> For the complete documentation index, see [llms.txt](https://docs.dctx.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dctx.ai/key-components/agent-context-interface-aci/core-capabilities.md).

# Core Capabilities

#### 1. **Context Querying**

Agents can query any available on-chain context using:

* Direct key-value access
* Graph-based traversal
* Schema-aware search

Example:

```python
import requests

requests.get("https://api.dctx.ai/context/user.reputation.score", params={"address": "0xabc..."}).json()
```

#### 2. **Context Subscription**

Agents can subscribe to updates for specific entities or schema types, enabling real-time reactions.

Example:

```python
# Simulated polling or event listener
while True:
    signal = requests.get("https://api.dctx.ai/context/market.signal.volatility", params={"token": "ETH"}).json()
    if signal.get("alert") == True:
        print("⚠️ Volatility spike detected!")
    time.sleep(5)
```

#### 3. **Context Injection into AI Reasoning**

Decontext allows context data to be injected directly into:

* LLM prompts
* Strategy modules
* Multi-agent communication channels

Example Prompt:

```
context = requests.get("https://api.dctx.ai/context/user.reputation.score", params={"address": "0xabc..."}).json()

prompt = f"User 0xABC has {context['score']} reputation and {context['votes']} governance votes in the last 30 days."
```

#### 4. **Behavioral Triggers**

Agents can bind behaviors to context events using rule sets:

* “If wallet stake > 10 ETH AND behavior score > 80, initiate high-tier offer.”
* “If DAO proposal passes, trigger knowledge sync.”