Cognitive Architecture MCP Server Implementation

This project constitutes a TypeScript-based Model Context Protocol (MCP) backend, architecturally inspired by Arben Ademi’s foundational work in Sequential Thinking (originally implemented in Python). The core objective is to enable automated generation of rich, navigable concept maps by LLMs, integrating rigorous, mandated self-monitoring protocols.

Initial Configuration

To integrate this utility within an MCP-compatible client environment (such as Claude Desktop or Cursor), configure the tool specification as follows:

{ "cognitive-architecture": { "command": "npx", "args": ["-y", "cognitive-architecture-framework"] } }

Operational Paradigm

Artifact Quality Indexing

Upon recording any cognitive artifact (a 'thought'), the LLM must assign a scalar quality rating, constrained between 0.0 and 1.0. This metric, interpreted alongside the artifact's current developmental stage, serves as the primary input for generating 'metacognitive' directive feedback, guiding the LLM's cognitive trajectory.

Developmental State Labeling

Every artifact is ascribed a discrete state label (e.g., 'Premise Establishment', 'Deep Dive Analysis', 'Divergent Concept Generation'). These stages dictate the lifecycle management of the LLM's thought process. In the present iteration, these labels exert substantial influence; prolonged stagnation within one state, or consistent low-quality submissions for that state, triggers corrective prompts intended to pivot the LLM toward alternative operational modes or strategies atypical for the current stage (e.g., encouraging speculative creativity during a predominantly deductive operational phase).

Reasoning Branching Capability

The LLM possesses the facility to initiate distinct 'branches' stemming from any specific artifact, facilitating simultaneous exploration of parallel lines of logical inference. Each branch maintains independent tracking, essential for managing concurrent scenarios involving multiple competing hypotheses or potential resolutions.

Information Retention Protocols

The server maintains two distinct memory tiers: a 'working buffer' retaining the LLM's ten most recently processed artifacts, and a 'historical archive' where older artifacts are indexed by their associated tags, enabling retrieval for comprehensive synthesis of the entire cognitive trajectory concerning a defined subject.

Current Architectural Constraints

Primitive Metacognitive Oversight

Presently, the generation of quality metrics and subsequent advisory feedback relies on rudimentary, mechanically applied stage-specific multipliers acting upon the single, self-reported quality score.

Future enhancements are slated to incorporate advanced oversight mechanisms, including semantic content analysis, cross-artifact verification routines, and more nuanced detection heuristics for logical fallacies.

Lack of Persistency and Visualization Layer

All recorded cognitive data resides strictly in volatile memory and is not currently written to persistent storage (filesystem or database). Furthermore, the system lacks any graphical user interface for inspecting the evolving conceptual graph.

Planned roadmap items include the integration of a lightweight client interface to allow real-time observation of the thought topology's progression.

Exposed MCP Endpoints

The server furnishes the following programmatic interfaces:

record_cognitive_artifact

Instantiates a new artifact within the sequential history, requiring associated metadata regarding its classification, assessed quality, semantic content, and relational linkages.

Parameters: - content: The textual payload of the current cognitive artifact - sequence_index: The current ordinal position in the processing timeline - projected_total: The anticipated final count of artifacts - continuation_required: Boolean indicating if subsequent artifact submission is expected - processing_state: The active cognitive phase (e.g., "Premise Establishment", "Synthesis") - is_amendment (optional): Flag indicating revision of prior content - target_artifact_id (optional): Identifier of the artifact being modified - divergent_origin_node (optional): The artifact initiating a new parallel exploration path - branch_identifier (optional): Unique key for the current reasoning branch - further_input_solicited (optional): Indicator for the need for more iterative input - confidence_rating (optional): The objective quality score (range 0.0 to 1.0) - classification_tags (optional): Descriptive keywords or categories

amend_cognitive_artifact

Modifies an existing artifact in the history, utilizing parameters similar to record_cognitive_artifact.

Parameters: - artifact_uid: The unique identifier of the target artifact - Parameters from record_cognitive_artifact

fetch_related_artifacts

Queries the long-term archive to retrieve historical artifacts sharing classification tags with the specified artifact.

Parameters: - artifact_uid: The identifier of the artifact for which related context is sought

synthesize_cognitive_narrative

Generates a holistic, aggregated summary detailing the complete intellectual progression.

purge_cognitive_session

Empties all recorded artifacts and reinstates the server to its initial quiescent state.

Licensing

Distributed under the MIT License.

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