frontmatter-mcp
An MCP server for querying Markdown frontmatter with DuckDB SQL.
Configuration
{
"mcpServers": {
"frontmatter": {
"command": "uvx",
"args": ["frontmatter-mcp"],
"env": {
"FRONTMATTER_BASE_DIR": "/path/to/markdown/directory"
}
}
}
}
With Semantic Search
To enable semantic search, use the [semantic] extras:
{
"mcpServers": {
"frontmatter": {
"command": "uvx",
"args": ["--from", "frontmatter-mcp[semantic]", "frontmatter-mcp"],
"env": {
"FRONTMATTER_BASE_DIR": "/path/to/markdown/directory",
"FRONTMATTER_ENABLE_SEMANTIC": "true"
}
}
}
}
Installation (Optional)
If you prefer to install globally:
pip install frontmatter-mcp
# or
uv tool install frontmatter-mcp
Tools
query_inspect
Get schema information from frontmatter across files.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
Example:
// Input
{ "glob": "**/*.md" }
// Output
{
"file_count": 186,
"schema": {
"date": { "type": "string", "count": 180, "nullable": true },
"tags": { "type": "array", "count": 150, "nullable": true }
}
}
// Output (with semantic search ready)
{
"file_count": 186,
"schema": {
"date": { "type": "string", "count": 180, "nullable": true },
"tags": { "type": "array", "count": 150, "nullable": true },
"embedding": { "type": "FLOAT[256]", "nullable": false }
}
}
query
Query frontmatter data with DuckDB SQL.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
sql |
string | DuckDB SQL query referencing files table |
Example:
// Input
{
"glob": "**/*.md",
"sql": "SELECT path, date FROM files WHERE date >= '2025-11-01' ORDER BY date DESC"
}
// Output
{
"columns": ["path", "date"],
"row_count": 24,
"results": [
{"path": "daily/2025-11-28.md", "date": "2025-11-28"},
{"path": "daily/2025-11-27.md", "date": "2025-11-27"}
]
}
update
Update frontmatter properties in a single file.
| Parameter | Type | Description |
|---|---|---|
path |
string | File path relative to base directory |
set |
object | Properties to add or overwrite |
unset |
string[] | Property names to remove |
Example:
// Input
{ "path": "notes/idea.md", "set": {"status": "published"} }
// Output
{ "path": "notes/idea.md", "frontmatter": {"title": "Idea", "status": "published"} }
batch_update
Update frontmatter properties in multiple files.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
set |
object | Properties to add or overwrite |
unset |
string[] | Property names to remove |
Example:
// Input
{ "glob": "drafts/*.md", "set": {"status": "review"} }
// Output
{ "updated_count": 5, "updated_files": ["drafts/a.md", "drafts/b.md", ...] }
batch_array_add
Add a value to an array property in multiple files.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
property |
string | Name of the array property |
value |
any | Value to add |
allow_duplicates |
bool | Allow duplicate values (default: false) |
Example:
// Input
{ "glob": "**/*.md", "property": "tags", "value": "reviewed" }
// Output
{ "updated_count": 42, "updated_files": ["a.md", "b.md", ...] }
batch_array_remove
Remove a value from an array property in multiple files.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
property |
string | Name of the array property |
value |
any | Value to remove |
Example:
// Input
{ "glob": "**/*.md", "property": "tags", "value": "draft" }
// Output
{ "updated_count": 15, "updated_files": ["a.md", "b.md", ...] }
batch_array_replace
Replace a value in an array property in multiple files.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
property |
string | Name of the array property |
old_value |
any | Value to replace |
new_value |
any | New value |
Example:
// Input
{ "glob": "**/*.md", "property": "tags", "old_value": "draft", "new_value": "review" }
// Output
{ "updated_count": 10, "updated_files": ["a.md", "b.md", ...] }
batch_array_sort
Sort an array property in multiple files.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
property |
string | Name of the array property |
reverse |
bool | Sort in descending order (default: false) |
Example:
// Input
{ "glob": "**/*.md", "property": "tags" }
// Output
{ "updated_count": 20, "updated_files": ["a.md", "b.md", ...] }
batch_array_unique
Remove duplicate values from an array property in multiple files.
| Parameter | Type | Description |
|---|---|---|
glob |
string | Glob pattern relative to base directory |
property |
string | Name of the array property |
Example:
// Input
{ "glob": "**/*.md", "property": "tags" }
// Output
{ "updated_count": 5, "updated_files": ["a.md", "b.md", ...] }
index_status
Get the status of the semantic search index.
This tool is only available when FRONTMATTER_ENABLE_SEMANTIC=true.
Example:
// Output (not started)
{ "state": "idle" }
// Output (indexing in progress)
{ "state": "indexing" }
// Output (ready)
{ "state": "ready" }
index_refresh
Refresh the semantic search index (differential update).
This tool is only available when FRONTMATTER_ENABLE_SEMANTIC=true.
Example:
// Output
{ "state": "indexing", "message": "Indexing started", "target_count": 665 }
// Output (when already indexing)
{ "state": "indexing", "message": "Indexing already in progress" }
Technical Notes
All Values Are Strings
All frontmatter values are passed to DuckDB as strings. Use TRY_CAST in SQL for type conversion when needed.
SELECT * FROM files
WHERE TRY_CAST(date AS DATE) >= '2025-11-01'
Arrays Are JSON Strings
Arrays like tags: [ai, python] are stored as JSON strings '["ai", "python"]'. Use from_json() and UNNEST to expand them.
SELECT path, tag
FROM files, UNNEST(from_json(tags, '[""]')) AS t(tag)
WHERE tag = 'ai'
Templater Expression Support
Files containing Obsidian Templater expressions (e.g., <% tp.date.now("YYYY-MM-DD") %>) are handled gracefully. These expressions are treated as strings and naturally excluded by date filtering.
Semantic Search
When semantic search is enabled, you can use the embed() function and embedding column in SQL queries. After running index_refresh, the markdown body content is indexed as vectors.
-- Find semantically similar documents
SELECT path, 1 - array_cosine_distance(embedding, embed('feeling better')) as score
FROM files
ORDER BY score DESC
LIMIT 10
-- Combine with frontmatter filters
SELECT path, date, 1 - array_cosine_distance(embedding, embed('motivation')) as score
FROM files
WHERE date >= '2025-11-01'
ORDER BY score DESC
LIMIT 10
Environment variables:
| Variable | Default | Description |
|---|---|---|
| FRONTMATTER_BASE_DIR | (required) | Base directory for files |
| FRONTMATTER_ENABLE_SEMANTIC | false | Enable semantic search |
| FRONTMATTER_EMBEDDING_MODEL | cl-nagoya/ruri-v3-30m | Embedding model name |
| FRONTMATTER_CACHE_DIR | FRONTMATTER_BASE_DIR/.frontmatter-mcp | Cache directory for embeddings |
License
MIT