recallRecall this Lady Whiskerdown's knowledge by semantic search. Use before answering any user question about this Lady Whiskerdown, the customer/product, project facts, onboarding, architecture, policies, product decisions, or imported documents. Returns the most relevant memories you have access to, scoped to your knowledge area.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 10) | |
| query | Yes | What to search for, in natural language |
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 only provide a title, so the description carries the full burden of behavioral disclosure. The description notes that the tool performs a semantic search and returns memories scoped to the agent's knowledge area, implying a read-only operation. However, it does not explicitly state that it is non-destructive, does not describe any side effects, rate limits, or authorization requirements. The behavior is adequately described for a simple search, but lacks explicit transparency about safety and constraints.
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?
The description consists of three sentences: the first states the core action, the second provides usage guidance, and the third describes the return. It is front-loaded and efficient, with no extraneous information. It could be slightly more compact (e.g., merging the first two sentences), but overall it is well-structured and concise for a tool of this complexity.
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?
The tool has two parameters, no output schema, and no nested objects. The description covers the purpose, usage context, and return type (memories). However, it does not describe the output format or structure, nor does it mention error handling, pagination, or the exact scope of 'knowledge area'. Given the absence of an output schema, the description should provide more detail about what the agent can expect to receive. The description is adequate but incomplete for a standalone specification.
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 already has 100% description coverage: 'query' is described as 'What to search for, in natural language' and 'limit' as 'Max results (default 10)'. The description adds 'semantic search' and 'scoped to your knowledge area', which are general context, but does not provide additional meaning or constraints for the parameters beyond what the schema already states. Per the rubric, with high schema coverage, the baseline is 3, and the description does not significantly enhance parameter semantics.
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 tool's purpose: 'Recall this Lady Whiskerdown's knowledge by semantic search.' It specifies both the action (recall) and the resource (Lady Whiskerdown's knowledge). The mention of returning 'the most relevant memories you have access to, scoped to your knowledge area' further clarifies the output. The usage context ('Use before answering any user question about this Lady Whiskerdown...') and the sibling tools (e.g., 'remember' for storing, 'areas' for listing) distinguish this tool as a retrieval mechanism.
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 explicitly states when to use the tool: 'Use before answering any user question about this Lady Whiskerdown, the customer/product, project facts, onboarding, architecture, policies, product decisions, or imported documents.' This provides a clear set of scenarios. However, it does not mention when NOT to use it or suggest alternative tools (e.g., 'remember' for storing, 'focus' for setting context). The instructions are straightforward but lack exclusions, so a score of 4 is appropriate.
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.
Most tools have distinct purposes like listing areas, capturing drafts, committing memories, and searching. However, the 'note' and 'handoff' tools overlap significantly because 'note' can be used with kind=handoff for the same scenarios that 'handoff' is designed for, potentially confusing an agent on which to use.
Tool names are all single-word, lowercase, and descriptive. Most are verbs (capture, commit, focus, etc.), but 'areas' and 'presence' are nouns, introducing a minor inconsistency. Overall, the pattern is simple and readable.
With 10 tools covering listing, capturing, committing, searching, retrieving, and setting context, the count is well-scoped for a memory/knowledge management server. Each tool serves a clear purpose and fits within the domain.
The tool surface lacks update and delete operations for memories, which are essential for a complete lifecycle. There is no way to modify or remove committed memories, and no tool to list all memories (only semantic search). This creates significant gaps that will likely cause agent failures.