mini-app-mcp

Agent-First CRUD store MCP server — schema.yaml driven, SQLite backend, multi-table in a single daemon.

What it does

mini-app-mcp is a lightweight MCP server that manages one or more SQLite tables in a single running process. The shape of each table is defined entirely by a schema.yaml file; no migrations, no REST API, no GUI. CRUD is exposed exclusively as MCP tools, making it a natural backend for agents that need structured persistent storage.

Related MCP server: Claude State MCP Server

Design principles

• schema.yaml as sole schema authority — field names, types, and required constraints are read from YAML at startup. No field is hard-coded in application code.

• Multi-table in one daemon — a single server process discovers and mounts all tables found under the configured User and Project scope directories. A dedicated legacy mode (MINI_APP_SCHEMA + MINI_APP_DB) preserves the original single-table behaviour.

• MCP-only entry point — there is no HTTP/REST/CLI CRUD interface. All reads and writes go through MCP tools.

• Structured JSON errors — every error response carries a machine-readable code field so agents can handle failures programmatically.

schema.yaml format

table: issues
title: "Issue tracker"
description: "Tracks bugs and feature requests for a project."
fields:
  - name: title
    type: string
    required: true
    description: "Short one-line summary of the issue."
  - name: state
    type: string
    required: false
  - name: tags
    type: array
    required: false

The optional title and description keys at the table level provide human- and AI-readable metadata about the table. Each field entry may also carry an optional description string. All three keys follow the OpenAPI 3.1 / JSON Schema 2020-12 naming convention and are included in info tool output and the schema://json resource.

Supported types: string, number, boolean, array, object.

Configuration

Multi-table mode (recommended)

Tables are discovered at startup by scanning both directories. Project-scope definitions take precedence over User-scope definitions for the same table name.

Legacy single-table mode

When MINI_APP_SCHEMA and MINI_APP_DB are set the server starts in legacy mode, mounting exactly one table. The table argument on all tools may be omitted in this mode.

All variables can also be placed in a .mini-app-mcp.env file in the working directory.

MCP tools

All tools accept an optional table argument that selects the target table. In multi-table mode the argument is required; omitting it returns error code TABLE_REQUIRED. Supplying an unknown table name returns error code TABLE_NOT_FOUND. In legacy single-table mode the argument may be omitted.

MCP resources

In addition to the 17 tools above, the server exposes 7 read-only Resources addressable by URI. Resources are intended for agents that want to fetch the schema definition or reference documentation without invoking a mutating tool.

The info tool and schema://json resource return equivalent content but serve different purposes: info is a callable tool (good for one-off introspection in a conversation), while resources are URI-addressable and can be subscribed to or cached by the client.

Usage

Start the server via the --mcp flag (required; the binary has no other entry point).

Multi-table mode

Place each table's schema.yaml and <table>.db under ~/.mini-app/<table>/ (User scope) or ./.mini-app/<table>/ (Project scope), then start without any extra environment variables:

mini-app-mcp --mcp

Register it once in .mcp.json to serve all mounted tables:

{
  "mcpServers": {
    "mini-app": {
      "command": "mini-app-mcp",
      "args": ["--mcp"]
    }
  }
}

Legacy single-table mode

MINI_APP_SCHEMA=./schema.yaml MINI_APP_DB=./issues.db mini-app-mcp --mcp

Or configure via .mini-app-mcp.env:

MINI_APP_SCHEMA=./schema.yaml
MINI_APP_DB=./issues.db

Dump / file materialization

mini-app-mcp can write each created or updated row to disk as a Markdown file. This is useful for agents that read context from files, for version-controlling records with git, or for quick human inspection.

How it works

After every successful create or update call the server writes (or overwrites) a file:

<dump-dir>/<id>.md

The file format is:

# <title-field value>

<body-field value>

delete does not remove the dump file by default (the record stays on disk as an archive).

Enabling dump in schema.yaml

Add a dump: section to your schema.yaml:

table: issues
fields:
  - name: title
    type: string
    required: true
  - name: body
    type: string
    required: false
dump:
  dir: ./issues          # optional; default: <cwd>/.mini-app/<table>/
  title_field: title     # optional; default: title
  body_field: body       # optional; default: body
  sync: write-only       # optional; default: write-only

Schema management

mini-app-mcp exposes four tools for managing table schemas at runtime without restarting the server.

Creating a table

schema_create(scope="project", table="notes", title="Notes", description="Personal notes.", fields=[...])

The tool writes <scope_dir>/notes/schema.yaml and immediately registers the new table in the live registry. Calling it again for the same table name returns SCHEMA_EXISTS. The optional title and description arguments are written to the YAML file and are immediately visible via info or schema://json. Individual field entries may also include a description string.

Updating a schema

schema_update(scope="project", table="notes", title="Notes", description="Updated notes.", fields=[...])

Before overwriting, the server backs up the existing schema.yaml and a point-in-time SQLite snapshot to <scope_dir>/_backup/notes.<timestamp>.yaml and <scope_dir>/_backup/notes.<timestamp>.db. The live registry is refreshed after the write. No DDL migration is applied — the underlying table structure is unchanged.

Deleting a schema

schema_delete(scope="project", table="notes")

The schema.yaml is moved to _backup/ and the table is unregistered from the live registry. The SQLite database file is not modified. Dropping or altering the table remains the operator's responsibility.

Atomic batch operations

schema_batch(ops=[
  { "op": "create", "table": "notes", "scope": "project", "fields": [...] },
  { "op": "data_insert", "table": "notes", "data": { "title": "first note" } }
])

All ops execute under a single SQLite SAVEPOINT. If any op fails the entire batch rolls back — YAML files are not written and the registry is not changed. All ops in one batch must target the same table.

dry_run preview

All four schema tools accept dry_run: true. In dry-run mode the tool computes and returns affect counts (rows, fields_added, fields_removed) without writing to any YAML file, SQLite table, or backup directory.

Backup retention

Backup files accumulate in <scope_dir>/_backup/. The server automatically deletes the oldest copies beyond the retention limit (default 10 pairs per table). Override the limit with MINI_APP_BACKUP_RETENTION.

Data snapshots

data_snapshot creates a standalone SQLite snapshot of one or more mounted tables without touching any YAML file or altering the schema:

data_snapshot(table="notes", scope="project")
data_snapshot()                       # snapshot all mounted tables
data_snapshot(dry_run=true)           # preview without writing

Snapshots are written to <scope_root>/_snapshots/<table>.<unix_secs>.db using the SQLite hot backup API (rusqlite::Connection::backup), so the source database remains open and writable during the operation. The retention limit (default 10) is controlled independently via MINI_APP_SNAPSHOT_RETENTION and never interacts with _backup/.

Row materialization

row_materialize exports rows from any mounted table to the local filesystem in a format your agent or toolchain can consume directly — without re-reading the database.

Selecting rows

row_materialize(table="notes", selector={"type": "ById", "id": "<uuid>"}, ...)
row_materialize(table="notes", selector={"type": "ByFilter", "filter": {"type": "eq", "field": "state", "value": "done"}}, ...)

ById fetches exactly one row by primary key. ByFilter accepts any ListFilter expression (the same eq / in / like / or / and combinators available in list).

Output formats

Destination and concat mode

# One file per row, written to /tmp/export/{id}.md
row_materialize(table="notes", format="markdown", dest="/tmp/export", concat=false)

# All rows concatenated into a single file
row_materialize(table="notes", format="json", dest="/tmp/notes.json", concat=true)

When concat=false (default), dest is treated as a directory and each row is written to {dest}/{id}.{ext}. The directory is created with create_dir_all if it does not exist.

When concat=true, dest is a file path. Raw rows are separated by \n\n, Markdown rows by ---\n, and YAML rows by ---\n. JSON uses a top-level array.

The destination path must be absolute. Relative paths are rejected immediately with MATERIALIZE_DEST_RELATIVE. No project-root sandbox is applied — any absolute path is permitted, giving agents full filesystem reach.

Field projection

row_materialize(..., fields={"type": "All"})                              # default: all schema fields
row_materialize(..., fields={"type": "List", "fields": ["title", "state"]})  # named subset

Unknown field names return MATERIALIZE_FIELD_UNKNOWN before any file is written.

Integrity: SHA-256 in every response

Every entry in files[] includes a sha256 field — a 64-character hex digest of the exact bytes written. Agents can use this for idempotency checks or to verify content after transfer without re-reading the file.

{
  "count": 2,
  "files": [
    { "path": "/tmp/export/abc.md", "bytes": 142, "sha256": "e3b0c44…", "row_id": "abc" },
    { "path": "/tmp/export/def.md", "bytes": 198, "sha256": "a87ff6…", "row_id": "def" }
  ]
}

row_id is the source row's primary key for per-row files and null for concatenated output.

Dry run

row_materialize(..., dry_run=true)

Dry-run mode runs all validation, projection, serialization, and SHA-256 computation but skips std::fs::write. The response shape is identical to a real write — path, byte count, and digest are all populated as "would-be" values.

Write mode

row_materialize(..., write_mode="Overwrite")   # default: overwrite existing files
row_materialize(..., write_mode="Error")       # fail with MATERIALIZE_DEST_INVALID if dest exists

Ignoring dump files in git

Add .mini-app/ (or your custom dump.dir) to .gitignore if you do not want dump files tracked by version control:

.mini-app/

Relation graph usage

mini-app-mcp is table-agnostic, but a common pattern is to use a single table as a relation graph layer for agents that need typed edges between nodes (e.g. persona-AI memory: sister_of, member_of_studio, mother_of).

The reference schema lives at examples/schemas/relations.schema.yaml:

Bulk insert (N edges atomically)

Use schema_batch with query ops to register N edges in one SAVEPOINT:

{
  "ops": [
    {
      "op": "query",
      "sql": "INSERT INTO rows (id, data, created_at, updated_at) VALUES (?1, ?2, ?3, ?4)",
      "params": ["<uuid>", "{\"from\":\"persona-x\",\"to\":\"a\",\"type\":\"sister_of\",\"ts\":1779330000,\"strength\":1.0}", "1779330000", "1779330000"]
    }
  ]
}

Any op failure rolls back the entire batch.

Bulk replace (edge set full replacement)

Use the dedicated replace op to swap an edge set in one atomic SAVEPOINT. The server handles UUID generation, timestamps, and JSON serialization; the caller supplies the match scope and items list only.

{
  "ops": [
    {
      "op": "replace",
      "table": "relations",
      "match": {"from": "persona-x", "type": "sister_of"},
      "items": [
        {"from": "persona-x", "to": "a", "type": "sister_of", "ts": 1779330000, "strength": 1.0},
        {"from": "persona-x", "to": "b", "type": "sister_of", "ts": 1779330000, "strength": 1.0},
        {"from": "persona-x", "to": "c", "type": "sister_of", "ts": 1779330000, "strength": 0.8}
      ]
    }
  ]
}

The replace op runs DELETE WHERE (match) + N INSERTs inside a single SAVEPOINT. Both phases roll back together on any failure. The response includes per-op affects:

{
  "committed": true,
  "ops_executed": 1,
  "affects": {"deleted": 5, "inserted": 3}
}

Semantics:

• Each key in match becomes a separate json_extract(data, '$.key') = value predicate joined by AND. Match value must be a scalar (string/number/bool/null).

• Empty match ({}) is rejected with VALIDATION_ERROR to prevent accidental full-table wipes.

• Each item in items is validated against the table's schema before any SQL runs.

• The query op (raw SQL escape hatch shown in Bulk insert) remains available for cases the replace op does not cover.

Listing edges (sub-1000 scope)

The list tool returns rows in created_at DESC order with limit capped at 1000. An optional filter argument enables server-side row filtering over schema-validated fields.

Filter variants

or and and accept Vec<ListFilter> recursively, enabling nested compositions to arbitrary depth.

Constraints: field names must be registered in the table's schema.yaml (unknown fields return a VALIDATION_ERROR). Values are type-checked against the schema field's declared type — the same typed scalar validation applied to BatchOp::Replace match values. Omitting filter returns all rows within limit/offset (full backward compatibility).

Mailbox-style OR filter example

Fetch messages addressed to "alice" or "broadcast" in a single call:

{
  "filter": {
    "type": "or",
    "filters": [
      {"type": "eq", "field": "to", "value": "alice"},
      {"type": "eq", "field": "to", "value": "broadcast"}
    ]
  }
}

For small graphs where client-side filtering is sufficient:

edges = mini_app.list(table="relations", limit=1000)
sisters_of_x = [
    e for e in edges
    if e["data"]["from"] == "persona-x" and e["data"]["type"] == "sister_of"
]

Update semantics: merge vs replace

The update tool supports two modes controlled by the optional "mode" field.

Merge mode (default)

update(table="notes", id="<uuid>", data={"state": "done"})
// or equivalently:
update(table="notes", id="<uuid>", data={"state": "done"}, mode="merge")

Merge mode follows RFC 7396 (JSON Merge Patch) shallow semantics:

• Absent fields are preserved — keys not present in data keep their stored values.

• Non-null values overwrite — a key present in data with a non-null value replaces the stored value for that key. Nested objects are replaced wholesale (no deep merge).

• null deletes an optional field — if a key's value is null and the field is required: false in the schema, the field is removed from the stored row.

• null on a required field is a Validation error — if a key's value is null and the field is required: true, the call fails with VALIDATION_ERROR before any write occurs.

• Full schema validation runs on the merged result — after merging, the complete merged object is validated against the schema. Any constraint violation (missing required field, type mismatch) returns a Validation error and the row is not updated.

Replace mode

update(table="notes", id="<uuid>", data={...}, mode="replace")

Replace mode performs a full replacement: data is validated against the schema and then written as-is, completely overwriting the stored row. This is identical to the behaviour of update before version 0.9. Callers that relied on the old default and send partial data objects should switch to mode="replace" to restore the original behaviour.

Choosing a mode

Field projection

list, get, and alias_run all accept an optional fields argument that limits which schema fields appear in the returned data object. id, created_at, and updated_at are always included in the response envelope regardless of the selector.

Selector shapes

{"mode": "all"}                              // default: return all schema fields
{"mode": "list", "fields": ["title", "state"]}   // return only the named fields

Omitting fields is fully backward-compatible — existing callers receive complete rows without any change.

Usage examples

list(table="issues", fields={"mode": "list", "fields": ["title", "state"]})
get(table="issues", id="<uuid>", fields={"mode": "list", "fields": ["title"]})
alias_run(table="issues", name="recent_open", fields={"mode": "list", "fields": ["title", "state"]})

Validation

Unknown field names are rejected with VALIDATION_ERROR before any query executes. Field name validation consults schema.yaml's canonical field definitions — not the actual keys present in stored rows — so projection errors are caught reliably even when the field is simply absent from a particular row.

Query aliases

Query aliases let you save a ListFilter expression (or a MiniJinja filter template) under a short name and replay it — with optional per-call limit / offset overrides — without repeating the filter JSON every time.

Creating a static alias

alias_create(
  table="notes",
  name="recent_open",
  filter={"type": "eq", "field": "state", "value": "open"},
  default_limit=20,
  description="Open notes, newest first"
)

The alias is stored in the table's own SQLite database under _aliases. Alias names are unique per table; calling alias_create again with the same name returns ALIAS_ALREADY_EXISTS.

Creating a parameterized alias (filter template)

Parameterized aliases use a MiniJinja template string instead of a fixed filter. At run time the caller supplies parameter values that are rendered into the template before execution.

alias_create(
  table="issues",
  name="by_state",
  filter_template='{"type": "eq", "field": "state", "value": "{{ state }}"}',
  params_schema=["state"],
  default_limit=50,
  description="Filter issues by state (parameterized)"
)

filter_template and filter are mutually exclusive — exactly one must be supplied. params_schema is an optional array of parameter names; supply it to document which keys alias_run expects in its params object.

Running an alias

Static alias (no parameters):

alias_run(table="notes", name="recent_open")
# → uses stored default_limit=20

alias_run(table="notes", name="recent_open", limit=5)
# → runtime limit overrides the stored default_limit

alias_run(table="notes", name="recent_open", limit=10, offset=20)
# → pagination with runtime overrides

Parameterized alias (with params):

alias_run(table="issues", name="by_state", params={"state": "open"})
# → renders template → {"type": "eq", "field": "state", "value": "open"}
# → validates as ListFilter → executes Store::list

alias_run(table="issues", name="by_state", params={"state": "open"}, limit=10)
# → runtime limit override with params injection

alias_run resolves the stored filter or renders the template and passes the result to Store::list. If limit is supplied at call time it overrides default_limit; if omitted, default_limit from the alias is used. offset is always a runtime-only argument (not stored).

Error cases for parameterized aliases:

Listing and deleting aliases

alias_list(table="notes")
# → [{"name": "recent_open", "filter": {...}, "default_limit": 20, "description": "...", "params_schema": null}]

alias_list(table="issues")
# → [{"name": "by_state", "filter": null, "default_limit": 50, "description": "...", "params_schema": ["state"]}]

alias_delete(table="notes", name="recent_open")

Alias isolation

Each table's _aliases storage is physically separate: aliases for notes live in notes.db, aliases for issues live in issues.db. There is no global alias namespace and no way for an alias operation to read or write aliases belonging to a different table.

Aggregation

query_aggregate runs COUNT / SUM / AVG / MIN / MAX / GROUP BY over one or many tables in a single tool call. Multi-table sources are joined with literal UNION ALL via SQLite ATTACH DATABASE, eliminating the N+1 round trips that result when callers fetch per-table rows and reduce client-side. The tool is read-only and idempotent.

Shape

{
  "sources":   { "kind": "single", "value": "<table>" },          // or "multi" + ["t1","t2",...]
  "filter":    null,                                              // optional, same ListFilter shape as `list`
  "aggregator": { "kind": "count" }                               // or sum / avg / min / max / group_by
}

Result is externally-tagged so callers can dispatch on kind:

{ "kind": "count",  "value": 42 }
{ "kind": "value",  "value": 3.14 }
{ "kind": "groups", "value": [ { "key": "a", "count": 12, "value": 7.0 }, ... ] }

Single-source aggregation

// COUNT all rows in `emo`.
{ "name": "query_aggregate", "arguments": {
    "sources":    { "kind": "single", "value": "emo" },
    "aggregator": { "kind": "count" }
}}

// SUM the `amount` field.
{ "name": "query_aggregate", "arguments": {
    "sources":    { "kind": "single", "value": "ledger" },
    "aggregator": { "kind": "sum", "field": "amount" }
}}

Multi-table aggregation

Multi mounts every backing .db file via SQLite ATTACH DATABASE (limit 10) and composes a UNION ALL between per-table sub-queries before applying the outer aggregate.

// COUNT rows across two same-shape tables.
{ "name": "query_aggregate", "arguments": {
    "sources":    { "kind": "multi", "value": ["events_a", "events_b"] },
    "aggregator": { "kind": "count" }
}}

// GROUP BY tag with HAVING and an inner SUM(amount).
{ "name": "query_aggregate", "arguments": {
    "sources":    { "kind": "multi", "value": ["events_a", "events_b"] },
    "aggregator": {
        "kind":     "group_by",
        "by_field": "tag",
        "having":   { "type": "eq", "field": "tag", "value": "a" },
        "inner":    { "kind": "sum", "field": "amount" }
    }
}}

Caller is responsible for ensuring all Multi sources share a compatible schema; per-table field validation is a Phase 2 carry. Aggregator-specific errors (empty sources, ATTACH-limit exceeded, nested GroupBy, non-UTF-8 db path) return AGGREGATOR_ERROR (data.code); unknown table names return TABLE_NOT_FOUND; field / identifier rejections return VALIDATION_ERROR.

Global Alias (Phase 2)

Phase 2 unifies aliases across tables in a single global storage (<project_dir>/_global.db + <user_dir>/_global.db, with lookup precedence Project → User). The alias_create MCP tool accepts new optional sources and aggregator arguments so one alias can span multiple tables and stored aggregate logic:

// Multi-table alias with COUNT aggregator (replays via execute_aggregate).
{ "name": "alias_create", "arguments": {
    "name":       "combined_events_count",
    "sources":    { "kind": "multi", "value": ["events_a", "events_b"] },
    "aggregator": { "kind": "count" },
    "filter":     { "type": "like", "field": "tag", "pattern": "%" }
}}

// Pattern source — resolved against the live registry's table list at
// alias_run time. Matches all currently-mounted `events_*` tables.
{ "name": "alias_create", "arguments": {
    "name":       "all_events_count",
    "sources":    { "kind": "pattern", "value": "events_*" },
    "aggregator": { "kind": "count" },
    "filter":     { "type": "like", "field": "tag", "pattern": "%" }
}}

// alias_run dispatches to execute_aggregate transparently.
{ "name": "alias_run", "arguments": { "name": "all_events_count" } }
// → { "kind": "count", "value": <sum across all events_* tables> }

The legacy table argument is still accepted and is silently normalised to sources = { "kind": "single", "value": "<table>" }; specifying both table and sources is an error.

Migration: existing per-table _aliases rows are copied into the project-scope _global_aliases automatically on every TableRegistry::mount_from_dirs call. The migration is lossless (all five legacy fields preserved + sources = Single(<table>) filled in) and idempotent (INSERT OR IGNORE skips collisions), so it is safe to run on every server restart. Per-table _aliases tables are not deleted, preserving a rollback path until a future minor release.

Phase 2 limitations

• Multi / Pattern source aliases require an aggregator; running them without one returns a structured error (the per-table list path cannot serve cross-table rows).

• Pattern glob supports * only (one or more); ? / [] are reserved for a future revision.

• Pattern matching that resolves to zero tables returns AGGREGATOR_ERROR at alias_run time (early surface — does not defer to ATTACH DATABASE).

• Filter validation uses the schema of the first source table only; cross-table schema divergence is the caller's responsibility (same constraint as query_aggregate).

• Legacy single-table mode (no MINI_APP_USER_DIR / MINI_APP_PROJECT_DIR) falls back to the per-table Store::alias_* path; Pattern sources are rejected in this mode.

Row history auto log

Every create, update, and delete call automatically writes a full-JSON snapshot of the affected row into a _row_history table inside the same SQLite database. The snapshot is written atomically inside the same transaction as the data change — either both the data DML and the history record commit, or neither does.

What gets recorded

Every history record also carries a monotonically increasing version counter (scoped per table + row id) and a recorded_at Unix seconds timestamp.

Restoring a row with row_restore

row_restore fetches the latest snapshot at or before a given Unix timestamp and writes it back as the live row. If the row was deleted it is re-inserted with the same id; if the row still exists it is replaced with the snapshot data.

{
  "name": "row_restore",
  "arguments": {
    "table": "issues",
    "id": "550e8400-e29b-41d4-a716-446655440000",
    "at_unix_secs": 1750000000
  }
}

The response is the same shape as get — a full row object with id, data, created_at, and updated_at.

Purge SOP

History records are not purged on every update. Purge is a separate operation performed by calling purge_old_history in application code with the desired retention policy:

• Retention window: 30 days (records older than 30 days are deleted)

• Max versions per row: 100 (oldest versions beyond 100 are deleted per row)

Purge is intentionally not wired to any automatic hook so that history accumulation does not add latency to normal create / update / delete calls.

Partial edit tools

Three tools let agents view and surgically edit individual string fields without fetching or replacing the entire row. Non-string fields (arrays, objects, numbers, booleans) are rejected with TYPE_ERROR.

content_view

Return the contents of a string field with line numbers (cat -n style). Pass view_range to limit output to a contiguous slice (1-indexed, inclusive on both ends).

// View lines 12–30 of a body field
content_view(table="issue", id="26a8a7f9", field="body", view_range=[12, 30])
// → {"content": "    12  first visible line\n    13  ...\n"}

// Omit view_range to get the full field
content_view(table="issue", id="26a8a7f9", field="body")

content_replace

Replace a unique occurrence of old_str with new_str inside a string field.

// Unique replacement — returns the number of substitutions
content_replace(table="issue", id="26a8a7f9", field="body",
                old_str="foo", new_str="bar")
// → {"matches": 1}

// Scope the search to a line range before replacing
content_replace(table="issue", id="26a8a7f9", field="body",
                old_str="foo", new_str="bar", view_range=[1, 10])

// Replace every occurrence in one call
content_replace(table="issue", id="26a8a7f9", field="body",
                old_str="foo", new_str="bar", replace_all=true)
// → {"matches": 4}

If old_str does not appear the tool returns STRING_NOT_FOUND. If it appears more than once and replace_all is not set, the tool returns AMBIGUOUS with a candidates list.

content_insert

Insert one or more lines into a string field at a specific position.

// Prepend (line=0 inserts before the first line)
content_insert(table="issue", id="26a8a7f9", field="body",
               line=0, content="Prepended line")
// → {"line": 0, "inserted_lines": 1}

// Insert after line N (1-indexed)
content_insert(table="issue", id="26a8a7f9", field="body",
               line=5, content="Inserted after line 5")
// → {"line": 5, "inserted_lines": 1}

line values greater than total_lines + 1 return OUT_OF_RANGE. All three tools record changes through the normal row_history hook, so every edit is fully auditable and restorable via row_restore.

Storage notes

SQLite databases are opened in WAL journal mode for safe concurrent access during reload. Sidecar files <db>.db-wal and <db>.db-shm are created next to each .db file — these are managed by SQLite and should not be deleted manually.

MCP Registry

mini-app-mcp is listed on the MCP Registry. The OCI image is published to GitHub Container Registry on every tagged release.

Docker (no Rust toolchain required)

{
  "mcpServers": {
    "mini-app": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/path/to/data:/data",
        "ghcr.io/ynishi/mini-app-mcp:latest"
      ]
    }
  }
}

License

MIT OR Apache-2.0