Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add rules/consiliency/code-index-mcp/databasegit clone --depth 1 https://github.com/Consiliency/Code-Index-MCPWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01098 | $0.01098 |
| Opus 5 | $0.00549 | $0.00549 |
| Sonnet 5 | $0.00220 | $0.00220 |
| Haiku 4.5 | $0.00110 | $0.00110 |
Grade A, and why
database scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Database Rules for Code-Index-MCP
Overview
This file defines database design patterns, query optimization strategies, and data management rules for the Code-Index-MCP project.
Storage Architecture
Local-First Design
- Primary Storage: SQLite with FTS5 for full-text search
- Index Format: Structured JSON with normalized tables
- File-Based: Each project has its own database file
- Portability: Database files can be copied/moved
Schema Design
-- Core tables structure
CREATE TABLE files (
id INTEGER PRIMARY KEY,
path TEXT UNIQUE NOT NULL,
content TEXT,
language TEXT,
last_modified INTEGER,
hash TEXT
);
CREATE TABLE symbols (
id INTEGER PRIMARY KEY,
file_id INTEGER REFERENCES files(id),
name TEXT NOT NULL,
type TEXT, -- function, class, variable, etc.
line_start INTEGER,
line_end INTEGER,
column_start INTEGER,
column_end INTEGER,
parent_id INTEGER REFERENCES symbols(id)
);
CREATE TABLE imports (
id INTEGER PRIMARY KEY,
file_id INTEGER REFERENCES files(id),
module_name TEXT,
alias TEXT,
line_number INTEGER
);
-- Full-text search tables
CREATE VIRTUAL TABLE files_fts USING fts5(
path, content, tokenize='porter unicode61'
);
CREATE VIRTUAL TABLE symbols_fts USING fts5(
name, type, tokenize='porter unicode61'
);
Query Optimization
Indexing Strategy
-- Performance indexes
CREATE INDEX idx_symbols_file ON symbols(file_id);
CREATE INDEX idx_symbols_name ON symbols(name);
CREATE INDEX idx_symbols_type ON symbols(type);
CREATE INDEX idx_imports_file ON imports(file_id);
CREATE INDEX idx_files_language ON files(language);
Query Patterns
# Efficient symbol lookup
def find_symbol(name: str, file_path: str = None):
query = """
SELECT s.*, f.path
FROM symbols s
JOIN files f ON s.file_id = f.id
WHERE s.name = ?
"""
params = [name]
if file_path:
query += " AND f.path = ?"
params.append(file_path)
return db.execute(query, params)
# Full-text search with ranking
def search_code(query: str):
return db.execute("""
SELECT path, snippet(files_fts, 1, '<b>', '</b>', '...', 32) as snippet,
rank
FROM files_fts
WHERE files_fts MATCH ?
ORDER BY rank
LIMIT 50
""", [query])
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 197 lines · 1,098 tokens per session scan A 09ce8c0874ea
database is a cursor rule published in the GitHub repository Consiliency/Code-Index-MCP (57 stars, last pushed 1mo ago), licensed MIT. It adds 1,098 tokens to every session, about $0.0055 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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