NeuralMind provides 14 tools with complete input schemas and descriptions. Naming follows verb_noun convention (query, audit, record, invalidate, search, build, etc.). Descriptions are substantive (100-200 chars typical), explaining WHAT and WHEN. However, output schemas are not documented, LLMs cannot predict return structure for downstream chaining. Error handling is absent from visible code; no recovery guidance or categorization. Parameters lack constraints (enums, ranges, patterns) despite accepting structured inputs. No tool annotations (readOnlyHint/destructiveHint) despite clear read/write distinction. Composition is sound, tools are single-responsibility and chainable via project_path. Security: project_path is user-supplied (path traversal risk if not validated internally); no visible secret injection or permission gates.

• Document output schemas for all tools. Example: neuralmind_query_decisions should specify {decisions: [{id, title, rationale, commit_sha, files, score}], total_count}. Enables LLM to chain results into downstream tools.
• Add tool annotations: mark read-only tools with readOnlyHint=true; mark write tools (record_decision, invalidate_decision, build) with destructiveHint=true and idempotentHint=false (or true if idempotent). Guides LLM retry logic.
• Implement input validation and error recovery. Validate project_path against a whitelist or sandbox; return actionable errors like 'Project not found at /path/to/project. Ensure path is absolute and readable.' Categorize errors as retryable (network) vs user-fixable (invalid path) vs fatal.
• Add parameter constraints: limit (1 - 100, default 10), depth (1 - 5, default 2), top_k (1 - 50, default 10). Use JSON Schema minimum/maximum and enum for boolean flags. Prevents LLM from passing absurd values.
• Implement pagination for list tools (audit_decisions, search, synaptic_neighbors). Return {results: [...], total_count, next_cursor} to handle large result sets without blowing context window.