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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data and scenarios for deletion, aligning 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, front-loaded with purpose, followed by usage guidelines. Every word serves a purpose with no redundancy.
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?
Given low complexity (single parameter, no output schema, annotations present), the description sufficiently covers purpose, context, and usage. No further detail needed.
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 the parameter 'key' clearly described. The description does not add additional meaning beyond what the schema provides, so baseline score 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 resource 'memory by key', clearly stating the function. It also distinguishes from sibling tools by mentioning pairing with '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?
Explicitly states when to use: when context is stale, task is done, or to clear sensitive data. References alternatives by naming sibling tools, providing clear exclusions and guidance.
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-duplicates or heavily overlapping: ask_pipeworx_beta explicitly matches ask_pipeworx exactly, and ask_pipeworx_grounded, deep_research, and validate_claim all cover grounded-answer territory. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread, bet_research) also has fuzzy boundaries that would require careful reading to differentiate.
Naming mixes several conventions: verb_noun (query_dataset, resolve_entity, validate_claim), noun phrases (entity_profile, disaster_declarations, deep_research), and branded prefixes (pipeworx_trending, pipeworx_feedback, polymarket_edges, polymarket_arbitrage). Some tools use scan_, some ask_, some list_, with no single predictable pattern across the set.
At 34 tools, the set exceeds the 25+ 'too many' threshold and carries a lot of surface area. The server is named Openfema, yet only about three tools actually relate to FEMA data, making the count feel inflated relative to the stated name and purpose.
For its actual broad domain—a general structured-data research gateway—the surface is quite comprehensive: discovery, single lookups, grounded verification, deep multi-source research, entity resolution, comparisons, change feeds, memory, subscriptions, and prediction-market analysis are all covered. Minor gaps exist (e.g., no direct OpenFEMA dataset metadata beyond list_datasets, and some tools require accounts/paywalls), but agents can generally accomplish the intended workflows.