mcp-agora: Instructions file for Codex

AGENTS.md

mcp-agora AGENTS.md is an instructions file for Codex, OpenCode from cioffiAI/mcp-agora. It costs 3,759 tokens per session, scanned A, original, MIT.

Repository instructions for an MCP server that gives AI agents shared, persistent memory. It stores knowledge and lets agents search it by meaning across tools such as Claude Code, Codex, ChatGPT, and Gemini CLI.

In plain words
What is it for?
Use it to look up saved knowledge before investigating a problem and to save a useful solution after resolving one.
Why use it?
It helps agents reuse solutions and information from earlier work instead of starting from scratch or relying only on keyword searches.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions Claude Code; mentions AGENTS.md; mentions Codex.

This is cioffiAI/mcp-agora's own configuration. It tells Codex and OpenCode how to work on mcp-agora itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything mcp-agora configures →

Reuse

Borrowing it

Nothing to install: this file belongs to cioffiAI/mcp-agora. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/cioffiAI/mcp-agora/master/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/cioffiAI/mcp-agora

Made for: Codex, OpenCode.

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Per session 3,759 This file is loaded in full into every session.
When invoked 3,759 The same file — it is already loaded in full.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.03759 $0.03759
Opus 5.5 $0.01504 $0.01504
Sonnet 5.5 $0.00752 $0.00752
Haiku 4.5 $0.00376 $0.00376

Measured 2mo ago against content hash 814327a87a24, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

mcp-agora AGENTS.md scanned grade A with 1 finding 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.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

uv run python -c "import subprocess,json,time;p=subprocess.Popen(['agora.exe'],stdin=subprocess.PIPE,stdout=subprocess.PIPE,stderr=subprocess.PIPE,text=True);time.sleep(20);p.stdin.write(json.dumps({'jsonrpc':'2.0','id':
AGENTS.md · 315 lines

How it starts

The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.

MCP Agora — Agent Instructions

Memory & Knowledge (Agora stesso)

Agora è un MCP server con memoria persistente cross-agente. Quando lavori in questo progetto:

  1. Prima di cercare soluzioni esterne → chiama agora_query per vedere se informazioni utili sono già state salvate
  2. Dopo aver risolto un problema → chiama agora_save per memorizzare la soluzione (con tags)
  3. Le informazioni sono condivise tra tutti gli agenti — Claude Code, Codex, ChatGPT, Gemini CLI

La ricerca è semantica (embedding a 384 dimensioni), non per keyword. ChromaDB usa un indice ANN (HNSW) che trova le entry più simili in O(log n) — anche con 10.000 entry la risposta arriva in <50ms.

Project Overview

MCP Agora is a portfolio/learning project implementing an MCP Server with cross-agent persistent memory. It is NOT a product — it is not competing with ContextForge (IBM), MetaMCP, AutoMem, or mcp-memory-service.

Goal

Build an MCP Server that allows AI agents (Claude Code, Codex, ChatGPT, Gemini CLI) to:

  • Save knowledge with agora.save → persistent vector memory (ChromaDB)
  • Query knowledge with agora.query → semantic search across saved entries
  • Share memory across agents and sessions
  • Cache frequent queries in-memory (TTLCache)

Non-goals

  • Semantic broadcasting / fan-out
  • Chunking (save short entries only, ≤256 word pieces)
  • Docker, RBAC, auth, scaling

Architecture Stack (corrente)

Python 3.13+  │  uv 0.11+
FastMCP       │  MCP SDK ≥1.0.0
ChromaDB      │  PersistentClient
sentence-transformers  │  all-MiniLM-L6-v2 (384d)
cachetools    │  TTLCache (1000 entries, 5min TTL)
pyyaml        │  config.yaml
pytest        │  pytest-asyncio
SQLite3       │  stdlib (provenance, agent registry, L2 cache)

Directory Structure

mcp-agora/
├── pyproject.toml
├── config.yaml
├── README.md
├── AGENTS.md
├── ARCHITECTURE.md
├── agora/
│   ├── __init__.py
│   ├── main.py              # Entry point: `agora` command
│   ├── server.py            # FastMCP server + 8 tool registration
│   ├── config.py            # YAML config loader
│   ├── logging.py           # File-based structured logging
│   ├── registry.py          # BackendRegistry (lifecycle, lazy connect)
│   ├── connectors/
│   │   ├── __init__.py
│   │   ├── base.py          # BackendConnector ABC + ReadOnlyBlockedError
│   │   ├── stdio.py         # STDIO subprocess MCP client
│   │   └── http.py          # Streamable HTTP MCP client
│   ├── routing/
│   │   ├── __init__.py
│   │   └── router.py        # Semantic + exact name router
│   ├── embedding/
│   │   ├── __init__.py
│   │   ├── base.py          # Abstract EmbeddingProvider + WarmingUpError
│   │   └── sentence.py      # sentence-transformers wrapper (sync preload, local_files_only, 60s timeout)
│   ├── memory/
│   │   ├── __init__.py
│   │   └── vector_store.py  # ChromaDB PersistentClient wrapper
│   ├── cache/
│   │   ├── __init__.py
│   │   ├── l1_memory.py     # TTLCache in-memory
│   │   └── l2_cache.py      # SQLite-backed persistent cache
│   └── db/
│       ├── __init__.py
│       └── database.py      # SQLite: agents, provenance, L2 cache
├── tests/
│   ├── __init__.py
│   ├── test_embedding.py
│   ├── test_memory.py
│   ├── test_cache.py
│   ├── test_l2_cache.py     # L2 persistent cache tests
│   ├── test_provenance.py   # Provenance + agent registry tests
│   ├── test_protocol.py
│   ├── test_routing.py
│   ├── test_connectors.py
│   ├── test_graceful.py     # Health check, retry, rate limit tests
│   ├── test_mcp_smoke.py
│   └── _echo_server.py      # Minimal FastMCP echo server for tests
└── examples/
    └── config.yaml.example

Read the full file on GitHub · 315 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. 2mo ago First seen · 315 lines · 3,759 tokens per session scan A 814327a87a24

Subscribe to this mod's changes

mcp-agora AGENTS.md is an instructions file published in the GitHub repository cioffiAI/mcp-agora (5 stars, last pushed 3mo ago), licensed MIT. It adds 3,759 tokens to every session, about $0.0150 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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