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?
Description mentions deletion and clearing data, consistent with annotations (destructiveHint=true). Adds context about sensitive data but doesn't elaborate on idempotency or side effects.
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 efficient sentences with zero wasted text; front-loaded with the core action.
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 one-parameter tool with annotations, the description covers purpose, usage, parameter, and context sufficiently.
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 a clear 'key' description. Description adds 'by key' but largely redundant with 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?
Clear verb ('Delete') and resource ('memory') with specific action ('by key'). Distinguishes from sibling tools like '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?
Specifies when to use (stale context, task done, clear sensitive data) and pairs with siblings, but does not explicitly state when not to use.
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 have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, with ask_pipeworx_grounded and deep_research routing through the same 5,756-tool catalog, and validate_claim falling into the same grounded pipeline. The five polymarket_* tools plus bet_research all target prediction-market opportunities with overlapping outputs (edge_pp vs gap_pp vs spread_pp), and compare_entities/entity_profile/recent_changes share the same SEC/XBRL/news fan-out. The verbose descriptions help, but the set itself would frequently misroute an agent.
All names are uniformly snake_case with no casing mixing, and the ask_pipeworx_*, polymarket_*, and pipeworx_* prefixes create recognizable families. However, the set mixes verb_noun names (validate_claim, list_subscriptions), bare verbs (query, recall, forget), and noun-phrase names (entity_profile, recent_alerts, datasets, metadata), so there is no single predictable pattern across the server.
34 tools is heavy, but the server's scope is genuinely enormous: it is a gateway to 5,756 tools across 1,504 sources, plus prediction-market analysis, subscriptions, and memory. The count is defensible for that scope, yet several tools (generate_llms_txt, scan_dependency, ai_visibility_check, the memory trio) are peripheral to the core data mission, giving the set a scattershot feel and preventing a well-scoped rating.
The core data-research workflow is thoroughly covered: casual lookup (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), deep research (deep_research), entity resolution and profiling (resolve_entity, entity_profile, compare_entities, recent_changes), and a six-tool prediction-market suite. Subscriptions and memory have full lifecycles, and Oakland data offers search, schema, and query. Minor gaps exist — no subscription update/pause, no raw dataset export, and no write path for Oakland data — but no advertised workflow hits a dead end.