Sentor MCP Server

Score sentiment toward specific entities in text. Cluster documents and name topics. Exposed over MCP by the sentor mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.

What it actually does

Sentor is an entity-based sentiment analysis platform powered by fine-tuned BERT models. This MCP server exposes Sentor's ML APIs as tools your AI assistant can call directly — score sentiment toward specific entities in text, cluster documents by topic, and generate topic labels, all from a single natural-language prompt.

Configuration

You will need 2 environment variables: SENTOR_API_KEY, SENTOR_BASE_URL. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.

Adding it to your client

Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.

When to reach for it

Among the developer tooling options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. It is maintained by NIKX-Tech; worth a glance at recent repository activity before you build anything load-bearing on it.

This entry was verified against Sentor's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.

Caveats

• Your data travels to the provider's service, so the usual questions apply about what you send and what they retain.
• Missing credentials fail quietly in some clients — if no tools show up, check the environment block first.
• MCP clients confirm each tool call by default. Leave that on until you have watched what the sentor mcp server does with a few real requests.