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 commands/codingthefuturewithai/rag-retriever/rag-list-collectionsgit clone --depth 1 https://github.com/codingthefuturewithai/rag-retrieverWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-list-collections)<a href="https://agentmods.dev/commands/codingthefuturewithai/rag-retriever/rag-list-collections"><img src="https://agentmods.dev/badge/commands/codingthefuturewithai/rag-retriever/rag-list-collections.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00000 | $0.00259 |
| Opus 5 | $0.00000 | $0.00130 |
| Sonnet 5 | $0.00000 | $0.00052 |
| Haiku 4.5 | $0.00000 | $0.00026 |
Grade A, and why
rag-list-collections 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 5d 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.
What it actually says
List RAG Retriever Collections
Discover all available vector store collections and their contents.
Prerequisites
This command requires the RAG Retriever MCP server to be configured in your Claude Code setup. The server manages vector store collections for semantic search.
Implementation Approach
This command uses direct implementation as it involves straightforward MCP operations and data analysis.
Your Task
-
Collection Discovery
- Use
list_collectionsto retrieve all available collections - Display collection names with document counts
- Show metadata like creation dates and descriptions
- Use
-
Collection Analysis
- Analyze the size and scope of each collection
- Identify the most useful collections for different use cases
- Note any empty or underutilized collections
-
Usage Recommendations
- Suggest which collections to use for different types of queries
- Recommend collection names for specific search scenarios
- Identify collections that might need updating or expansion
Success Criteria
- Complete list of all collections displayed
- Document counts and metadata shown for each collection
- Clear recommendations for collection usage
Available MCP Tools
list_collections()- List all available collections with metadatavector_search(query, collection_name)- Search specific collections if needed for analysis
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.
- 5d ago First seen · 34 lines · 0 tokens per session scan A 613f24ed316a
rag-list-collections is a command published in the GitHub repository codingthefuturewithai/rag-retriever (27 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 259 tokens. 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.
Other commands, from other repositories
querying
Query documents from a search index using type-safe filters with support for pagination, sorting, field selection, scoring, and highlighting. Count matching documents efficiently without returning results.
agent-brain-index
Index documents for semantic search.
agent
Add an AI agent / RAG backend (@convex-dev/agent) to the Convex app.
ingest
Manually add knowledge to the Weaviate store.
web-ingest
Crawl the configured source and generate knowledge markdown files.
web-update
Re-ingest a URL or refresh a local knowledge file, then rebuild the search index.