litescope

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Server Quality Checklist

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Latest release: v0.8.0
Disambiguation5/5
Each 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.
Naming Consistency5/5
All tools follow a consistent `litescope_` prefix with descriptive snake_case names (e.g., `litescope_advise`, `litescope_fleet_health`), making naming predictable.
Tool Count5/5
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.
Completeness4/5
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.
Average 4.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 181 commits in the last 12 weeks
- Last stable release on July 14, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under AGPL 3.0.
This repository includes a README.md file.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines3/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines3/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines3/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness4/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior4/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.
Behavior5/5
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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines4/5
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.

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