('clear sensitive data') but doesn't disclose behavior beyond annotations, such as what happens if the key doesn't exist.
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, first states the core action, second provides usage context. No filler or redundancy; every word 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 1-parameter, no-output-schema tool with annotations covering destructive and idempotent behavior, the description provides purpose, timing, and relationships to siblings. It omits only minor details like return value or missing-key behavior, which are not critical given the simplicity.
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 description coverage is 100% — the only parameter 'key' is already fully described as 'Memory key to delete'. The description adds no further meaning beyond the schema.
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 a specific verb ('Delete') and resource ('previously stored memory') along with the mechanism ('by key'), which clearly distinguishes it from sibling tools like remember and recall. It also names the action context (clearing sensitive data).
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 ('context is stale, the task is done, or you want to clear sensitive data') and even directs pairing with remember and recall, giving clear context versus alternatives.
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 have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical in routing, differing only in response mode; bet_research and polymarket_edges both surface betting opportunities. Even with detailed descriptions, an agent could easily select the wrong one for a given task.
Tool names are all snake_case, but the pattern is inconsistent: some are verb_noun (get_fact, list_breeds, validate_claim), some are noun/adjective compounds (entity_profile, deep_research, bet_research), and several use a pipeworx_ prefix. There is no consistent verb style or object-first convention.
34 tools is over the 25-tool threshold for a server whose name suggests a narrow cat-facts focus. Only 3 tools relate to cat facts; the rest form a sprawling data platform, creating a severe scope mismatch that makes the count feel excessive and unfocused.
For the cat-facts domain, the set covers the essentials: single fact, multiple facts, and breed listing. However, the overall tool surface is a mix of unrelated capabilities (data lookups, memory, subscriptions, prediction markets) that don't form a coherent domain, leaving the cat-facts portion sparse and the broader set without clear lifecycle coverage.