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 indicate destructiveHint=true and idempotentHint=true, so the description's 'delete' aligns but adds little new behavioral context. The mention of clearing sensitive data is useful but not a behavioral trait.
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 action, and no extraneous wording. Every sentence serves a purpose.
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?
The description covers when to use but does not explain return values or error conditions (e.g., missing key). Given no output schema, more detail on invocation results would improve completeness.
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% with clear description of the 'key' parameter. The tool description does not add additional meaning beyond what the schema already provides.
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 the resource 'previously stored memory by key'. It also distinguishes from sibling tools 'remember' and 'recall', making the purpose unambiguous.
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 clear use cases: 'when context is stale, the task is done, or you want to clear sensitive data'. It mentions pairing with siblings but does not explicitly state when not to use or exclude 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.
Multiple tools appear to do the same thing: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all provide natural-language data lookup, with ask_pipeworx_beta explicitly identical to ask_pipeworx. The six Polymarket tools also have heavily overlapping boundaries, and the Census-specific tools overlap with each other and with ask_pipeworx's routing to Census data.
All names are lowercase snake_case, which is consistent, but the pattern is a mix: some are verb_noun (discover_tools, generate_llms_txt, list_subscriptions) while many are noun_verb or bare noun phrases (entity_profile, pipeworx_feedback, polymarket_edges, census_acs). Suffixes like _beta, _grounded, and the scattered noun-first names prevent a uniform convention.
36 tools is well into the too-heavy range for a coherent server. Even if the domain truly is broad data research and prediction markets, many of these tools are near-duplicates or serve the same purpose with minor variations, so the count feels inflated rather than scoped.
The broad data-research domain is well covered: single lookups (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), multi-source research (deep_research, entitity_profile, compare_entities, recent_changes), plus memory and subscription utilities. Minor gaps exist (e.g., no general data update/delete, but data is read-only; no direct Census variable explorer beyond census_available_datasets), but no critical dead ends appear.