"DaVinci Resolve" matching MCP servers:

Helps merchants resolve credit card disputes by searching VISA guidelines and checking dispute history to generate structured recommendations.

Enables AI agents to discover, inspect, compare, and resolve local fonts and typography through bounded search, paging, family inspection, exact assets, contextual compositions, and preview images, while keeping fonts and model inference local.

Enables querying Context7 V2 documentation through resolve-library-id and query-docs tools, alternating between two API keys and retrying when a key is blocked.

Provides standardized tools for querying shipment status and carrier events, searching SOPs, checking and reserving warehouse inventory, retrieving IoT telemetry, and generating carrier SLA claim tickets so agents can diagnose and resolve logistics incidents.

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.

Enables AI agents to retrieve up-to-date documentation for Russian services (payments, fiscal receipts, messengers, CRM, data/SMS/AI, 1C) and the verified integration pitfalls tied to each topic, with links back to the original sources. Documentation is indexed locally and refreshed automatically, so agents can resolve libraries, fetch topical fragments, and pull gotchas on demand.

An MCP server for Chinese financial reports, providing 44 tools to resolve companies, list/retrieve filings, extract report sections, run AI-based indicator extraction, search via Elasticsearch, and browse a 21,698-rule industry rules dashboard.

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.

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.

Connects to Stack AI workflows, allowing you to run workflows and pass user inputs through MCP-compatible clients.