world-model-mcp

resolve_contradiction

Resolve conflicting facts by choosing a winner via strategy: higher confidence, most recent, most sources, or manual override.

Instructions

Pick a winner between two contradicting facts using a confidence-weighted strategy (auto, keep_higher_confidence, keep_most_recent, keep_most_sources, supersede_a, supersede_b, manual).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "fact_a_id": "<fact_a_id>",
      "fact_b_id": "<fact_b_id>"
    },
    "name": "resolve_contradiction"
  }
}

Input Schema

Name	Required	Description	Default
`notes`	No
`strategy`	No
`fact_a_id`	Yes
`fact_b_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.3May 27, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only mentions the strategy selection and does not disclose side effects, persistence, reversibility, or what happens to the losing fact. This is a material gap for a tool that presumably modifies state.

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 immediately states the primary action and enumerates strategies in a parenthetical list. Every element serves a purpose, with no fluff or redundant repetition of schema information.

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

Completeness1/5

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

Given the absence of annotations, output schema, and any behavioral details, the description is far from complete. It fails to explain return values, the outcome of the resolution (e.g., which fact is updated), or any prerequisites. This is a mutation-like operation with substantial missing context, making the tool risky to invoke correctly.

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

Parameters2/5

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

The schema provides no descriptions (0% coverage), so the description must compensate. It lists possible strategy values, which adds some meaning, but it does not explain the semantics of each strategy (e.g., what 'auto' does) or describe the 'fact_a_id', 'fact_b_id', and 'notes' parameters beyond their schema names. The compensation is partial and insufficient.

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 the specific verb 'Pick a winner' and identifies the resource as 'two contradicting facts,' clearly indicating the tool's purpose. It differentiates from sibling tools like find_contradictions by focusing on resolution rather than detection, though it doesn't explicitly name alternatives.

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 description implies usage when a contradiction exists between two facts and provides a list of strategies, which serves as guidance on how to resolve. However, it lacks explicit exclusions or directives about when not to use this tool versus alternatives, leaving some ambiguity.

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