Borrowing it
Nothing to install: this file belongs to AnthonyPuggs/ausecon-mcp-server. 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/AnthonyPuggs/ausecon-mcp-server/main/AGENTS.mdgit clone --depth 1 https://github.com/AnthonyPuggs/ausecon-mcp-serverWrote 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/anthonypuggs/ausecon-mcp-server/agents-md)<a href="https://agentmods.dev/instructions/anthonypuggs/ausecon-mcp-server/agents-md"><img src="https://agentmods.dev/badge/instructions/anthonypuggs/ausecon-mcp-server/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/anthonypuggs/ausecon-mcp-server/agents-md"><img src="https://agentmods.dev/badge/instructions/anthonypuggs/ausecon-mcp-server/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 | $0.00901 | $0.00901 |
| Opus 5.5 | $0.00360 | $0.00360 |
| Sonnet 5.5 | $0.00180 | $0.00180 |
| Haiku 4.5 | $0.00090 | $0.00090 |
Grade A, and why
ausecon-mcp-server 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
This file provides guidance to Codex (Codex.ai/code) when working with code in this repository.
Commands
Python 3.12 is recommended for local development. The package metadata and CI matrix support Python 3.10+.
# Install dependencies
uv sync --python 3.12
# Run all tests
uv run pytest
# Run a single test file
uv run pytest tests/test_catalogue.py
# Run a single test by name
uv run pytest tests/test_catalogue.py::test_search_catalogue_prefers_high_value_alias_matches
# Lint and format
uv run ruff check src tests
uv run ruff format src tests
# Run the MCP server (stdio mode)
uv run ausecon-mcp-server
Architecture
This is a FastMCP server that wraps ABS (Australian Bureau of Statistics) and RBA (Reserve Bank of Australia) data APIs. The design is intentionally thin on the MCP surface and delegates all logic to internal layers.
Layer structure
server.py → FastMCP tool definitions, AuseconService orchestrator
providers/ → Async httpx clients for each source (ABS, RBA), TTLCache wrappers
parsers/ → Pure functions: raw HTTP response text → normalised dicts
catalogue/ → Static curated dicts (abs.py, rba.py) + search.py ranking logic
models.py → SeriesDescriptor and Observation dataclasses; shared to_dict() helpers
cache.py → Dual-layer TTLCache (memory + on-disk JSON cache)
Data flow
- MCP tool call →
AuseconServicemethod AuseconService→ABSProviderorRBAProvider(checks TTLCache first)- Provider → httpx GET → raw CSV or XML response
- Parser (
parse_abs_csv,parse_rba_csv,parse_abs_structure) →{metadata, series, observations}dict - Optional post-fetch filtering (
filters.py) forlast_n, date ranges, and series IDs - Result cached and returned as dict (not model instances)
ABS vs RBA differences
- ABS uses SDMX REST (
data.api.abs.gov.au/rest): structure endpoint returns XML, data endpoint returns CSV withformat=csvfilewithlabels(paired code/label columns; the parser also still accepts the code-onlycsvfilelayout). - RBA serves static CSVs at
rba.gov.au/statistics/tables/csv/; most files use{table_id}-data.csv, while cataloguecsv_pathoverrides cover exceptions such asf17-yields.csv. Filtering is done client-side after download.
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 · 77 lines · 901 tokens per session scan A 02f0672245cd
ausecon-mcp-server AGENTS.md is an instructions file published in the GitHub repository AnthonyPuggs/ausecon-mcp-server (4 stars, last pushed yesterday), licensed MIT. It adds 901 tokens to every session, about $0.0036 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.
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