Litescope is an MCP-first operations tool for Cloudflare D1 and SQLite databases, giving AI agents direct access for inspection, migration, optimization, and fleet management.
Read-Only Capabilities
Health Inspection: Check for corruption, WAL bloat, fragmentation, and reachability — returns a severity verdict (ok/warning/critical)
Schema Inspection: Retrieve tables, columns (name, type, not-null, primary key), and indexes from any SQLite or D1 database
Read-Only Queries: Run SELECT statements or read-only PRAGMAs with token budgeting (row caps, column projection, truncation reporting)
Database Diff: Compare two databases (any combination of local, D1, Turso) for schema and row-count differences
Migration Planning: Generate migration SQL with blast-radius analysis (safe/risky/destructive classification and lock-duration estimates)
Index Advisor: Analyze for missing/redundant indexes and full table scans — returns runnable CREATE/DROP INDEX suggestions
Backup Verification: Run integrity checks on a backup and optionally compare schema/row counts against a reference
Lock Diagnosis: Diagnose SQLITE_BUSY issues — inspects journal mode, busy_timeout, WAL bloat, and live lock state
Fleet Schema Fingerprinting: Cluster a fleet of databases by schema to detect drift across instances
Fleet Health Triage: Run parallel health checks across an entire fleet, sorted worst-first
List D1 Databases: List all Cloudflare D1 databases with UUIDs, names, table counts, and DSNs
List Snapshots: List available point-in-time backups for a local SQLite database
Write Capabilities (require --allow-writes)
Mutating SQL: Execute write statements with automatic dry-run and pre-application snapshots
Apply Migrations: Apply migration SQL with dry-run and backup support
Self-Driving Optimization (Autopilot): Automatically apply ANALYZE, PRAGMA optimize, missing FK indexes, VACUUM, and redundant-index cleanup — with a pre-run snapshot for rollback
Snapshot & Restore: Take and restore point-in-time snapshots of local databases
D1 Time Travel: Rewind a Cloudflare D1 database to a specific point in time
D1 Sync: Pull D1 to local SQLite, create, or delete D1 databases
Additional CLI Tools
Local SQLite doctor, schema linting, ERD/schema visualization, SQL dump, CSV/Excel import-export, schema drift monitoring, and a local web dashboard
CI integration via GitHub Action for linting, diffing, and PR commenting
Staged fleet migrations with canary support
Provides tools for managing Cloudflare D1 databases, including listing, querying, migrating, rewinding via Time Travel, pulling/pushing, and health checks.
Enables local SQLite database operations such as querying, schema inspection, diffing, migrations, snapshots, restore, autopilot optimization, lock diagnosis, and monitoring.
Supports diff operations between local SQLite databases and Turso remote databases, extending Litescope's data management to Turso.
Let AI agents touch production SQLite — safely. Diagnose before the write, rewind after it. For Cloudflare D1, Turso, and local files.
Ask Claude to change your D1 database — and undo it if it's wrong. Every write is dry-run by default with the exact blast radius, and one call away from revert.
Free and open source (AGPL-3.0) — every command, every fleet operation, self-hostable, no license key. The only paid thing is the hosted dashboard we run for you.
"It's just a file — what's there to operate?"
That was true when SQLite was a local dev toy. It isn't anymore. D1, Turso, LiteFS, and Litestream put SQLite in production at fleet scale — thousands of databases, one per tenant. But the tooling never caught up:
sqlite3 assumes one database. Production assumes thousands. There's no
pg_stat, no APM, no standard way to see from the outside why a database is
locked, why WAL is bloating, or which tenant went silent.
Embedded means unobservable. The very thing that makes SQLite great — no server — is what leaves you blind in production.
Agents now write to your data. No SQLite tool was built assuming an AI would run the migration. Litescope was.
Postgres has a mature tool for every problem. SQLite has none — so Litescope is one binary for the whole job: fleet observability, file-level superpowers (bisect / rewind / salvage), lock & WAL diagnostics, and a safe interface for agents. Things a generic DB client structurally can't do.
Related MCP server: mcp-server-sqlite
Claude Code — one line (read-only):
claude mcp add litescope -- litescope mcpWith writes + D1 (Litescope still dry-runs every write and captures a rewind point before applying):
claude mcp add litescope \
-e CLOUDFLARE_API_TOKEN=your-token \
-e CLOUDFLARE_ACCOUNT_ID=your-account-id \
-- litescope mcp --allow-writesCursor — one-click:
➕ Add litescope to Cursor
(installs read-only; add --allow-writes and the Cloudflare env vars in
mcp.json to enable writes).
Claude Desktop / Windsurf / any MCP client — add to the config file directly:
{
"mcpServers": {
"litescope": {
"command": "litescope",
"args": ["mcp", "--allow-writes"],
"env": {
"CLOUDFLARE_API_TOKEN": "your-token",
"CLOUDFLARE_ACCOUNT_ID": "your-account-id"
}
}
}
}Also listed in the MCP Registry
as io.github.croc100/litescope.
Then ask Claude things like:
"List my D1 databases"
"Show me the schema of the users table in d1://abc-123"
"Delete every inactive user from d1://prod-db-id" ← dry-run first: shows the
exact blast radius before
you approve the write
"Undo that — put it back the way it was" ← one-call revert via the
rewind_token from the write
"The deploy at 2pm broke something — rewind prod to 1:45pm"
"Diff my local dev.db against d1://prod-db-id and show me the migration SQL"Tool | What it does |
| List all D1 databases in the account (UUID, name, DSN) |
| Run a SELECT on any D1 database or local SQLite file |
| Inspect tables, columns, indexes |
| Check for corruption, WAL bloat, fragmentation |
| Schema and row-count diff between any two sources |
| Generate migration SQL + blast-radius analysis |
| Generate migration SQL only (no blast-radius) |
| Performance analysis: missing indexes, full table scans |
| Verify a backup against a reference database |
| Cluster a fleet by schema fingerprint |
| Triage faults across a whole fleet |
| Diagnose |
| List point-in-time snapshots for a local database |
litescope_query enforces token budgeting — max_rows cap + columns
projection + truncation reporting — so a large table never blows the agent's
context window.
--allow-writes)Tool | What it does |
| Mutating SQL — dry-run by default with exact rows affected and blast-radius diff (D1 dry-runs measured on a pulled copy); on apply, captures an undo point first (local: snapshot, D1: Time Travel bookmark) and returns it as a |
| Revert a write in one call using its |
| Apply a migration — same reversible contract as |
| Self-driving optimization (ANALYZE, indexes, VACUUM) — dry-run by default |
| Take a point-in-time backup of a local database |
| Restore a local database from a snapshot |
| Restore a D1 database to a point in time (Time Travel) |
| Download a D1 database to a local SQLite file |
| Create a new D1 database |
| Delete a D1 database (irreversible) |
Write tools are off unless you start the server with --allow-writes, every
write is dry-run by default, and no write commits without an auto-captured undo
point. See the security model for the full boundary — what each
layer protects against, and what it doesn't.
Beyond tools, the MCP server exposes prompts — canned workflows like
diagnose_locked_database, review_migration, safe_optimize, and
health_checkup that chain the tools above into a safe plan — and
resources: a database's schema, data dictionary, live health, and live lock
diagnosis — readable by the agent without spending a tool call, and
subscribable for push updates whenever the underlying file changes. Bind one
with litescope mcp ./app.db, or address any source via
litescope://schema/{source}, litescope://dictionary/{source},
litescope://health/{source}, and litescope://locks/{source}.
The server implements MCP 2025-06-18: tool annotations (read-only /
destructive hints), structured output (structuredContent + outputSchema),
argument completion, resource-change subscriptions, and server logging.
By default litescope mcp speaks stdio. For a hosted, remote, or multi-client
setup, serve over the Streamable HTTP transport instead:
litescope mcp --http :7577 --http-token "$LITESCOPE_MCP_TOKEN"POST a JSON-RPC message to the endpoint (/mcp by default), or open a GET SSE
stream for server notifications; each client gets its own session via the
Mcp-Session-Id header.
Before exposing it publicly, lock it down: --http-token (or the
LITESCOPE_MCP_TOKEN env var) requires Authorization: Bearer <token> on every
request, and --http-origin allowlists browser Origins (localhost is always
allowed) for DNS-rebinding protection. Without a token the endpoint is open and
the server prints a warning.
# Restore to a previous point in time
litescope rewind d1://DB_ID --to "2h ago"
litescope rewind d1://DB_ID --to "yesterday"
litescope rewind d1://DB_ID --to "2024-01-15T10:30:00Z"
# List available restore points (30-day window + migration timestamps)
litescope rewind list d1://DB_ID# Download D1 → local (for inspection, backup, or diffing)
litescope d1 pull d1://DB_ID ./snapshot.db
# Upload local → D1 (seed a fresh database or restore from snapshot)
litescope d1 push ./seed.db d1://DB_ID
litescope d1 push ./seed.db d1://DB_ID --drop-existing# Diff local dev schema against live D1 — generate migration SQL
litescope migrate local.db d1://DB_ID
# Apply a migration directly to D1
litescope migrate apply d1://DB_ID migration.sqlBinary-search D1 Time Travel to pinpoint the exact snapshot where a query started returning wrong results:
litescope bisect d1://DB_ID \
--good "3d ago" \
--bad now \
--check "SELECT COUNT(*) FROM orders WHERE status = 'paid'" \
--expect "gt:0"Checks gt:0 (greater-than), lt:N, eq:N, or a literal value. Narrows
to the snapshot window where the condition first failed, then lets you
inspect or rewind.
doctor — one-shot checkuplitescope doctor app.db
litescope doctor app.db --deep # exhaustive integrity_check
litescope doctor app.db --format html -o report.htmlCombines integrity check, WAL/fragmentation health, index advisor, and schema lint in one command. Exits 1 when attention is needed — use it as a CI quality gate.
snapshot / restore — point-in-time backupslitescope snapshot app.db # consistent VACUUM INTO copy
litescope snapshot app.db --label before-migration
litescope snapshot app.db --keep 7 # retain only the 7 newest
litescope snapshot list app.db
litescope restore app.db # restore the newest snapshot
litescope restore app.db --from <snapshot.db>Snapshots live in a sibling .litescope-snapshots/ directory. Restore is
integrity-checked and takes a pre-restore safety snapshot first — the same
"did you back up?" safety net that D1 gets from Time Travel, for local and Turso.
autopilot — self-driving optimizationlitescope autopilot app.db # dry-run: show the plan
litescope autopilot app.db --apply # apply the safe actions
litescope autopilot app.db --apply --aggressive
litescope autopilot --fleet litescope.fleet.yaml --applyRuns ANALYZE + PRAGMA optimize, adds missing foreign-key indexes, and
(with --aggressive) VACUUMs and drops redundant indexes — each explained in
plain language. Dry-run by default; every real change is preceded by an
automatic snapshot.
locks — diagnose "database is locked"litescope locks app.db # static config diagnosis
litescope locks app.db --live # is a writer holding the lock now?
litescope locks app.db --watch # stream lock-state changes
litescope locks app.db --timeline # recorded contention history
litescope locks app.db --timeline --since 24hInspects journal mode, busy_timeout, locking mode, and WAL bloat, and
prescribes the exact PRAGMA/DSN fix. --live identifies the process holding
the lock right now. --watch records every observation to a local history
store; --timeline then aggregates it into a per-database contention view —
when the database was jammed, for how long, which processes held it, wait-time
percentiles, and whether the WAL checkpoint kept up.
diff — schema and data difflitescope diff old.db new.db
litescope diff old.db new.db --format json
litescope diff local.db d1://DB_ID # local vs live D1
litescope diff local.db turso://TOKEN@ORG/prodmigrate — generate and apply migrationslitescope migrate before.db after.db --output migration.sql
litescope migrate apply prod.db migration.sql --dry-run
litescope migrate apply prod.db migration.sql --verify after.dbmigrate apply safety sequence: pre-flight integrity check → VACUUM INTO backup → single transaction → FK verification → auto-rollback on failure.
lint — schema anti-patternslitescope lint app.db
litescope lint app.db --strict # exit 1 on info findings tooRules: no-primary-key, untyped-column, not-strict, autoincrement-overhead, non-integer-pk.
schema — inspect schema + ERDlitescope schema app.db
litescope schema app.db --erd # Mermaid ER diagramdump — portable SQL exportlitescope dump app.db -o backup.sql
litescope dump app.db --schema-only
litescope dump app.db --table usersimport / export — spreadsheets and SQLitelitescope import sales.csv # → sales.db, table "sales"
litescope import budget.xlsx # first sheet → budget.db
litescope export shop.db --table orders -o orders.xlsx
litescope export shop.db --query "SELECT city, COUNT(*) FROM users GROUP BY city"Formats: CSV, TSV, JSON, Excel (.xlsx). No external dependencies.
monitor — schema drift detectionlitescope monitor snapshot prod.db --output baseline.json
litescope monitor check prod.db --baseline baseline.json # exits 1 on drift
litescope monitor watch prod.db --baseline baseline.json --interval 1h --webhook https://hooks.slack.com/...serve — local web dashboardlitescope serve # opens http://127.0.0.1:7575
litescope serve --config litescope.fleet.yamlFleet topology map, health triage, schema fingerprinting, interactive ERD, a paginated data browser with a visual query builder, drag-drop import, and a visual diff panel — pick any two databases to review schema and row-count changes before applying. Entirely local, no account required.
Manage hundreds of databases at once. Built for multi-tenant apps on Turso and D1.
# Discover all databases
litescope fleet discover turso --org my-org --token $TURSO_API_TOKEN
litescope fleet discover d1 --account $CF_ACCOUNT_ID --token $CF_API_TOKEN
# Triage the whole fleet
litescope fleet health
litescope fleet locks # roll up "database is locked" contention, worst-first
litescope fleet fingerprint # cluster by schema — find drift before it bites
# Stage a migration across the fleet
litescope fleet migrate migration.sql --dry-run
litescope fleet migrate migration.sql --canary 5
litescope fleet migrate migration.sqlRun Litescope on every pull request — lint the schema, diff against the base branch, and comment the blast radius so a risky migration can't merge unreviewed.
- uses: croc100/Litescope@v1
with:
args: "lint app.db --strict"
comment: "true" # post the result as a sticky PR commentargs is any Litescope command; the job exits non-zero when Litescope flags
something, failing the check. See
examples/github-actions/migration-ci.yml
for a full lint + diff workflow.
Input | Default | Description |
| — | Litescope command to run (required) |
|
| Release tag to install, or |
|
| Post output as a sticky PR comment |
|
| Directory to run in |
Homebrew
brew install croc100/tap/litescopenpm / npx — for JS and wrangler users, no separate install:
npx litescope doctor app.db
npm install -g litescopeGo install
go install github.com/croc100/litescope/cmd/litescope@latestBinary download
macOS, Linux, Windows — Releases.
DSN | Provider |
| Local SQLite file |
| Cloudflare D1 (env: |
| Cloudflare D1 (explicit credentials) |
|
The tool is free. We only charge to run the dashboard for you.
Free (OSS, AGPL-3.0) | Cloud (paid) | |
Every CLI command, MCP server, fleet ops | ✅ | ✅ |
| ✅ | ✅ |
Self-hosted dashboard on your own infra | ✅ | ✅ |
Hosted dashboard we run & maintain | — | ✅ |
Managed metadata ingestion, retention, alerting | — | ✅ |
Org auth, teams, SSO | — | ✅ |
Support SLA | — | ✅ |
The line is simple: the software and every feature is free and self-hostable forever. You pay only if you want us to host and operate the dashboard so you don't have to. No feature is locked behind a license key.
See litescope-site.pages.dev/pricing for the hosted plans.
Litescope is AGPL-3.0. Free to use, modify, and self-host. If you offer it as a network service the AGPL requires you to share your modifications. A commercial license (AGPL exception + support SLA) is available for organizations — see COMMERCIAL.md or email dl_litescope@crode.net.
Analyze a local SQLite database for performance problems and recommend fixes: foreign keys with no index, redundant indexes, and full table scans for any supplied queries. Returns findings with runnable CREATE/DROP INDEX suggestions. Read-only — recommends, never alters the schema. (Local files only.)
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Local SQLite file path (advise requires direct file access) | |
| queries | No | Optional SQL queries to check for full table scans |
| Name | Required | Description |
|---|---|---|
| path | No | The analyzed database. |
| findings | Yes | Issues with rule, severity, and a runnable suggestion. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description explicitly states 'Read-only — recommends, never alters the schema' and 'Local files only,' which adds valuable context beyond the annotations (readOnlyHint, destructiveHint). This clarifies the read-only nature and file access constraint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose ('Analyze...'), and each sentence adds value without redundancy. It is concise and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (implied), the description adequately explains the inputs and behavior. It lists specific diagnostics (foreign keys, redundant indexes, table scans) but does not mention potential limitations or error cases, which is acceptable for a read-only tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers both parameters with descriptions (source path, optional queries). The description adds no additional meaning beyond the schema, such as formats or constraints. With 100% schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with 'Analyze a local SQLite database for performance problems and recommend fixes' which clearly states the action and resource. It lists specific types of problems (foreign keys, redundant indexes, full table scans) and distinguishes from sibling tools like litescope_query (executes queries) and litescope_schema (shows schema).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the tool's purpose and output (recommendations with runnable SQL), making it clear when to use it. However, it does not explicitly state when not to use it or name alternative tools, though the sibling context helps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Verify a SQLite backup. Runs a PRAGMA integrity check; if 'against' is given, also compares schema and row counts to a reference database. Returns a JSON report. Read-only. (Local files only.)
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | Also compare row counts per table | |
| source | Yes | Local path to the backup database to verify | |
| against | No | Optional local reference database to compare schema against |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: runs PRAGMA integrity check, compares schema/row counts when 'against' is given, returns JSON report, and notes read-only and local-file constraints. These details add value beyond the annotations, which already indicate read-only and idempotent hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (two sentences) yet packs all essential information. It front-loads the primary action ('Verify a SQLite backup') and efficiently communicates the conditional behavior and constraints.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three well-documented parameters in the schema, rich annotations, and no output schema, the description is complete. It explains the core logic (integrity check) and the optional comparison, which suffices for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the description adds extra semantics by explaining that 'against' triggers schema and row count comparison, beyond the schema's 'Optional local reference database to compare schema against'. This clarifies the tool's behavior beyond the parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: verifying a SQLite backup via PRAGMA integrity check, with optional comparison against a reference database. It distinguishes itself from sibling tools like litescope_diff by focusing on backup verification specifically.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implicitly guides usage by stating 'Read-only. (Local files only.)' and describing the optional 'against' parameter. While it doesn't explicitly list alternatives, the context of backup verification is clear enough for an agent to decide when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all Cloudflare D1 databases in the account. Returns each database's UUID, name, creation date, table count, and the DSN to use with other litescope tools. Requires CLOUDFLARE_API_TOKEN and CLOUDFLARE_ACCOUNT_ID environment variables. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| count | Yes | Number of databases. |
| databases | Yes | Each D1 database with uuid, name, table count, and dsn. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and destructiveHint false, so the description adds value by specifying the exact return fields (UUID, name, creation date, table count, DSN) and the required environment variables. This is helpful context beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. The first sentence states the purpose immediately, and the second adds return values and prerequisites. It is optimally sized for an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and an output schema exists, the description covers all necessary information: purpose, return values, and required environment variables. It is fully sufficient for an AI agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description cannot add parameter details. The baseline for zero parameters is 4, and the description does not need to elaborate further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the action ('List') and the resource ('all Cloudflare D1 databases in the account'). It is specific and distinguishes it from sibling tools, which include other litescope commands but no other list tool for D1 databases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates when to use (to list databases) and mentions required environment variables, but it does not provide explicit guidance on when not to use or mention alternatives among siblings. The context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Compare two SQLite or D1 databases and return their schema and row-count differences as JSON. Works across any combination of local files, D1, and Turso — e.g. diff a local migration target against a live D1 database. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| new | Yes | Changed ('after') source — Database source: a local file path (./app.db), a Cloudflare D1 DSN (d1://DB_ID when CLOUDFLARE_API_TOKEN+CLOUDFLARE_ACCOUNT_ID are set, or d1://TOKEN@ACCOUNT_ID/DB_ID), or a Turso DSN (turso://TOKEN@ORG/DB). | |
| old | Yes | Baseline ('before') source — Database source: a local file path (./app.db), a Cloudflare D1 DSN (d1://DB_ID when CLOUDFLARE_API_TOKEN+CLOUDFLARE_ACCOUNT_ID are set, or d1://TOKEN@ACCOUNT_ID/DB_ID), or a Turso DSN (turso://TOKEN@ORG/DB). |
| Name | Required | Description |
|---|---|---|
| summary | Yes | Counts of tables added/removed/modified. |
| data_changes | No | Per-table row-count differences (present when data differs). |
| schema_changes | Yes | Per-table schema differences. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is clear. The description adds value by specifying the output format (JSON) and supported database sources, which are not covered by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: first sentence states purpose and output, second sentence expands on sources and gives an example. No wasted words; each sentence provides essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (covering return values), detailed parameter descriptions (100% coverage), and annotations that cover safety and idempotency, the description is complete. It adequately informs an AI agent about the tool's functionality and usage scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters have detailed descriptions already. The tool description adds context by defining 'old' as baseline and 'new' as changed, and gives examples of sources and authentication notes, which enhances understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool compares two databases and returns schema and row-count differences as JSON, which is a specific verb+resource. It distinguishes from sibling litescope_migrate_diff by specifying the output is schema and row-count differences, not just migration-related.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (e.g., diff a local migration target against a live D1 database) and lists supported database types (local files, D1, Turso). However, it does not explicitly state when not to use or mention alternatives among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cluster a fleet of SQLite databases by schema and report how many distinct schemas are running, with each cluster's drift from the canonical (largest) one. Reads a fleet config file (litescope.fleet.yaml). Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Only include databases with this tag | |
| config | No | Path to the fleet config (default: litescope.fleet.yaml) |
| Name | Required | Description |
|---|---|---|
| total | No | Databases successfully fingerprinted. |
| clusters | Yes | Schema clusters, canonical first, with drift from canonical. |
| unreachable | No | Databases that could not be read. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds behavioral context: reads a fleet config file (litescope.fleet.yaml), outputs clusters with drift from canonical. Does not contradict annotations. Good additional context beyond structured fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise, front-loaded sentences. Each sentence adds value: first explains core function, second mentions config file and read-only nature. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has output schema (so return format is covered), schema coverage 100%, annotations complete. Description provides all necessary context: what it does, what it reads, and its read-only nature. Fully adequate for agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters ('tag', 'config') are already described in the schema. Description does not add extra meaning or usage details for parameters, meeting baseline expectation for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool clusters SQLite databases by schema and reports distinct schemas and drift from canonical. Verb 'cluster and report' is specific, resource is 'fleet of SQLite databases', outcome is well-defined. Distinguishes from siblings like litescope_diff or litescope_schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description indicates it reads a fleet config file and is read-only, implying usage for fleet-level schema analysis. No explicit guidance on when to use versus alternatives or when not to use. Adequate but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Triage operational faults across a whole fleet of SQLite databases in parallel — corruption, WAL bloat, fragmentation, reachability — sorted worst-first. Reads a fleet config file (litescope.fleet.yaml). Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Only include databases with this tag | |
| deep | No | Use the exhaustive integrity_check instead of quick_check | |
| config | No | Path to the fleet config (default: litescope.fleet.yaml) |
| Name | Required | Description |
|---|---|---|
| results | Yes | Per-database health reports, worst-first. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context: operations are performed in parallel across the fleet, results are sorted worst-first, and it relies on a fleet config file. The explicit 'Read-only' statement reinforces the annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences covering purpose, scope, specifics, and read-only nature. Every word adds value; no filler or repetition. It is front-loaded with the primary action and key differentiators.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (fleet, parallel, multiple fault types) and the presence of a full output schema, the description provides sufficient context. It explains the parallel triage, fault types, sorting, and config file dependency. The only minor gap is no mention of the output format, but the output schema covers that. Overall, complete for the task.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (all three parameters have descriptions in the input schema). The description does not add additional semantics beyond what the schema provides (e.g., tag, deep, config). According to guidelines, when coverage is high, baseline is 3, and no extra information is provided here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: triaging operational faults across a fleet of SQLite databases in parallel, listing specific fault types (corruption, WAL bloat, fragmentation, reachability) and sorting worst-first. This verb+resource combination ('triage operational faults across a fleet') distinguishes it from sibling tools like litescope_health, which likely targets single databases.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for fleet-wide health triage and explicitly marks the tool as read-only, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., litescope_health for single databases, litescope_check for specific checks). The usage context is implied but not fully delineated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Diff two SQLite or D1 databases and return the migration SQL that would bring the 'old' source up to the 'new' schema — without applying it or computing blast-radius. Useful when you only need the SQL to review or pass to litescope_migrate_apply. For a full blast-radius analysis use litescope_migrate_plan. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| new | Yes | Target ('after') source with the desired schema — Database source: a local file path (./app.db), a Cloudflare D1 DSN (d1://DB_ID when CLOUDFLARE_API_TOKEN+CLOUDFLARE_ACCOUNT_ID are set, or d1://TOKEN@ACCOUNT_ID/DB_ID), or a Turso DSN (turso://TOKEN@ORG/DB). | |
| old | Yes | Current ('before') source — Database source: a local file path (./app.db), a Cloudflare D1 DSN (d1://DB_ID when CLOUDFLARE_API_TOKEN+CLOUDFLARE_ACCOUNT_ID are set, or d1://TOKEN@ACCOUNT_ID/DB_ID), or a Turso DSN (turso://TOKEN@ORG/DB). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds non-obvious behaviors: it does not apply the migration and does not compute blast radius. It also explicitly says 'Read-only'. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus 'Read-only' suffix, no fluff. First sentence states purpose, second sentence gives use case and alternative. Extremely efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with two parameters and comprehensive schema descriptions, the description is mostly complete. It doesn't describe the output format (SQL text), but given no output schema, this is acceptable. The sibling references add context. Minor gap: environment variable dependencies mentioned only in schema, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description does not add additional parameter meaning beyond what the schema already provides, such as the detailed DSN formats. It is adequate but not extra.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool diffs two databases and returns migration SQL without applying or computing blast radius. It distinguishes from siblings like litescope_migrate_plan and litescope_migrate_apply, using specific verb 'diff' and resource 'SQLite or D1 databases'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit when to use: 'when you only need the SQL to review or pass to litescope_migrate_apply'. Explicit alternative: 'For a full blast-radius analysis use litescope_migrate_plan'. This provides clear guidance for agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a read-only SQL query on any SQLite or D1 database and return the results as JSON. Only SELECT statements and read-only PRAGMAs are allowed. This is the primary tool for an AI agent to explore data in a D1 database.
Token budgeting: results are capped at max_rows (default 200) so a large table won't blow your context window — the response reports total_rows and truncated. Use the columns argument to project only the fields you need. Narrow with LIMIT / WHERE for precise reads.
For D1: set CLOUDFLARE_API_TOKEN + CLOUDFLARE_ACCOUNT_ID and use source=d1://DB_ID.
| Name | Required | Description | Default |
|---|---|---|---|
| sql | Yes | A read-only SQL query (SELECT or read-only PRAGMA). Mutations are rejected. | |
| source | Yes | Database source: a local file path (./app.db), a Cloudflare D1 DSN (d1://DB_ID when CLOUDFLARE_API_TOKEN+CLOUDFLARE_ACCOUNT_ID are set, or d1://TOKEN@ACCOUNT_ID/DB_ID), or a Turso DSN (turso://TOKEN@ORG/DB). | |
| columns | No | Optional: keep only these columns in each row (projection) to save context. | |
| max_rows | No | Maximum rows to return (default 200, max 2000). Excess rows are dropped and reported via truncated. |
| Name | Required | Description |
|---|---|---|
| rows | Yes | The result rows. |
| count | Yes | Rows returned after truncation. |
| truncated | Yes | True when total_rows exceeded max_rows. |
| total_rows | Yes | Rows the query produced before truncation. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint. The description adds useful behavioral details beyond annotations: results are capped at max_rows (default 200, max 2000), the response reports total_rows and truncated, and mutations are rejected. This provides context for AI agents.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured. The first sentence immediately states the purpose, followed by clear paragraphs on constraints, token budgeting, and setup. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters, output schema exists), the description is complete. It covers all aspects needed for correct invocation: query restrictions, row limits, projection, source configuration, and default behavior. No gaps are apparent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds meaningful context: explains the default and max for max_rows, the projection benefit of columns, and clarifies source format with examples (local, D1, Turso). This goes beyond the schema's descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs a read-only SQL query on SQLite or D1 databases returning JSON. It specifies only SELECT and read-only PRAGMAs are allowed, distinguishing it from sibling tools like litescope_schema or litescope_health by being the primary data exploration tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (exploring data) and gives practical guidance on token budgeting, row limits, column projection, and narrowing queries. However, it does not explicitly exclude scenarios or mention alternative sibling tools for different tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.8.0litescope_autopilotlitescope_healthlitescope_lockslitescope_migrate_planlitescope_schemalitescope_snapshot_listv0.6.4litescope_adviselitescope_autopilotlitescope_checklitescope_d1_listlitescope_difflitescope_fingerprintlitescope_fleet_healthlitescope_healthlitescope_lockslitescope_migrate_difflitescope_migrate_planlitescope_querylitescope_schemalitescope_snapshot_listEach tool targets a distinct operation: performance advice, backup verification, listing D1 databases, comparing databases, fleet clustering, fleet health, migration diff, and querying. No functional overlap.
All tools follow a consistent `litescope_` prefix with descriptive snake_case names (e.g., `litescope_advise`, `litescope_fleet_health`), making naming predictable.
8 tools cover a broad range of database management tasks without being excessive or insufficient. The count is well-scoped for a focused utility server.
Covers key read-only operations (analysis, diff, migration planning, querying, fleet management) but lacks write operations (e.g., applying migrations, creating databases). However, this aligns with the toolset's read-only design philosophy.
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