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 provide destructiveHint and idempotentHint. The description adds context about clearing sensitive data and that the tool is used for deletion, which aligns with annotations. No contradiction.
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 sentences, no wasted words. First sentence clearly states purpose, second provides usage context. Front-loaded 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 simple tool with one parameter and no output schema, the description fully covers purpose, usage, and related tools. No gaps.
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% and the parameter 'key' is described as 'Memory key to delete' in the schema. The description does not add extra semantics beyond what is already in 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 states 'Delete a previously stored memory by key' which clearly identifies the verb and resource. It also mentions pairing with 'remember' and 'recall', distinguishing it from siblings.
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?
Explicitly specifies when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. This helps the agent decide when to invoke this tool over 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 are near-indistinguishable by role: ask_pipeworx and ask_pipeworx_beta are documented as currently identical, and ask_pipeworx, ask_pipeworx_grounded, and deep_research overlap as lookup/research entry points. The six-tool Polymarket cluster and the suggest_questions/discover_tools pair add further boundary confusion despite very long descriptions.
Snake_case is used consistently, and clusters like ask_pipeworx_* and polymarket_* have internal consistency. However, the global convention is mixed: verb_object names (get_cell, resolve_entity, unsubscribe) sit beside noun phrases (entity_profile, recent_changes, cells_in_area) and product-prefixed nouns (pipeworx_feedback, polymarket_edges), so tool names are not predictable from function.
33 tools is too many for a server named Opencellid, especially since only get_cell and cells_in_area actually belong to the cell-tower domain. Even as a broader Pipeworx/data bundle, the set is heavy and includes unrelated one-offs like generate_llms_txt and scan_dependency.
For an OpenCellID server, the surface is just two lookups, missing coverage stats, operator-based search, and other natural cell-tower operations. If judged instead as a Pipeworx data-research suite, coverage is broad, but the lack of a coherent stated domain makes obvious gaps and dead ends harder to identify.