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samvallad33

Vestige

by samvallad33

dream

Replay recent memories to discover hidden connections, synthesize insights, and reinforce important patterns. Returns insights, connections, and statistics.

Instructions

Trigger memory dreaming — replays recent memories to discover hidden connections, synthesize insights, and strengthen important patterns. Returns insights, connections, and dream stats.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memory_countNoNumber of recent memories to dream about (default: 50)
min_similarityNoMinimum similarity for connection discovery (0.0-1.0, default: 0.5)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.6.0
  2. Removedv2.3.0
  3. First observedv2.1.27

TDQS

B3.3/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It says the tool 'strengthens important patterns,' which strongly implies mutation of memory state, but never states whether this is a read-only operation, whether changes are reversible, or what side effects occur. It also does not mention cost or runtime considerations. This ambiguity is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that conveys the action, the purpose, and the expected return value without wasted words. It delivers high information density in under thirty words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description provides a high-level output ('insights, connections, and dream stats') but lacks detail on return structure, potential side effects, prerequisites (e.g., sufficient memory history), or performance implications. Given no output schema and no annotations, this leaves noticeable gaps for an agent deciding whether to invoke the tool and how to interpret its effects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description reinforces the ideas of 'recent memories' (memory_count) and 'connection discovery' (min_similarity) but adds no detail beyond the schema. It neither compensates for nor obscures the parameter meanings.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Trigger') with a clear resource ('memory dreaming') and states the outcome: 'replays recent memories to discover hidden connections, synthesize insights, and strengthen important patterns.' This distinguishes the tool's core function, though it does not explicitly differentiate it from similar siblings like explore_connections or consolidate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose is implied through the stated outcomes—use when you want to discover hidden connections, synthesize insights, or strengthen patterns. However, there is no explicit guidance on when NOT to use this tool or which siblings (explore_connections, consolidate, predict) are better suited for similar goals. The large sibling list makes this ambiguity more costly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.