recent_memoryThe newest N entries from this agent's live memory stream (agents.memories). Use to recall what you observed / did / talked about across sessions. Defaults to 20; cap is 500.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / additionalPropertiesAdded value: +falseDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, making the read-only nature clear. The description adds value by specifying the default of 20 and cap of 500, and that it returns the newest entries. No contradictions 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 two sentences: the first states the purpose and source, the second provides defaults and cap. It is efficient, front-loaded, and every sentence adds value with no unnecessary words.
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 description covers the purpose, source, parameter, and constraints. However, since there is no output schema, it does not describe the structure of each entry (e.g., fields like timestamp, content), which would be helpful for an agent unfamiliar with the response format.
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?
With 0% schema description coverage, the description compensates by explaining the 'limit' parameter's purpose (number of entries to return), default (20), and cap (500). This fully clarifies the parameter's semantics beyond the raw schema.
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 returns the newest N entries from the agent's live memory stream, specifying the verb 'recall' and the resource 'live memory stream (agents.memories)'. This distinguishes it from siblings like 'search_memories' or 'recall_nearby_memories' which have different scopes.
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 says 'Use to recall what you observed / did / talked about across sessions', providing a clear use case. However, it does not mention when not to use this tool or suggest alternatives like 'search_memories' for filtering, which would improve guidance.
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 clearly distinct purposes, with detailed descriptions differentiating them. However, the high count (60) introduces some overlap among memory-related tools (e.g., recall_nearby_memories, search_memories, recent_memory) and environment inspection tools (look_around, look_at, survey_site), causing minor ambiguity.
The majority of tools follow a consistent verb_noun pattern (e.g., enter_space, create_commitment, recall_nearby_memories). A few names break pattern, like cognitive_boot (adjective_noun) or who_is_here (phrase), but overall the naming is predictable and readable.
With 60 tools, the server exceeds the high end of the typical well-scoped range (3-15). While the domain is complex, many tools could be consolidated (e.g., multiple memory retrieval and building tools), making the surface feel bloated rather than lean and focused.
The tool set covers the core functionalities of the 3D world—spatial navigation, building, memory, commitments, skills, and social interaction—with few obvious gaps (e.g., no direct region deletion or agent interaction beyond chat). The breadth is appropriate for the domain, though some redundancy suggests minor over-engineering.