Persistent semantic memory storage, associative recall, and recent memory index by namespace.
Check health status and connectivity of the persistent memory backend service.
Returns: Dict with health status ('healthy' or error details).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that the tool returns a dict with health status or error details, which is useful but does not go deeper into connectivity nuances or failure modes. No contradiction with 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?
The description is extremely concise: one sentence for purpose, one for return value. It is front-loaded and contains no filler or repetition.
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 no-parameter health check tool with rich annotations and an output schema, the description covers the essential purpose and return shape. An agent can select and invoke it correctly without additional context.
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 tool has zero parameters, so the description is not required to explain parameter behavior. Baseline 4 applies since there is nothing missing in parameter documentation.
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 checks health status and connectivity of the persistent memory backend service. This distinguishes it from sibling tools like recall or remember, which operate on memory content rather than service health.
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 phrase 'health status and connectivity' makes the intended use obvious: diagnose whether the backend is reachable and operational. It does not explicitly list exclusions or alternatives, but the context is clear given the sibling tools are all memory operations rather than health checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search persistent memory by semantic meaning to retrieve past facts, context, or preferences.
Use when answering queries that benefit from prior user context, past decisions, or stored preferences. Performs vector search (semantic similarity) rather than simple keyword match.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of memory items to return (default: 5, ceiling: 50). | |
| query | Yes | Search query describing the fact, context, or topic to find by semantic similarity. | |
| namespace | Yes | Storage namespace to search (e.g., 'user_123_pref', 'agent_project_alpha'). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds the key behavioral trait of performing vector-based semantic search rather than keyword match, which is not captured in annotations. This is valuable context but not exhaustive; yet with annotations covering other aspects, a high score is warranted.
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 efficient: two sentences that state purpose, usage, and the semantic search mechanism. No wasted words, and essential information is front-loaded. Exemplary conciseness.
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?
With an output schema present, return values need not be explained. The description covers purpose, usage, and the core behavioral distinction. Combined with annotations (read-only, idempotent) and full parameter docs, the tool is adequately specified. Slight lack of mention of the 'namespace' parameter's role in isolation is minor.
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%, so each parameter is already described. The description adds the semantic similarity emphasis for 'query', which slightly reinforces its purpose but does not introduce new meaning. Baseline of 3 is appropriate given high schema coverage.
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 searches persistent memory by semantic meaning to retrieve past facts, context, or preferences. It explicitly contrasts with simple keyword match, which helps distinguish it from other retrieval approaches. Even without referencing siblings, the purpose is unambiguous.
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?
It provides clear guidance on when to use the tool: when queries benefit from prior user context, past decisions, or stored preferences. It does not explicitly name alternatives like 'recent_memories' or 'remember', but the semantic distinction is implied. A brief mention of when not to use it would have earned a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Retrieve the most recently recorded memories for a namespace to re-orient at session startup.
Use at the beginning of a conversation or task to discover recent context, active objectives, or latest user facts recorded in this namespace.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of recent memories to return (default: 5, ceiling: 50). | |
| namespace | Yes | Storage namespace to inspect (e.g., 'user_123_pref', 'agent_project_alpha'). |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds the ordering notion of 'most recently recorded' and the session-startup context, but doesn't disclose behavior such as pagination, chronological order guarantees, or namespace absence handling. With rich annotations, this is adequate but not exceptional.
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 brief sentences with the core purpose front-loaded in the first sentence and usage context in the second. No filler or redundant schema repetition.
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-only tool with 2 documented params and an output schema, the description covers what the tool does, when to use it, and what kind of content to expect. It stops short of explicitly routing to recall/remember for non-recent or specific lookups, leaving a small completeness gap.
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%; the schema already documents namespace examples and limit default/ceiling. The description only restates 'for a namespace' and adds no new parameter semantics beyond the structured data.
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?
States a specific verb ('Retrieve'), a specific resource ('most recently recorded memories'), and a namespace scope, and pairs it with a clear purpose ('re-orient at session startup'). This differentiates it from sibling tools like recall (targeted lookup) and remember (write) even without naming them.
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 says when to use: 'at the beginning of a conversation or task' and what it surfaces ('recent context, active objectives, or latest user facts'). It lacks when-not-to-use or named alternatives, so not a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Store a durable fact, preference, or decision into persistent memory across sessions.
Use when you discover an important user preference, key background detail, architecture choice, or durable fact that must be recalled in future sessions. Do NOT use for storing entire conversation logs or transient temporary state.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Durable fact, preference, decision, or memory to store. Store key facts for future sessions; do NOT store raw full chat transcripts. | |
| metadata | No | Optional structured key-value metadata to attach to this memory (e.g., {'category': 'user_preference', 'source': 'onboarding'}). | |
| namespace | Yes | Isolated storage namespace for the agent or user (e.g., 'user_123_pref', 'agent_project_alpha'). Must be 1-64 alphanumeric characters, hyphens, underscores, or dots. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=false, idempotentHint=false, and destructiveHint=false. The description adds useful behavioral context beyond those: this persists across sessions and is meant for durable facts rather than ephemeral or transcript-style data. It does not detail duplicate-handling or overwrite behavior, but the idempotency annotation partially covers that.
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?
Three sentences, each earning its place: the core behavior, the when-to-use guidance, and the explicit anti-pattern. The most important information is front-loaded in the first sentence.
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 3-parameter write tool with a rich schema and an output schema present, the description provides complete selection and usage context. Nothing essential is missing for an agent to decide whether and how to invoke it.
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%, and each parameter (text, metadata, namespace) has a detailed description including namespace format constraints and metadata examples. The description itself adds no parameter-level semantics, so the baseline 3 is appropriate: the schema carries the burden, and the description does not need to compensate.
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 opens with a specific verb and resource: 'Store a durable fact, preference, or decision into persistent memory across sessions.' It clearly differentiates from sibling read tools like recall and recent_memories by framing this as the write operation for durable knowledge.
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?
It gives explicit positive guidance ('Use when you discover an important user preference, key background detail, architecture choice, or durable fact') and explicit negative guidance ('Do NOT use for storing entire conversation logs or transient temporary state'). This strongly steers an agent toward correct selection even without naming sibling alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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Each tool has a clearly distinct purpose: remember stores, recall searches semantically, recent_memories lists by recency, and check_memory_service monitors health. There is no meaningful overlap between the retrieval tools because one is query-driven and the other is time-driven.
Naming is inconsistent: remember and recall are bare verbs, recent_memories is a noun phrase, and check_memory_service uses a verb_noun pattern. A more consistent set would use e.g. remember, recall, list_recent_memories, check_service_health.
Four tools is well-scoped for a persistent memory service. Each tool covers a distinct, necessary operation without redundancy or bloat.
The core create/read and health-check operations are present, but there is no update or delete tool for managing stored memories. This creates a notable lifecycle gap, especially for correcting or removing outdated facts.
