AI that thinks more like humans do - MCP server with human-like cognitive architecture for enhanced reasoning, memory, and self-monitoring
Thought MCP presents a mixed picture. The server exposes 20 tools across two domains: memory management (tools 1-12) and cognitive reasoning (tools 13-20). All tool definitions are explicitly visible in source files with proper JSON Schema input definitions and descriptions. However, several critical issues reduce quality significantly: (1) Output schemas are NOT documented anywhere, no tool describes what fields it returns, violating pattern:tool and pattern:response-shaper. (2) Many parameter descriptions are vague or lack actionable constraint guidance (e.g., 'Optional text for semantic search' in recall tool lacks format/length specifics). (3) Error handling is completely absent from tool definitions, no guidance on recovery, retryability, or what to do on failure. (4) The 'think', 'analyze', 'ponder', 'breakdown', 'assess_confidence', 'detect_bias', 'detect_emotion', and 'evaluate' tools are LLM-facing reasoning tools that return unstructured reasoning output (implied, not documented), which violates pattern:response-shaper. (5) No tool documentation addresses idempotency, even though several tools (remember, update_memory, prune_memories) modify state and agents will retry on ambiguous failures. Tool names follow verb_noun convention consistently, which is good. Parameter typing is complete for all visible tools. However, the complete absence of output schema documentation and error handling, combined with STDIO transport, positions this server below the 60 threshold despite solid parameter definition work.
Analyze systematically using specified framework (scientific-method, design-thinking, systems-thinking, critical-thinking, creative-problem-solving, root-cause-analysis, first-principles, scenario-planning)
Assess confidence level in reasoning with evidence evaluation
Delete multiple memories in a single operation
Retrieve multiple memories by ID in a single operation
Store multiple memories in a single operation
Decompose a problem into constituent parts with optional depth control
Consolidate similar memories to reduce redundancy
Detect biases in reasoning with continuous monitoring option
Output schemas completely absent from all 20 tool definitions. No documentation of what fields, types, or structures are returned by any tool.
Error handling and recovery guidance completely absent from all tool definitions. No indication of what errors are possible, whether they are retryable, or what the LLM should do on failure.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 52 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 36 | - | v1 |
Detect emotional content in text with discrete emotion classification
Analyze reasoning comprehensively with optional confidence, bias, and emotion detection
Export memories with optional filtering
Delete a memory, with option for soft delete
Check the health status of a user's memory system
Think in parallel across multiple perspectives with optional timeout
Prune weak or old memories with configurable criteria
Retrieve memories from the memory system with optional filtering by sector, strength, salience, similarity, and date range
Store a memory in a specific sector (episodic, semantic, procedural, emotional, or reflective)
Search memories with comprehensive filtering capabilities
Initiate thinking process with specified mode (analytical, creative, critical, synthetic, or parallel)
Update an existing memory's content, strength, salience, or metadata
State-mutating tools (remember, update_memory, forget, batch_remember, batch_forget, prune_memories, consolidate_memories) lack idempotency declarations and have no confirmation/dry-run mechanism for destructive operations.
Reasoning tools (think, analyze, ponder, breakdown, assess_confidence, detect_bias, detect_emotion, evaluate) return unstructured reasoning output with no documented schema. LLM cannot parse or structure the reasoning for downstream use.
STDIO transport only, server cannot be used by hosted MCP clients or accessed remotely. Hard-capped at 50 for Protocol Readiness.
Many parameter descriptions are vague and lack actionable constraints. Examples: 'Optional text for semantic search' (no length, format, or language guidance), 'Memory content to store' (10-100k chars mentioned in description but not enforced in schema), 'Analysis framework' (no guidance on when to use each).
No tool annotations (readOnlyHint, destructiveHint, idempotentHint) declared in schemas. LLM cannot distinguish safe reads from state mutations without manual analysis of descriptions.
Batch tools (batch_remember, batch_recall, batch_forget) provide no per-item error handling. If one item fails, unclear whether the entire operation fails or partial results are returned.
Tools lack permission/scope declarations. No indication of what authorization each tool requires (e.g., read:memory, write:memory, delete:memory) or audit trail guidance.
Configuration tools (prune_memories, consolidate_memories) lack clarity on what actions modify state vs. read-only. The preview/list/prune enum suggests different behaviors but descriptions don't explain which is which.