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 indicate destructiveHint=true, and the description aligns by stating 'Delete.' It adds value by explaining the behavioral reason ('clear sensitive data'), which goes beyond the annotation.
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 with no wasted words. The description is front-loaded and efficiently conveys purpose, usage, and relationships.
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 tool with one parameter, full schema coverage, and annotations present, the description is complete. It covers purpose, usage, and parameter without needing an output schema.
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 covers 100% of the parameter 'key' with a description. The tool description does not add additional semantic detail beyond the schema, so baseline 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 explicitly states 'Delete a previously stored memory by key,' which is a clear verb+resource. It distinguishes itself from siblings 'remember' and 'recall' by specifying deletion.
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 explicit guidance on when to use: 'Use when context is stale, the task is done, or you want to clear sensitive data.' It also suggests pairing with siblings, offering clear context and 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 tool groups overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same catalog; entity_profile, recent_changes, and compare_entities cover overlapping company-data territory; ai_visibility_check and scan_competitor_ai_presence are near-duplicates. Agents would frequently need to read long descriptions to pick the right tool.
All tool names use snake_case and mostly follow verb_noun patterns (ask_pipeworx, compare_entities, resolve_entity, validate_claim). Minor inconsistencies exist: generic noun-only names like entity_profile, recent_changes, and bet_research, plus inconsistent prefixes (ask_, baltimore_, polymarket_, pipeworx_, scan_) that group by domain rather than action.
34 tools is on the heavy side for a data-access server, though the scope is broad. Several tools feel tangential to the core data mission (remember/recall/forget, generate_llms_txt, pipeworx_feedback, pipeworx_trending), and the ask_pipeworx family plus the six polymarket_* tools inflate the count with overlapping functionality.
The surface covers the domain well: discovery (discover_tools, suggest_questions, baltimore_layers), lookup (ask_pipeworx, baltimore_query/recent), grounded verification (ask_pipeworx_grounded, validate_claim), comparison (compare_entities), profiling (entity_profile), change tracking (recent_changes), and prediction-market analysis. Minor gaps include no direct web search and no Baltimore-specific export/bulk operations, but agents can work around these.