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 those. It adds context about the impact ('clear sensitive data') and the nature of the data ('previously stored memory'), which helps the agent assess consequences beyond the raw hint flags. This is a useful addition without contradicting annotations.
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: the first states the action, the second lists use cases and mentions complementary tools. No filler, front-loaded with the core verb, and every word earns its place.
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 delete operation with one parameter and no output schema, the description covers the purpose, usage triggers, and relationship to sibling tools. Annotations cover safety traits. Nothing essential is missing for an agent to select and invoke this tool correctly.
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 covers the single parameter 'key' with a clear description 'Memory key to delete' (100% coverage). The description's phrase 'by key' simply echoes the schema, adding no new semantic detail. Baseline 3 is appropriate when the schema already documents the parameter fully.
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 clearly states the action ('Delete a previously stored memory by key') with a specific verb and resource. It distinguishes from sibling tools by explicitly mentioning 'previously stored memory' and pairing with remember/recall, which are clearly complementary memory operations.
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 when-to-use guidance: 'Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier.' It also references sibling tools ('Pair with remember and recall'), giving context on how it fits into the workflow. This goes beyond vague hints to actionable conditions.
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 genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are described as currently identical, ask_pipeworx_grounded shares the same router, and the polymarket_edges/arbitrage/fill_risk/bet_research group overlaps heavily in purpose. The remaining clusters (dog data, memory, subscriptions) are mostly distinct, so the confusion is concentrated in a few spots but severe there.
Nearly all names are lower_snake_case and readable, but the conventions are mixed: get_/list_/ask_/scan_ verb-noun names sit alongside bare verbs (remember, forget, recall), noun-phrase names (entity_profile, bet_research, pipeworx_trending), and a versioned suffix (ask_pipeworx_beta). No single predictable pattern covers the whole set.
At 35 tools, the server is well past the 25+ threshold for feeling bloated, and the count is dominated by unrelated Pipeworx, prediction-market, and AI-visibility tools rather than the dog-data domain implied by 'dogsapi'. Only four tools actually serve the dog API, making the surface both oversized and misaligned with the server name.
The broad data-access side is thorough, covering discovery, universal routing, grounded answers, deep research, entity profiles, comparisons, claim validation, memory, and subscriptions. However, the nominal dog domain is thin (list/get/groups/facts with no filtering or additional operations), subscriptions have no update path, and some one-off tools like generate_llms_txt and scan_dependency exist without any surrounding lifecycle.