Both tools have minimal descriptions (under 100 chars) and severely underdeveloped parameter documentation. Input schemas exist but lack parameter-level descriptions entirely, LLMs cannot understand what variable_traces or invariants should contain. No output schema documented. No error handling guidance. No parameter constraints (enums, ranges, formats). Tool names lack action verbs (infer_invariants is acceptable; synthesize_assertion_code is vague about what 'code' means). This server fails to meet baseline standards for LLM-driven tool selection.

• Add 150-200 character descriptions to both tools explaining WHAT they do, WHEN to use them, and WHAT the output enables. Example: 'infer_invariants: Analyzes execution state snapshots to detect runtime invariants (non-null, bounds, constants). Use this to understand agent behavior patterns before generating assertions.'
• Document parameter schemas with explicit field definitions. For variable_traces, specify: 'Array of state snapshots. Each object maps variable names (string keys) to their values (any type). Example: [{"x": 5, "y": null}, {"x": 10, "y": "active"}]'
• Add output schema documentation. Specify that infer_invariants returns {"invariants": [{"variable": string, "predicate": string, "type": enum, "confidence": number}]} and synthesize_assertion_code returns {"assertions": string}.
• Add error handling with recovery hints. E.g., 'If variable_traces is empty, provide at least 2 state snapshots. If a variable is missing in some snapshots, it will be skipped, ensure all variables appear in all snapshots for complete analysis.'
• Rename synthesize_assertion_code to generate_assertions or synthesize_assertions for clarity. Current name is vague about what 'code' means (Python? Generic pseudocode?).