This server presents a severely underdeveloped tool definition that fails to meet baseline production standards. The single 'resolve' tool has a minimalist input schema with no parameter descriptions, a vague one-line description, and no documented output schema. The implementation appears to be a stub that returns a hardcoded string rather than performing actual conflict resolution. The tool violates multiple critical patterns from the 54 Agentic Tool Patterns baseline: no parameter annotations, no output structure documentation, and no error handling guidance. The description 'Resolves conflicts among multiple agent propositions by merging consensus' is too generic to guide LLM tool selection and lacks actionable context (When should this tool be called? What does 'consensus' mean? What format do propositions expect? What does the output contain?). The only parameter 'propositions' has a description that merely restates the parameter name, this is below baseline for production tools. No security validation, no retry guidance, and no output schema documentation are evident.

• Expand the tool description to 100-200 characters following LLM-optimized patterns: explain the conflict resolution strategy (e.g., 'Merges multiple agent propositions by finding common agreement on facts and decisions. Returns a consensus statement and confidence score.'), clarify when to call it (e.g., 'Use when multiple agents have proposed different solutions to the same problem'), and document the expected input format (e.g., 'Each proposition should be a single-sentence fact, decision, or solution').
• Add formal parameter descriptions: 'propositions: List of 2 - 20 string propositions from different agents or knowledge sources. Each proposition should be a single sentence (50 - 500 chars) describing a fact, decision, or solution to the problem. Example: ["Allocate budget to Q1 marketing", "Reallocate budget to Q1 marketing"].'
• Document the output schema explicitly in the tool description or add a separate 'Output' section: 'Returns a dict with keys: agreed_consensus (str, the merged consensus), confidence (float 0 - 1, how certain the consensus is), reasoning (str, explanation of how propositions were merged), disagreements (list[str], propositions that could not be merged).'
• Implement real conflict resolution logic instead of a stub. Choose a concrete strategy (e.g., semantic similarity matching, voting-based consensus, LLM-powered merging) and document it in the description so agents understand what to expect.
• Add input validation and error handling: check for empty propositions list, validate proposition length/format, return actionable errors like 'Need at least 2 propositions to resolve conflicts; received 1.' and 'Each proposition must be 10 - 500 characters; proposition 3 is 8 characters.'