vector-mcp AGENTS.md

A repository instruction file for vector-mcp, an MCP server that lets agents use tools and vector databases for searching stored information. It documents the project’s Python stack, architecture, diagrams, and required commands.

In plain words
What is it for?
Use it when working on vector-mcp to understand its server, agent, skills, retrieval, and vector-database components, plus the commands and workflows the project expects.
Why use it?
It gives coding agents project-specific rules and structure so they can modify the repository consistently.

Instructions file for CodexOpenCode

Install

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.

agentmods
npx agentmods add instructions/craftingcodegig/vector-mcp/agents-md
Clone the repo
git clone --depth 1 https://github.com/craftingcodegig/vector-mcp

Made for: Codex, OpenCode.

Per session 2,938 This file is loaded in full into every session.
When invoked 2,938 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.02938 $0.02938
Opus 5 $0.01469 $0.01469
Sonnet 5 $0.00588 $0.00588
Haiku 4.5 $0.00294 $0.00294

Measured 2d ago against content hash 57a5c22ed14c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

vector-mcp AGENTS.md 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.

Origin

This is a copy

100% identical to vector-mcp AGENTS.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

AGENTS.md · 308 lines

How it starts

The opening of the file, as written. The whole thing — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Claude Code loads this file via CLAUDE.md (@AGENTS.md import) — the two stay in sync. Edit this file, not CLAUDE.md.

Tech Stack & Architecture

  • Language/Version: Python 3.10+
  • Core Libraries: agent-utilities, fastmcp, pydantic-ai
  • Key principles: Functional patterns, Pydantic for data validation, asynchronous tool execution.
  • Architecture:
    • mcp_server.py: Main MCP server entry point and tool registration.
    • agent.py: Pydantic AI agent definition and logic.
    • skills/: Directory containing modular agent skills (if applicable).
    • vectordb/: Vector database implementations for multiple backends.
    • retriever/: Retriever implementations for each backend.

Architecture Diagram

graph TD
    User([User/A2A]) --> Server[A2A Server / FastAPI]
    Server --> Agent[Pydantic AI Agent]
    Agent --> Skills[Modular Skills]
    Agent --> MCP[MCP Server / FastMCP]
    MCP --> VectorDB[Vector Database Layer]
    VectorDB --> Backend[Backend Implementation]
    Backend --> Storage[(Vector Storage)]

Workflow Diagram

sequenceDiagram
    participant U as User
    participant S as Server
    participant A as Agent
    participant T as MCP Tool
    participant V as VectorDB
    participant B as Backend

    U->>S: Request
    S->>A: Process Query
    A->>T: Invoke Tool
    T->>V: VectorDB Operation
    V->>B: Backend Call
    B-->>V: Backend Response
    V-->>T: VectorDB Result
    T-->>A: Tool Result
    A-->>S: Final Response
    S-->>U: Output

Commands (run these exactly)

Installation

pip install .[all]

Quality & Linting (run from project root)

pre-commit run --all-files

Execution Commands

vector-mcp\nvector_mcp.mcp:mcp_server\n# vector-agent\nvector_mcp.agent:agent_server

Testing

Start test databases

podman-compose -f docker-compose.test.yml up -d

Run all tests

python -m pytest tests/test_all_backends.py -v

Run specific backend tests

python -m pytest tests/test_all_backends.py -k chromadb -v python -m pytest tests/test_all_backends.py -k postgres -v python -m pytest tests/test_all_backends.py -k mongodb -v python -m pytest tests/test_all_backends.py -k qdrant -v python -m pytest tests/test_all_backends.py -k couchbase -v

Read the full file on GitHub · 308 lines

Changes

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.

  1. 2d ago First seen · 308 lines · 2,938 tokens per session scan A 57a5c22ed14c

Subscribe to this mod's changes

vector-mcp AGENTS.md is an instructions file published in the GitHub repository craftingcodegig/vector-mcp (11 stars, last pushed 2mo ago), licensed MIT. It adds 2,938 tokens to every session, about $0.0147 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to vector-mcp AGENTS.md, differing in 0 lines, and is treated as a copy.

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