Lsl

A Model Context Protocol (MCP) server providing accurate LSL (Linden Scripting Language) reference data to AI coding assistants. Grounded in the official Second Life wiki, with a curated database of AI-specific pitfalls.

Motivation

AI coding assistants (Claude Code, Kiro, etc.) frequently produce incorrect LSL — hallucinated function names, unsupported syntax, and wrong signatures. This server gives those tools a live, authoritative reference to query against rather than relying on training data.

Design Goals

• Portable LSL only — no Firestorm-specific syntax
• AI-aware — pitfalls are categorised and tagged by which tool produced them
• Git-tracked — JSON is the source of truth; SQLite is always derivable
• Incrementally maintainable — new pitfalls are captured via CLI as they are discovered

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Project Structure

lsl-mcp/
├── server.py              # MCP server entrypoint
├── db/
│   ├── schema.sql         # Table definitions
│   └── lsl.db             # SQLite database (gitignored)
├── data/
│   ├── functions/         # One JSON file per LSL function (source of truth)
│   ├── pitfalls.json      # Language pitfalls collection (source of truth)
│   └── constants.json     # LSL constants
├── scripts/
│   ├── scrape_wiki.py     # Populates data/functions/ from the LSL wiki
│   ├── load_db.py         # Imports JSON data into lsl.db
│   └── add_pitfall.py     # CLI tool for adding new pitfalls
├── tools/
│   ├── lookup.py          # lsl_lookup_function, lsl_search
│   ├── pitfalls.py        # lsl_get_pitfalls, lsl_check_code
│   └── reference.py       # lsl_list_events, lsl_constants
└── pyproject.toml

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Requirements

• Python 3.11+
• uv (recommended) or pip

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Initial Setup

1. Install dependencies

Using uv (recommended):

uv sync

Using pip:

pip install -e .

For development (includes pytest):

uv sync --dev
# or
pip install -e ".[dev]"

2. Scrape the LSL wiki

Populates data/functions/ with one JSON file per function. Approximately 700 functions; takes several minutes due to polite request throttling.

python scripts/scrape_wiki.py

Test a single function first:

python scripts/scrape_wiki.py --function llListen --dry-run

2. Initialise the database

Creates db/lsl.db from db/schema.sql and loads all JSON data.

python scripts/load_db.py

3. Connect to Claude Code

Register the server with Claude Code (personal scope):

# using uv (recommended)
claude mcp add --transport stdio lsl -- uv run /path/to/lsl-mcp/server.py

# using python directly
claude mcp add --transport stdio lsl -- python /path/to/lsl-mcp/server.py

Or add a .mcp.json to your project root for project-wide sharing:

{
  "mcpServers": {
    "lsl": {
      "type": "stdio",
      "command": "uv",
      "args": ["run", "/path/to/lsl-mcp/server.py"]
    }
  }
}

Verify the server is connected:

claude mcp list

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MCP Tools

Tool	Description
lookup_function(name)	Exact or fuzzy match on function name. Returns full record including signature, parameters, caveats, and known AI pitfalls. Falls back to did_you_mean on miss.
search_functions(query, limit?)	Full-text search across function names and descriptions. Returns ranked summary list.
get_pitfalls(category?, ai_source?)	Returns all pitfall entries, optionally filtered by category or source tool.
check_code(code)	Scans raw LSL source for known AI-generated mistakes. Returns line numbers and suggestions.
list_events(name?)	Returns all valid LSL event signatures, or looks up one by name.
get_constants(category?, name?)	Returns constants by category or direct name lookup.

Recommended AI workflow

Before starting an LSL task:
    get_pitfalls()               ← full briefing on known mistakes

While writing LSL:
    lookup_function("llFoo")     ← verify signature before use
    list_events("touch_start")   ← verify event name and parameters
    get_constants("permissions")  ← browse constants by category

Before presenting code to the user:
    check_code(generated_lsl)    ← catch mistakes before they reach the user

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Pitfall Categories

Category	Description
reserved_words	LSL type names or keywords used as variable identifiers
nonexistent_functions	Hallucinated function names with no LSL equivalent
unsupported_syntax	Syntax valid in other languages but a compile error in LSL
scoping	Permission/access scope issues that silently block operations
type_coercion	Implicit casting that LSL does not perform
state_behavior	Unexpected behavior around state changes
performance	Functions with hidden sleeps or inefficient patterns

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Adding a Pitfall

When an AI tool produces incorrect LSL, capture it:

python scripts/add_pitfall.py \
  --category nonexistent_functions \
  --title "llStringReplace does not exist in LSL" \
  --bad "llStringReplace(src, old, new)" \
  --good "llReplaceSubString(src, pattern, replacement, count)" \
  --ai-source kiro \
  --notes "llStringReplace is a plausible-sounding hallucination, likely confused with string replace conventions in other languages."

Then sync the database and commit:

python scripts/load_db.py --only pitfalls
git add data/pitfalls.json
git commit -m "pitfall(func_001): llStringReplace does not exist [kiro]"

Claude-assisted capture

Describe the mistake to Claude in plain language. Claude will format it into the correct schema and emit the exact CLI command to run. You verify, run it, and commit. Claude never writes to the database directly — the CLI is the deterministic protocol.

--dry-run

Preview what would be written without touching any files:

python scripts/add_pitfall.py --category unsupported_syntax --title "..." --dry-run

---

Refreshing Wiki Data

Re-scrape all functions and reload:

python scripts/scrape_wiki.py --overwrite
python scripts/load_db.py --only functions

Re-scrape a single function:

python scripts/scrape_wiki.py --function llReplaceSubString
python scripts/load_db.py --only functions

---

Database

db/lsl.db is gitignored — it is always fully derivable from the JSON sources via load_db.py. Only data/ and db/schema.sql are version-controlled.

To rebuild from scratch:

python scripts/load_db.py --reset

---

Pitfall ID Format

IDs are assigned automatically by add_pitfall.py based on category:

Category	Prefix	Example
reserved_words	lang	lang_001
nonexistent_functions	func	func_001
unsupported_syntax	syn	syn_001
scoping	scope	scope_001
type_coercion	type	type_001
state_behavior	state	state_001
performance	perf	perf_001

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Known Pitfalls

ID	Category	Issue	Source
type_001	type_coercion	Boolean NOT ! does not work on strings	claude-code
state_001	state_behavior	Cached owner key not updated after ownership transfer	claude-code
lang_001	reserved_words	key used as variable name	kiro
func_001	nonexistent_functions	llStringReplace does not exist	kiro
syn_001	unsupported_syntax	Ternary operator not supported	both
syn_002	unsupported_syntax	Switch statements not supported	both
scope_001	scoping	Transfer-only permissions silently prevent script edits	kiro
perf_001	state_behavior	llSetPrimitiveParams has forced 0.2s sleep; use llSetLinkPrimitiveParamsFast	both

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Testing

# Run all tests
python3 -m pytest

# Verbose output
python3 -m pytest -v

# Single module
python3 -m pytest tests/test_pitfalls.py

Tests use an in-memory SQLite fixture database built from db/schema.sql — no real lsl.db or network access required. The conftest.py fixture patches DB_PATH in all tool modules automatically.

Test file	Coverage
test_lookup.py	lsl_lookup_function, lsl_search
test_pitfalls.py	lsl_get_pitfalls, lsl_check_code
test_reference.py	lsl_list_events, lsl_constants
test_add_pitfall.py	ID generation logic in add_pitfall.py

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gitignore

db/lsl.db
data/functions/
data/scrape_errors.json
__pycache__/
*.pyc
.venv/

data/functions/ is gitignored because it is fully regenerable from the wiki. Only data/pitfalls.json and data/constants.json are committed — these contain hand-curated data that cannot be scraped.