lsl-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct aspect of LSL development: code checking, constants, pitfalls database, events, function lookup by name, and full-text search. No two tools have overlapping purposes, and descriptions clearly differentiate them.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in lowercase snake_case (e.g., check_code, lookup_function). The naming is predictable and easy to understand.
Tool Count5/5
With 6 tools, the server is well-scoped for assisting with LSL code. Each tool provides essential functionality without unnecessary bloat. The count feels appropriate for the domain.
Completeness5/5
The tool set covers the core needs for LSL development: checking for pitfalls, exploring constants, events, and functions. Missing features like syntax guidance are mitigated by detailed returns from lookup_function and search_functions.
Average 4.5/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It describes the tool as returning pitfalls, implying a read-only operation. However, it doesn't disclose potential side effects, rate limits, or authentication needs. The description is adequate but not rich in behavioral details.
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 with a clear purpose statement, usage guidance, and parameter details. It is front-loaded with the main intent and contains no superfluous words.
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 has only two optional parameters and no output schema, the description provides sufficient context: purpose, usage, and parameter options. It doesn't describe the return format, but for a list of pitfalls, this is likely acceptable. Overall, it is complete enough for an agent to use 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?
The input schema has 0% description coverage, so the description compensates by listing allowed values for category and ai_source in the Args section. It explains that category can be one of several enumerated values and ai_source can be 'kiro', 'claude-code', or 'both', adding significant meaning 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?
The description clearly states the tool returns known LSL pitfalls for AI coding assistants. It specifies the verb 'Return' and the resource 'known LSL pitfalls'. It also differentiates from sibling tools like check_code and get_constants by focusing specifically on pitfalls.
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 recommends calling with no arguments for a full briefing before starting an LSL task. It also explains how to filter by category or ai_source. While it doesn't explicitly state when not to use it, the usage advice is clear and contextual.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully bears the burden of behavioral disclosure. It mentions 'returns a ranked summary list' but does not describe the search algorithm, ranking criteria, pagination, error handling, or structure of the summary. For a tool with zero annotation coverage, this is insufficient.
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 appropriately sized: a one-line purpose statement, a two-line usage guideline, and two-line parameter descriptions (with examples). Every sentence is necessary and front-loaded with the most important info. No fluff or irrelevant details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no output schema and no annotations, the description lacks details about the return format (structure of summary list, fields included). It also doesn't mention rate limits, error cases, or behavior when no results found. However, the param descriptions are good and the usage guidance is clear. It is somewhat incomplete but functional.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It provides meaningful explanations: for 'query' it says 'Keywords or natural language, e.g. "listen channel message" or "set prim texture face".' for 'limit' it says 'Maximum results to return (default 10, max 25).' These add significant meaning beyond the schema's mere titles and types.
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 states 'Full-text search across LSL function names and descriptions.' It uses specific verb (search) and resource (LSL function names/descriptions) and explicitly distinguishes from sibling 'lookup_function' by saying it returns a ranked summary list and to use lookup_function for the full record.
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?
The description says 'Use when you know roughly what a function does but not its exact name.' and 'returns a ranked summary list — call lookup_function for the full record.' This explicitly tells when to use and when not to use, with an alternative named.
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?
With no annotations, the description carries the full burden. It explains what the tool checks (pitfalls) and that it returns line numbers and suggestions. However, it does not disclose if the tool is read-only or if it has side effects, but for a scanning tool this is acceptable. The description adds value beyond the input schema.
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 (approximately 8 lines) with no wasted words. It front-loads the purpose and usage guidance, then lists checks and parameter details in a clear, structured manner.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should detail the return structure. It mentions 'line numbers and suggestions' but does not specify the format (e.g., list of objects). While the tool is simple, this omission could confuse an AI agent when interpreting results.
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?
The single parameter 'code' has no description in the schema. The description adds 'Raw LSL source code as a string,' which clarifies the expected input. While it could specify constraints like max length, the current description is sufficient for correct usage.
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 verb 'scan', the resource 'LSL code snippet', and the specific purpose of finding AI-generated pitfalls. It lists specific checks like nonexistent functions and unsupported syntax, which distinguishes it from sibling tools that are informational (e.g., lookup_function, get_constants).
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?
Explicitly states when to use the tool: 'Call this on any LSL you generate before presenting it to the user.' This provides clear guidance without ambiguity, covering the primary use case.
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?
Despite no annotations, the description adequately conveys a read-only query operation and explains the two distinct behaviors. No side effects or destructive actions are indicated, and the description provides sufficient behavioral context for an agent.
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, using three well-structured sentences and a clear arg specification. Every sentence adds value without redundancy, and the most important information is front-loaded.
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 simple tool with no output schema and one optional parameter, the description covers all necessary aspects: purpose, two usage modes, and parameter behavior. It could be slightly more specific about the return format, but overall it is complete and actionable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates by providing a natural language explanation, including an example ('listen', 'touch_start'), and explicitly states what happens when the parameter is omitted versus provided.
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 tool returns LSL event signatures and explicitly differentiates between listing all events and retrieving details for a specific event. This sets it apart from siblings like lookup_function (for functions) and check_code (for code checking).
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?
Provides clear usage instructions for both calling modes: with no argument to list all events, or with a name to get full signature details. Does not explicitly contrast with siblings, but the context from sibling names and the description makes the intended use obvious.
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?
Discloses fuzzy matching, 'did_you_mean' list, and full return record (signature, parameters, etc.), fully informing the agent of behavior without 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?
Concise with clear bullet-like listing of return fields; 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?
Complete for a single-parameter tool without output schema; covers return content, fallback, and examples adequately.
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?
Provides concrete examples like 'llListen' and 'llReplaceSubString,' adding value beyond the schema, though schema coverage is 0%.
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 'Look up an LSL function by name,' distinguishing it from sibling 'search_functions' by emphasizing exact lookup and fuzzy fallback.
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?
Indicates use for exact names with fallback and 'did_you_mean' for non-matches, but doesn't explicitly contrast with 'search_functions' or specify when not to use.
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?
Given no annotations, the description adequately explains behavior: default returns categories/count, category filters, name overrides category. It lacks mention of error handling for invalid inputs but covers core mechanics.
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 with a clear summary followed by usage scenarios and structured Arg section. 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?
For a simple two-parameter tool with no output schema, the description covers all scenarios: no args, category, name, and precedence. It provides complete guidance for an AI agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully compensates by explaining both parameters: category as a filter, name as exact lookup with precedence over category. This adds essential meaning beyond the schema's bare types.
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 returns LSL constants with optional filtering by category or name. It distinctly separates from siblings like 'check_code' and 'get_pitfalls' by specifying the exact resource and operation.
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 instructions are given: call with no arguments to see categories, use category for browsing, use name for direct lookup with precedence. This clarifies when to use each parameter and contrasts with sibling tools.
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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