Borrowing it
Nothing to install: this file belongs to markdevshop/vector-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/markdevshop/vector-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/markdevshop/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/instructions/markdevshop/vector-mcp/agents-md)<a href="https://agentmods.dev/instructions/markdevshop/vector-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/markdevshop/vector-mcp/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/markdevshop/vector-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/markdevshop/vector-mcp/agents-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.02938 | $0.02938 |
| Opus 5.5 | $0.01175 | $0.01175 |
| Sonnet 5.5 | $0.00588 | $0.00588 |
| Haiku 4.5 | $0.00294 | $0.00294 |
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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- vector-mcp AGENTS.md — 100% identical, 0 lines differ
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.mdimport) — the two stay in sync. Edit this file, notCLAUDE.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
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.
- 1mo ago First seen · 308 lines · 2,938 tokens per session scan A 57a5c22ed14c
vector-mcp AGENTS.md is an instructions file published in the GitHub repository markdevshop/vector-mcp (11 stars, last pushed 3mo ago), licensed MIT. It adds 2,938 tokens to every session, about $0.0118 per session on Opus 5.5. 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 2026-08-31.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deno copilot-instructions.md
Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.