Anticipate which memories an AI coding agent will need next by analyzing context and recent activity, returning suggestions and speculative retrievals.
Proactive memory prediction — predicts what memories you'll need next based on context, recent activity, and learned patterns. Returns predictions, suggestions, and speculative retrievals.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Current context for prediction |
Changes observed during successful MCP inspections.
v2.6.0v2.3.0v2.1.27Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the burden. It discloses that the tool performs prediction and returns 'predictions, suggestions, and speculative retrievals,' but does not state whether the call is side-effect free, whether predictions are persisted, or any reliability caveats beyond 'speculative.'
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 a compact single sentence with the key directive front-loaded. The phrase 'Proactive memory prediction' slightly repeats the verb 'predicts,' but overall it is efficient.
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 no annotations, no output schema, and several potentially overlapping siblings, a one-line description is insufficient. It omits side-effect guarantees, return format, and distinction from tools like dream, explore_connections, or intention, leaving an agent to guess when this tool is the correct choice.
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 single 'context' parameter is described in the schema, so the description is not required to add much. It adds a little value by explaining that predictions are based on context, recent activity, and learned patterns, but it does not elaborate on nested fields.
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 identifies a verb ('predicts') and resource ('what memories you'll need next'), and differentiates itself as 'proactive' memory prediction. It does not explicitly distinguish itself from overlapping siblings such as 'dream' or 'intention', so it stops short of a 5.
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 implies when to call the tool—when anticipating future memory needs from context, recent activity, and learned patterns—but never states when not to use it or names alternatives. No explicit routing guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.