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samvallad33

Vestige

by samvallad33

contradictions

Spot conflicting memories by scanning a topic or recent entries, then review trust-weighted contradiction pairs exposed through local logic.

Instructions

Inspect memory disagreements directly. Scans a topic or recent memories for trust-weighted contradiction pairs using the same local logic as deep_reference.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum memories to analyze before pairwise contradiction detection.
sinceNoOptional RFC3339 timestamp; only memories updated after this time are considered.
topicNoOptional topic/query to scope contradiction detection. If omitted, scans recent memories.
min_trustNoMinimum trust score for both sides of a contradiction.

Schema Changelog

Changes observed during successful MCP inspections.

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

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that the tool is inspection-oriented ('Inspect', 'Scans'), defines the input scope ('topic or recent memories'), describes the output concept ('contradiction pairs'), and mentions trust weighting. It does not detail every edge case or output format, but the core behavior and non-mutating nature are reasonably transparent.

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 two sentences with no fluff. The core purpose is front-loaded in the first sentence, and the second adds necessary scoping and algorithmic context. Every phrase contributes to agent understanding, making it concise without sacrificing informativeness.

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

Completeness4/5

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

Given all parameters are optional, the schema is fully descriptive, and the description clarifies purpose, scope, and output type, this is nearly complete. There is no output schema, so the description could have said slightly more about the exact return structure, but 'contradiction pairs' plus the trust-weighting mention gives sufficient context for an agent to invoke the tool confidently.

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 adds a little meaning by linking 'topic or recent memories' to the topic/since parameters and 'trust-weighted' to min_trust, but it does not explain limit or since beyond what the schema already provides. The schema is strong enough that the description adds only marginal value here.

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 states a specific action ('Inspect memory disagreements'), identifies the exact resource ('memory disagreements'), and explains the mechanism ('Scans a topic or recent memories for trust-weighted contradiction pairs'). It also differentiates itself from the sibling deep_reference by noting it uses 'the same local logic' but is focused on direct contradiction inspection.

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?

The description clearly establishes when to use this tool: when you want to inspect memory disagreements directly by scanning a topic or recent memories. It does not explicitly state when not to use it or name alternative tools beyond the brief mention of deep_reference, so it lacks explicit exclusions but still provides clear contextual guidance.

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