"DaVinci Resolve" matching MCP servers:

MCP server that provides situated identity for agents by storing reusable stanzas and composing them into project protocols, enabling agents to resolve and materialize standing guidance.

A read-only MCP server over a published model catalogue that lets agents search and resolve models, fetch overlay profiles, definitions and citations, and check memory records against a governance overlay through the same code path as the CLI.

Enables AI agents to resolve typed routing, loop-stall arbitration, and tool safety checks locally at sub-millisecond latency, avoiding unnecessary frontier LLM calls. It enforces reversible execution checkpoints, token budgets, and decayed episodic memory for safe, privacy-first autonomous operation.

Exposes an Agent Context Graph and ContextPod contract over MCP so agents can describe, resolve, and inspect context revisions and events for a pod. It also lets clients append events while applying required semantic steps such as redaction, cache invalidation, supersession, conflict escalation, and release eligibility.

Enables agents to search, resolve, and request authorized integration actions on demand while keeping the full Pipedream catalog server-side, so teams can act on time-aware meeting memory and shared context without context overload.

Read-only MCP server that exposes an Obsidian vault to LLM clients over Streamable HTTP, letting them browse the accessible tree, read notes, run full-text searches, and resolve wiki-links and backlinks within a share-filtered note set. Access is limited by an operator-editable sharing config that is default-deny and never writes to the vault.

Exposes a deterministic, centrality-aware GraphRAG retrieval planner as operator tools over stdio or SSE, letting agents index corpora, run seed-steerable multi-hop retrievals, resolve entities, inspect graph neighborhoods, and fetch documents. All responses come back in milliseconds with zero LLM calls in the query path, so the calling agent does the reasoning while the server handles hop-by-hop retrieval.

Enables MCP clients to resolve legal contract clause cross-references and build defined-term dependency graphs, flagging circular references and substituting statutory definitions. It also parses document layouts and complex financial/legal tables, returning structured matrices and audit telemetry with zero external dependencies.

Enables MCP clients to infer and resolve skills from text, normalize skill names, perform semantic taxonomy searches, and inspect lifecycle governance.

Headless, peer-to-peer context synchronization for local AI agents. It enables multiple LLM agents to share structured context and resolve state conflicts over a serverless P2P network.

Enables AI subagents to share a persistent, versioned memory across sessions and machines, with tools to add, recall, inspect, merge, resolve conflicts, and report usage for rewards.

Enables agents to resolve routine typed routing, loop-stall arbitration, and tool safety checks locally instead of invoking a frontier LLM, returning schema-enforced decision outputs and telemetry at sub-millisecond latency. Also provides reversible execution checkpoints, token budgets, and decay-based episodic memory for safe, privacy-preserving autonomous operation.

Enables an MCP client to resolve household appliances, retrieve local manual/warranty/incident records, compare a stored warranty date, review persistent service case history, and draft cited repair cases that only a human can approve or close before export.

Enables AI agents to store, search, and resolve shared memories with full provenance, conflict detection, and an audit trail across MCP-compatible clients.

Read-only MCP server that serves the Archivo Epistemico (epistemic graph), enabling AI to read and query the epistemic path autonomously via tools like search, get_via, resolve, and get_necesidad.

Enables autonomous research workflows to construct and analyze multi-source cross-citation graphs and resolve consensus from structured outputs via MCP.

The open-source, self-organizing memory for all your AI tools. Persistent memory over MCP (remember, recall, observe): background agents extract entities, resolve conflicts, and keep cited syntheses current in an append-only, encrypted, single-tenant vault.