Alembica MCP

[logo] alembica

Open Science Software for Semantic Synthesis and Extraction of Information from Unstructured Sources.

[Go Reference] [Go Report Card] [DOI] [MCP Registry]

About

alembica simplifies the use of Large Language Models (LLMs) to extract structured datasets from unstructured corpora of text. It provides a flexible and scalable framework to process, synthesize, and transform textual information into structured formats suitable for analysis and further processing.

Supports OpenAI, Google AI, Anthropic, Cohere, DeepSeek, Perplexity, AWS Bedrock, Azure AI, Vertex AI, and Self-Hosted OpenAI-compatible providers.

Installation (Go)

To install alembica in Go, run:

go get github.com/open-and-sustainable/alembica

If you want to use alembica in other programming languages, check out the C-Shared Library in the User Guide.

Documentation

User Guide – Learn how to use alembica in different programming languages. API Reference – Explore the Go package documentation.

MCP Server

alembica includes an optional MCP server for agent tool access.

The MCP server can be used from a locally built Go binary, the GHCR container image, or the MCP Registry.

Available in the official MCP Registry:

• https://registry.modelcontextprotocol.io/?q=alembica
• Server ID: io.github.open-and-sustainable/alembica-mcp

See the User Guide MCP page for installation options, run commands, and tool schemas.

MCP Registry publishing

The MCP server is published to the MCP Registry by GitHub Actions when a version tag such as v1.2.3 is pushed.

The workflow uses GitHub OIDC for authentication, and each new registry version is published explicitly by CI rather than implicitly from GitHub Releases.

Features

• Validation of Input – Ensures that queries are correctly formatted to support proper interaction with models.
• Cost Assessment – Calculates token costs based on the requested extraction and different model pricing.
• Data Extraction – Processes unstructured text and transforms it into structured datasets for further analysis.

Note: Cost estimation is not supported for Self-Hosted, AWS Bedrock, Azure AI, or Vertex AI providers and will return zero.

Optional model fields for cloud/local providers:

• base_url and api_version for Azure/OpenAI-compatible endpoints
• region for AWS Bedrock
• project_id and location for Vertex AI

Use schemaVersion: "v2" when you need these cloud/local provider fields or non-enumerated model IDs.

Authors & Contributions

Author: Riccardo Boero - [email protected]

Contributions are welcome!

License

alembica is licensed under the GNU AFFERO GENERAL PUBLIC LICENSE, Version 3.

[AGPL License]

Citation

Boero, R. (2025). alembica - Open Science Software for Semantic Synthesis and Extraction of Information from Unstructured Sources. Zenodo. https://doi.org/10.5281/zenodo.14899666