tei-mcp

An MCP server that helps AI agents read and write valid TEI XML. It parses the TEI P5 specification and exposes 21 tools spanning two complementary capabilities: schema grounding (element lookup, attribute resolution, content model expansion, nesting validation, document validation, ODD customisation) and span-locked composition (annotation of source text without letting the model rewrite the body).

Features

• Element, class, macro, and module lookup with case-insensitive matching and typo suggestions

• Attribute resolution across the full TEI class hierarchy (local + inherited)

• Content model expansion into structured trees with class and macro resolution

• Nesting validation (direct parent-child and recursive reachability with path tracking)

• Document validation against TEI P5: content models, attributes, closed value lists, reference integrity, deprecation warnings

• Single-element validation for incremental editing workflows

• ODD customisation support: load a project ODD to constrain the schema (moduleRef filtering, elementSpec delete/change, attDef modifications)

• Regex search across all entity types (elements, classes, macros, modules)

• Deprecation awareness with validUntil dates and replacement suggestions

• Attribute suggestion by intent description (keyword matching against attribute descriptions)

• Span-locked composition with a byte-equality body-text invariant: the model annotates source plaintext by registering tag offsets, and the composer assembles the final TEI without ever letting the model rewrite the body. See Span-Locked Composition below.

• Local and remote usage: all tools work both when the server runs on your machine and when it runs on a remote server

Related MCP server: BigContext MCP

Requirements

• Python 3.10+

• uv (recommended) or pip

Installation

The quickest way is via uvx, which fetches and runs the server automatically:

uvx tei-mcp

Or install from PyPI:

pip install tei-mcp

Or clone and install from source:

git clone https://github.com/Pantagrueliste/tei-mcp.git
cd tei-mcp
uv sync

On first run, the server downloads p5subset.xml from the TEI website (~5 MB) and caches it locally.

Usage

Local server (stdio)

When you run tei-mcp on your own machine, it communicates over stdio. Add the following to your client's MCP server configuration:

{
  "mcpServers": {
    "tei": {
      "command": "uvx",
      "args": ["tei-mcp"]
    }
  }
}

Where this file lives depends on your client:

Remote server (HTTP)

tei-mcp can also run as a remote HTTP server, so you don't need to install anything locally. Run it with:

fastmcp run tei_mcp/server.py:mcp --transport streamable-http --host 0.0.0.0 --port 8000

Then point your MCP client at the server URL (e.g., http://your-server:8000/mcp).

When the server runs remotely, it cannot access files on your computer. Tools that work with documents (validate_document, load_customisation) accept the XML content directly as a string, so the AI agent can read your local file and send its content to the remote server. See Working with documents below.

Tools

Most schema-grounding tools accept use_odd=True to query the customised schema instead of the full TEI P5. The span-locked tools (get_source, tag_span, compose, list_tags, reset_tags) require TEI_MCP_SPAN_SOURCE_ROOT to be configured (see Span-Locked Composition).

Working with documents

validate_document and load_customisation both need access to XML files. They support two ways of receiving them:

• By file path (file_path / odd_path): the server opens the file from disk. This is the simplest option when the server runs on your own machine.

• By content (xml_content / odd_content): the XML is passed directly as a string. This is how remote servers work — the AI agent reads your local file and sends its content to the server.

You don't need to choose or configure anything. When you ask the AI agent to validate a document, it will automatically use the right approach depending on whether the server is local or remote.

Examples

Local server (file path):

validate_document(file_path="/path/to/my-document.xml")
load_customisation(odd_path="/path/to/my-project.odd")

Remote server (content):

validate_document(xml_content="<TEI xmlns='...'>...</TEI>")
load_customisation(odd_content="<TEI xmlns='...'>...</TEI>")

validate_document also supports authority files (for reference integrity checks) in both forms: authority_files for local paths, authority_contents for XML strings.

ODD Customisation

Load a project-specific ODD file to constrain the schema:

1. Call load_customisation(odd_path="/path/to/my-project.odd")
   — or load_customisation(odd_content="<TEI>...</TEI>") for remote servers
2. Use use_odd=True on subsequent tool calls
3. Call unload_customisation() to revert to the full spec

Supported ODD features:

• moduleRef with include / except filtering

• elementSpec mode="delete" to remove elements

• elementSpec mode="change" with attDef modifications (delete, change, add)

• Closed/semi value list restrictions

Span-Locked Composition

A pattern for using language models to encode TEI without letting them rewrite the source.

In standard generation, a model is asked to produce TEI directly from a piece of source text. The model often produces output that looks correct but silently modifies the body — modernised spelling (mesme → même), dropped commas, archaic pronouns substituted (luy → lui), or wholly invented passages. Validators downstream cannot catch these errors: the output is well-formed and schema-valid, and only character-level diffing against the source surfaces the discrepancy. For archival workflows where the encoded text becomes a permanent record, this is the failure mode that matters most.

Span-locked composition prevents this by construction. The model never types body text. It retrieves the source through get_source, registers tags as offset ranges over that source through tag_span, and then asks the server to assemble the final TEI through compose. The composer interleaves the recorded tags with the source plaintext and verifies byte-by-byte that the rendered TEI's flat text content equals the source before returning. If the model's tags would yield a document whose body text differs from the source, compose() raises rather than returning a corrupted document.

This is complementary to schema grounding. The schema-grounding tools (validate_document, lookup_element, valid_children, etc.) help the model produce valid TEI; span-locked composition guarantees that the body text inside that TEI is faithful to the source. The two together cover both axes a deployable encoding workflow has to satisfy.

Configuration

Set TEI_MCP_SPAN_SOURCE_ROOT to a directory containing source plaintext files. Each file's stem becomes its document ID (e.g., letter_001.txt is addressed as letter_001). Source files are loaded lazily on first reference and cached for the lifetime of the server process.

export TEI_MCP_SPAN_SOURCE_ROOT=/path/to/sources
uvx tei-mcp

Workflow

1. Call get_source("letter_001") to retrieve the immutable body text.

2. Issue one or more tag_span("letter_001", start, end, element_path, attrs) calls to register tags at character offsets.

3. Call compose("letter_001") to obtain the final TEI fragment, with the body-text byte-equality check enforced.

4. Optionally call list_tags to inspect, or reset_tags to start over.

element_path is a slash-separated path documenting nesting context (e.g. TEI/text/body/p/persName); only the last segment becomes the element's local name. The rest is recorded for provenance.

Limitations

• Recorded tags are kept in process memory and do not survive server restart.

• compose() does not currently check that the registered tags are admissible per the loaded ODD customisation. Validate the composed output with validate_document as a separate step if schema validity matters for your workflow.

• Source files are read from disk at first reference, so the source-root directory must be readable at the time get_source is invoked.

Environment Variables

Development

# Install dev dependencies
uv sync

# Run tests
uv run pytest

# Run tests with coverage info
uv run pytest -v

License

MIT