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 idempotentHint=true. The description adds context about clearing sensitive data, which reinforces the behavioral disclosure. No contradictions.
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 concise sentences that cover purpose, usage, and pairing. No fluff.
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 single-parameter delete tool without an output schema, the description is complete: it explains what the tool does, when to use it, and how it relates to siblings.
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 one parameter 'key' described as 'Memory key to delete'. The description does not add extra information about the key format or constraints, so baseline 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 the resource ('previously stored memory by key'). It distinguishes from siblings by contrasting with '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?
Explicit usage scenarios: 'when context is stale, the task is done, or you want to clear sensitive data'. It also suggests pairing with 'remember' and 'recall', providing clear guidance on when to use this tool vs. 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.
The toolset is mostly organized by clear subdomains, but there are multiple overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer research questions and the beta version is currently identical to the stable router. Detailed descriptions reduce confusion, but an agent could still reasonably pick the wrong one for a given task. The entity, memory, and subscription tools are more clearly separated.
Names are consistently lower_snake_case and readable, but the set mixes verb-led names (compare_entities, resolve_entity, validate_claim) with noun-led names (entity_profile, polymarket_edges, pipeworx_trending) and some odd pairings like ai_visibility_check vs scan_competitor_ai_presence. No chaotic camelCase or inconsistent separators, but the convention is not uniform enough for a strong score.
34 tools is past the 25+ threshold and the surface spans many unrelated domains: structured data lookup, prediction markets, AI visibility marketing, city open data, npm dependency checking, llms.txt generation, memory, and subscriptions. Each tool may be individually useful, but the collection feels like a platform dump rather than a tightly scoped server. A more focused server would split off prediction markets, AI visibility, and utility tools.
The main data-research workflow is well covered: discovery, routing, grounded answering, deep research, entity resolution, profiles, comparisons, recent changes, claim validation, and search-within-results are all present. Prediction-market analysis, memory, and subscription lifecycles also have no major dead ends. Minor gaps exist, such as no write/update path for open data and no subscription option for AI-visibility monitoring, but these are not central to the apparent core purpose.