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 this. It adds usage context but does not disclose behaviors like what happens when the key doesn't exist or whether deletion persists across sessions. This is adequate given annotation coverage, but not rich.
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?
Three short sentences: first states the core action, second provides usage scenarios, third references complementary tools. No redundant wording or unnecessary detail.
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 tool is very simple: one parameter, no output schema, and annotations already cover safety. The description covers purpose, usage, and tool relationships. It does not mention error conditions or return values, but for a destructive action with idempotentHint, this is a minor gap in an otherwise complete description.
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 schema fully covers the single parameter 'key' with description 'Memory key to delete' (100% coverage). The description repeats 'by key' without adding new constraints or syntax details, so a baseline of 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 uses the specific verb 'Delete' and identifies the resource as 'a previously stored memory' with the mechanism 'by key'. It clearly distinguishes this from siblings like remember and recall by focusing on removal.
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, the task is done, or you want to clear sensitive data') and also names related tools ('Pair with remember and recall'), providing clear context for when to select this tool 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 and ask_pipeworx_beta are explicitly identical, discover_tools/suggest_questions/pipeworx_trending all serve discovery, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) find opportunities. Descriptions help but an agent could easily pick the wrong data-query tool.
Tool names follow a consistent snake_case pattern, mostly verb_noun (ask_pipeworx, resolve_entity, validate_claim) and prefix groups (fintech_*, polymarket_*). Minor deviations like entity_profile or recent_changes are noun phrases but still readable and predictable.
34 tools is excessive for a coherent set; the server appears to bundle a general-purpose data research platform, prediction-market analysis, memory, and subscriptions under one 'Fintech Intel' name. Many meta-tools could be split into separate servers, and the count burdens an agent with unnecessary choices.
The tool surface covers the fintech/data-intel domain thoroughly: SEC filings, FDIC, FDA, economic data, real estate, prediction markets, monitoring subscriptions, and memory. Gaps are minor—e.g., no direct tool for historical stock charts, but the router (ask_pipeworx) handles such queries.