rememberRead a single Lady Whiskerdown memory by id (as returned by recall). No graph or entity enrichment: the receiver endpoint returns the memory itself, scoped to this key.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory id (number or m#N as shown by recall) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / id / descriptionPrevious value: -"Memory id"New value: +"Memory id (number or m#N as shown by recall)"Input schema / properties / id / typePrevious value: -"number"New value: +[
+ "number",
+ "string"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no readOnlyHint or destructiveHint, so the description carries the full burden. It clarifies the read nature and scoping, but lacks details on authentication, error behavior, or idempotency.
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?
A single, front-loaded sentence with no fluff. Every part serves a purpose: verb, resource, scope, and key differentiation.
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 read tool with one parameter and no output schema, the description covers purpose, ID format, and what the tool does not do (enrichment). It is missing error handling or output format details but is largely complete.
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 schema already describes the 'id' parameter as 'Memory id (number or m#N as shown by recall)'. The description adds only a reference to recall, not new meaning.
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 it reads a single memory by ID, specifying it returns the memory itself without graph/enrichment. It explicitly distinguishes from sibling 'recall' by noting IDs come from 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?
The description tells when to use (to retrieve a specific memory by ID from recall) and what not to expect (no graph/enrichment). It does not explicitly name alternatives beyond recall, nor state when not to use.
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.