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samvallad33

Vestige

by samvallad33

smart_ingest

Ingest knowledge with prediction error gating to automatically create, update, or supersede existing memories. Supports batch saves of up to 20 items at session end for efficient memory consolidation.

Instructions

INTELLIGENT memory ingestion with Prediction Error Gating. Single mode: provide 'content' to auto-decide CREATE/UPDATE/SUPERSEDE. Batch mode: provide 'items' array (max 20) for session-end saves — each item runs the full cognitive pipeline (importance scoring, intent detection, synaptic tagging).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags for categorization
itemsNoBatch mode: array of items to save (max 20). Defaults to force-creating each caller-separated item; set batchMergePolicy='smart' to allow Prediction Error Gating against existing memories. Use at session end or before context compaction.
sourceNoSource or reference for this knowledge
contentNoThe content to remember. Will be compared against existing memories. (Single mode)
node_typeNoType of knowledge: fact, concept, event, person, place, note, pattern, decisionfact
forceCreateNoForce creation of a new memory even if similar content exists
batchMergePolicyNoBatch mode only. Defaults to 'force_create' so caller-separated items stay separate. Use 'smart' to allow Prediction Error Gating against existing memories.force_create

Schema Changelog

Changes observed during successful MCP inspections.

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

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries full behavioral disclosure. It reveals the internal pipeline—Prediction Error Gating, importance scoring, intent detection, synaptic tagging—and the auto-decide behavior for CREATE/UPDATE/SUPERSEDE. It does not detail destructive consequences of superseding or return-value behavior, but the core mutation semantics are transparent.

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

Conciseness4/5

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

The description is two compact sentences with the primary purpose front-loaded and mode details following quickly. It avoids waste, though the all-caps 'INTELLIGENT' and parenthetical pipeline list add a bit of rhetorical weight without core semantic gain.

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?

For a complex ingestion tool with no output schema and no annotations, the description covers modes, pipeline, and batch limits well. It is still missing expected return values, explicit consequences of SUPERSEDE, and clear guidance on how this tool relates to alternatives like consolidate or apply_plan, leaving an agent to infer those from sibling names.

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 parameters are already fully documented and the baseline is 3. The description adds useful mode-level guidance by mapping 'content' to single mode and 'items' to batch mode, but it does not substantially extend the schema's own parameter explanations.

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

Purpose5/5

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

The description names a concrete function—intelligent memory ingestion—and specifies the two operating modes with distinct inputs: single mode via 'content' and batch mode via 'items'. The explicit decision outcome (CREATE/UPDATE/SUPERSEDE) makes the purpose unambiguous and helps distinguish it from retrieval or consolidation siblings.

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

Usage Guidelines4/5

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

It clearly states when to use each mode: single mode for one content item, batch mode for session-end saves, and the schema adds that batch use is appropriate before context compaction. However, it does not explicitly name alternatives or state when not to use this tool versus the many memory/merge/consolidation siblings.

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