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"A search related to the concept of thinking" matching MCP tools:

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    Chain of Draft Server is a powerful AI-driven tool that helps developers make better decisions through systematic, iterative refinement of thoughts and designs. It integrates seamlessly with popular AI agents and provides a structured approach to reasoning, API design, architecture decisions, code r
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    MIT

Matching MCP Connectors

  • Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…

  • the-committee MCP — wraps StupidAPIs (requires X-API-Key)

  • Retrieve the current state of a thinking session to access step-by-step reasoning, revisions, and chained operations for structured problem-solving.
    MIT
  • Search the global concept store in Kratos-MCP to retrieve relevant code snippets, comments, or runtime traces using query keywords, result limits, and optional concept filters.
  • List the available scopes on this server to group related tools and use them as search terms for filtering tool lists.
  • Submit an agency engagement enquiry for a founder-led discovery call. Choose from four scopes: workflow sprint, proof-of-concept, pilot support, or advisory. Get hands-on expert support beyond self-service learning.
    MIT
  • File a single concept, decision, or finding as a persistent memory node. Requires searching for existing duplicates before creating; optionally mark as transient for short-lived data like ticket state.
    MIT
  • Search across screen, voice, and clipboard entries to find content semantically related to any query. Returns a unified ranked list with source tags for open-ended recall spanning multiple data types.
  • Return isolated, non-transient concept nodes with no connections to surface dropped context, then suggest linking them to related ideas or archiving if irrelevant.
    MIT
  • Search a knowledge graph for entities like people, organizations, or technologies using semantic similarity. Discover who or what is mentioned in memories and find entities by concept.
    MIT
  • Retrieve a comprehensive list of available metrics from the dbt Semantic Layer. Use it as the initial step to identify relevant metrics for answering data or business-related queries.
  • Visualize the semantic topology of your codebase to identify which directories concentrate domain concepts, including entity counts and density, for a quick understanding of codebase layout before detailed analysis.
    MIT
  • List all code entities (classes/functions) that implement a given concept, such as loss functions or network architectures. Returns entity names with semantic tags, enabling concept-based code search beyond simple text matching.
    MIT
  • Look up a concept to find all variants, related concepts, naming conventions, function signatures, and file locations. Resolves questions like 'what is X', 'what does X mean', or 'where is X used'.
    MIT