mcp server
Nordic Financial MCP
Semantic search over Nordic filings, press releases, macro data and electricity prices.
Description as published by the maintainer. Source
- version 1.1.0
- active
- retrieval
active — Most recent push to the repository was 2026-06-04. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-06-04 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 7 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.1.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-04-22 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-04-22 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
Darwin RAG
— last commit 2026-07-30, shares rag-pipeline
Local-first RAG engine with MCP server for AI agent integration.
-
Calypso Multimodal RAG MCP
— last commit 2026-06-09, shares rag-pipeline
Calypso multimodal RAG for grounded answers from docs, images, charts, and knowledge.
-
ArcadeDB MCP Server
— last commit 2026-08-06, shares vector-database
Built-in MCP server for ArcadeDB multi-model database (graph, document, vector, time-series)
-
GrantAi Memory
— last commit 2026-03-16, shares vector-database
Persistent memory for AI agents. Infinite context with sub-millisecond recall.
-
Citadel
— last commit 2026-08-06, shares vector-database
Encrypted-first embedded database with vector search and agent memory, exposed as MCP tools
-
ArcadeDB
— last commit 2026-08-06, shares vector-database
Multi-Model database with built-in MCP server for SQL, Cypher, Gremlin, and GraphQL.
-
io.github.barateza/mcp-plesk-dev-docs
— last commit 2026-07-30, shares vector-database
An MCP server that indexes and retrieves Plesk documentation.
-
io.github.bobybarack/sovseal-memory
— last commit 2026-08-05, shares vector-database
Local-first, zero-knowledge AI memory across Claude, Cursor, and every MCP client. 0 RTT reads.
-
VelesDB Memory
— last commit 2026-08-06, shares vector-database
Offline agentic memory: remember/recall/relate/forget/why over a fused vector+graph+columnar engine
-
hubmesh
— last commit 2026-08-05, shares vector-database
Deterministic multi-hop graph retrieval for RAG. Zero LLM calls in the query path.
These share tags the maintainers applied themselves, such as rag-pipeline, vector-database. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
Also from aidatanordic
-
io.github.AIDataNordic/food-recipe-mcp
— last commit 2026-07-14
Semantic search across 50,000+ food recipes with hybrid retrieval and reranking.
How the author describes it
Topics the maintainer set on GitHub: rag-pipeline, vector-database.
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
Join ZBS AI Practice Lab