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, readOnlyHint=false, and idempotentHint=true, covering the safety profile. The description adds minimal extra behavioral context beyond 'clear sensitive data' and doesn't disclose error handling or permanence details. With annotations, this is acceptable but not additive.
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 three short sentences with the action stated first. No filler; each sentence contributes purpose, usage timing, and complementary tools, making it tightly structured and efficient.
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 one-parameter destructive operation with a clear schema and annotations, the description covers what, when, and related tools. It lacks return-value or error details, but these are not critical for this simple tool, making it adequately complete.
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 fully documents the single 'key' parameter with 100% coverage, including an example. The description merely repeats 'by key' without adding new semantic details, so it doesn't add value 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 opens with 'Delete a previously stored memory by key', clearly stating the verb (delete), resource (memory), and key-based method. It distinguishes itself from siblings like remember and recall by explicitly describing the deletion operation.
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 gives explicit usage scenarios: 'context is stale, the task is done, or you want to clear sensitive data'. It also says 'Pair with remember and recall', providing direct alternatives and complements, making when-to-use guidance very clear.
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 tool clusters are near-duplicates or easy to confuse: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical (beta currently matches stable exactly), while polymarket_edges, polymarket_arbitrage, and bet_research all scan prediction-market opportunities with overlapping outputs. entity_profile/compare_entities/recent_changes and ai_visibility_check/scan_competitor_ai_presence add further redundancy. Despite detailed descriptions, the boundaries require careful reading, so an agent is likely to misselect.
All names are snake_case and readable, with useful prefixes like ask_, polymarket_, pipeworx_, and scan_. However, conventions are mixed: verb_noun (ask_pipeworx, list_subscriptions, resolve_entity) coexists with bare nouns (datasets, metadata, query) and noun-first compounds (entity_profile, bet_research, deep_research). There is no single predictable pattern.
34 tools is above the 25-tool threshold, and the set spans multiple unrelated domains such as data routing, prediction markets, memory, subscriptions, Virginia Open Data, AI visibility, and npm dependency checks. Several tools are effectively wrappers or near-overlaps that could be consolidated, e.g., ai_visibility_check vs scan_competitor_ai_presence and polymarket_edges vs polymarket_arbitrage. The surface feels bloated for a single server.
Within its main sub-domains the set is solid: memory has remember/recall/forget, subscriptions have subscribe/unsubscribe/list/recent_alerts, research has resolve_entity/compare_entities/entity_profile/recent_changes/validate_claim, and Polymarket has detection/arbitrage/fill-risk/edge-tracking. Minor gaps exist — no tool to fetch a raw pipeworx:// record, no write/update for Virginia Open Data, and no trade execution for prediction markets — but these do not create dead ends for a research-focused agent.