Borrowing it
Nothing to install: this file belongs to thinkchainai/stock-intelligence-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/thinkchainai/stock-intelligence-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/thinkchainai/stock-intelligence-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/thinkchainai/stock-intelligence-mcp/agents-md)<a href="https://agentmods.dev/instructions/thinkchainai/stock-intelligence-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/thinkchainai/stock-intelligence-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/thinkchainai/stock-intelligence-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/thinkchainai/stock-intelligence-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 | $0.01947 | $0.01947 |
| Opus 5.5 | $0.00779 | $0.00779 |
| Sonnet 5.5 | $0.00389 | $0.00389 |
| Haiku 4.5 | $0.00195 | $0.00195 |
Grade A, and why
stock-intelligence-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 2mo 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock Intelligence MCP — Agent Instructions
What This Is
An MCP App Server for stock market data with AI-driven dynamic charting. 10 tools: 1 interactive charting tool (stock_app with 7 views and inline controls), 7 inline data tools (for text responses), and 2 utility tools. Built with FastMCP (Python). All data from Financial Modeling Prep (FMP) free tier (250 calls/day).
Key Architectural Decision: Two Modes
Visual mode — stock_app is the single charting tool. It has meta={"ui": {"resourceUri": APP_RESOURCE_URI}} which opens the MCP App. The AI picks the view based on the user's question. The chart renders with inline controls (period, chart type, statement type) so users can adjust without a new AI call.
Inline mode — 7 data tools (stock_quote, stock_price_history, etc.) return structured data for text responses. No meta, no UI. The AI formats the data in its response.
When to use which:
- User says "show me", "chart", "visualize" →
stock_app - User says "what's the price", "tell me", "how much" → data tool
- User asks for numbers then says "chart that" →
stock_appwith same params
Architecture
src/stock_intelligence/
├── __init__.py # Package init — exports get_app_html()
├── server.py # FastMCP server — 10 tools + MCP App resource
├── core/
│ ├── views.py # View renderers + inline data functions (public API)
│ ├── scoring.py # Quote summarization, number formatting
│ └── clients/
│ └── fmp.py # FMP API client — free tier endpoints only
└── ui/
└── app.html # Interactive chart renderer — canvas-based, zero deps, inline controls
stock_app Views & Parameters
stock_app(
symbol="AAPL", # Stock ticker. Ignored for market view.
view="chart", # chart|quote|financials|earnings|analyst|valuation|market
period="6m", # Chart view: 1m|3m|6m|1y|2y|5y
chart_type="", # Override rendering. Leave empty for best default.
statement="income", # Financials view: income|balance|cashflow
fiscal_period="quarter", # Financials view: quarter|annual
)
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.
- 2mo ago First seen · 159 lines · 1,947 tokens per session scan A 0f7ff710dac8
stock-intelligence-mcp AGENTS.md is an instructions file published in the GitHub repository thinkchainai/stock-intelligence-mcp (2 stars, last pushed 5mo ago), licensed MIT. It adds 1,947 tokens to every session, about $0.0078 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.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.
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).