The server defines a single tool 'think-strategies' with a highly complex, strategy-specific schema. While the tool has a description and input schema is visible, there are critical issues: (1) The tool name does not follow verb_noun convention (should be 'execute_thinking' or 'run_thinking_strategy'), making LLM intent inference difficult. (2) The input schema is enormously overloaded with 60+ optional parameters covering 11 different reasoning strategies, violating the single-responsibility principle. (3) Many parameters lack descriptions or have vague descriptions ('Current state in the thinking flow'). (4) No output schema is documented, LLMs cannot infer what this tool returns. (5) No error handling guidance provided. The schema itself is technically complete (JSON Schema with types), but the tool design violates fundamental composition and naming patterns.

• Rename tool to 'execute_thinking_strategy' or 'run_reasoning' to signal action intent. Use verb_noun convention so LLMs parse the name without reading the full description.
• Document the output schema explicitly. Include example output showing reasoning state, next action suggestions, and completion indicators. Without this, LLMs cannot chain results.
• Split strategies into separate tools or introduce a tighter 'strategy_mode' enum parameter with per-strategy sub-parameters grouped in conditional objects. Currently, LLMs must guess which of 60 params apply.
• Add required vs optional clarity: mark strategy-specific parameters (e.g., 'action' for ReAct) with descriptions like '(ReAct only) Specific action to take...' and document which params are mandatory for each strategy.
• Add error handling: document failure modes (divergence, cycles, depth limits) and what the LLM should do (retry with adjusted params, simplify strategy, escalate to user).
• Remove or clarify 'sessionToken' and 'currentState', these imply statefulness. Redesign as stateless: pass full reasoning context in each request, use opaque IDs if continuity is needed.
• Improve tool description to answer: What reasoning tasks does this solve? When should an LLM call this vs solving the problem directly? E.g., 'Use this for complex multi-step problems requiring structured reasoning (legal analysis, code design, research synthesis).'
• Add parameter constraints: specify min/max for thoughtNumber, totalThoughts, iterationNumber, cycleNumber. Cap evaluationScore range explicitly (0 - 10 is stated; enforce it).