Store a memory for an AI agent. Persists across sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| ttl | No | Optional TTL in seconds | |
| content | Yes | Memory content to store | |
| metadata | No | Optional metadata key-value pairs | |
| namespace | Yes | Namespace to group memories (e.g. agent name) |
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?
With no annotations, the description must disclose behavioral traits. It only states that the memory 'persists across sessions,' but does not mention whether it overwrites existing memories, how it interacts with namespaces, or what the return value is. This is insufficient for a mutation tool.
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 is extremely concise — two short sentences — and front-loads the primary action. Every word adds value, with no filler or 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?
The tool has four parameters including nested objects and no output schema. The description is minimal and fails to explain what happens after storing, return values, or any side effects. For a mutation tool with zero annotations, this is incomplete.
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 description coverage is 100%, so the schema already documents all four parameters. The description adds no extra meaning beyond what the schema provides, thus the baseline 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 clearly states the operation: 'Store a memory for an AI agent' with a specific verb and resource. It also adds durability context ('Persists across sessions') and indirectly distinguishes from sibling tools like delete_memory, get_memory, and search_memories.
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?
No guidance is provided on when to use this tool versus alternatives. It does not mention that this is for creating new memories, nor any exclusions or prerequisites. The sibling tools exist but are never referenced.
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.
Each tool has a clearly distinct purpose: store, retrieve by ID, search, delete by ID, and clear a namespace. No two tools overlap in functionality or target different granularities.
All tool names follow a consistent verb_noun pattern with clear verbs (store, get, search, delete, clear) and nouns (memory, memories, namespace). The slight pluralization in search_memories is natural and does not disrupt consistency.
Five tools is a well-scoped set for an agent memory system, covering the essential operations without unnecessary bloat or redundancy.
The surface covers create (store), read (get, search), and delete (delete, clear) for memories. A direct update operation is missing, but users can work around it by deleting and re-storing, so the gap is minor.