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 skills/knuckles-team/vector-mcp/vector-backend-operationsnpx skills add Knuckles-Team/vector-mcp --skill vector-backend-operationsgit clone --depth 1 https://github.com/Knuckles-Team/vector-mcpWrote 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/skills/knuckles-team/vector-mcp/vector-backend-operations)<a href="https://agentmods.dev/skills/knuckles-team/vector-mcp/vector-backend-operations"><img src="https://agentmods.dev/badge/skills/knuckles-team/vector-mcp/vector-backend-operations.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 | $0.00113 | $0.01074 |
| Opus 5 | $0.00056 | $0.00537 |
| Sonnet 5 | $0.00023 | $0.00215 |
| Haiku 4.5 | $0.00011 | $0.00107 |
Grade A, and why
vector-backend-operations 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 4d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Backend Operations
Backend selection and connection wiring for the vector-mcp MCP server. Both the
vector_collection_management and vector_search tools take the same db_type +
connection parameters — this skill is the reference for getting those right per
engine.
When to use
- Choose a vector backend for a workload (embedded vs. networked, dev vs. prod).
- Assemble the correct connection parameters for a chosen
db_type. - Migrate a corpus between backends (e.g. local chromadb → hosted qdrant).
- Diagnose "backend unreachable / wrong params" failures before running collection or search calls.
When NOT to use
- Creating/loading/deleting collections →
vector-collection-management. - Semantic / lexical / hybrid retrieval →
vector-hybrid-search.
Prerequisites & environment
Connect via the mcp-client skill against the vector-mcp MCP server. The
relevant backend extra must be installed on the server (vector-mcp[chromadb],
[postgres], [qdrant], [couchbase], [mongodb], or [all]).
Backends & connection parameters
db_type selects the engine; the other params depend on it:
db_type |
Typical params | Notes |
|---|---|---|
chromadb |
db_path (embedded) or host/port |
Zero-infra local default |
postgres |
host, port, db_name, username, password |
pgvector-backed |
qdrant |
host, port |
Fast ANN, fastembed extra |
couchbase |
host, db_name, username, password |
|
mongodb |
host, port, db_name, username, password |
Atlas Vector Search |
The same params flow into vector_collection_management and vector_search, so
once you know the backend shape, reuse it across every call.
Tools & actions
Backend selection is not a standalone action — it is the db_type + connection
arguments shared by both tools:
| Condensed tool | Actions using these params |
|---|---|
vector_collection_management |
create_collection, add_documents, list_collections, delete_collection |
vector_search |
semantic_search, lexical_search, search |
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.
- 4d ago First seen · 96 lines · 113 tokens per session scan A b3158e94bec1
vector-backend-operations is a skill published in the GitHub repository Knuckles-Team/vector-mcp (15 stars, last pushed 6d ago), licensed MIT. It adds 113 tokens to every session and 1,074 once invoked, about $0.0006 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.
Other skills, from other repositories
vector-databases
Vector database operations — embed, store, search, and build RAG pipelines.
vector-databases
Vector database engineering covering Pinecone, Weaviate, Chroma, Qdrant, pgvector, and FAISS — including embedding pipeline design, HNSW index parameter tuning, hybrid dense+sparse (BM25) search, metadata filtering, namespace and tenant sharding, and RAG retrieval patterns for production knowledge systems.
global-utils-knowledge
Domain knowledge for the globalutils shared Python library. Provides cross-service utilities: config, Redis, ports, helpers, embedding, Flask, and Celery app setup. Use when working on files under globalutils/.
knowledge
Knowledge systems authority — RAG pipelines, vector search with Qdrant, embedding generation, semantic chunking, knowledge graph construction, retrieval evaluation, and citation-aware QA patterns.
chromadb
Semantic search for model descriptions and content using ChromaDB vector database. Use when searching for similar models, finding related content, querying vector collections, or looking up models by description or semantic similarity.
PostgreSQL Performance Optimization
Production-grade PostgreSQL query optimization, indexing strategies, performance tuning, and modern features including pgvector for AI/ML workloads. Master EXPLAIN plans, query analysis, and database design for high-performance applications.