Add LLM usage examples for Reactome MCP tools

examples/llm_usage.md

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+# Using Reactome MCP with LLMs
+
+This document demonstrates how Reactome MCP tools can be used within LLM-based workflows, such as agent systems and retrieval-augmented pipelines.
+
+The goal is to illustrate how natural language queries can be mapped to MCP tool calls and how results can be composed into structured responses.
+
+---
+
+## Example 1: Pathway Search
+
+**User query:**
+What pathways are related to cancer?
+
+**LLM Action:**
+
+- Select tool: `search_pathways`
+- Input: `"cancer"`
+
+**Expected Result:**
+
+- List of pathways related to cancer
+- Pathway identifiers
+- Descriptions and summaries
+
+**LLM Response (example):**
+The following pathways are associated with cancer:
+
+- Pathway A — Description...
+- Pathway B — Description...
+
+---
+
+## Example 2: Gene to Pathway Mapping
+
+**User query:**
+Which pathways involve TP53?
+
+**LLM Action:**
+
+- Select tool: `get_pathways_by_gene`
+- Input: `"TP53"`
+
+**Expected Result:**
+
+- Pathways involving TP53
+- Associated biological processes
+
+**LLM Response (example):**
+TP53 is involved in pathways related to cell cycle regulation, apoptosis, and DNA damage response.
+
+---
+
+## Example 3: Multi-step Reasoning
+
+**User query:**
+Compare pathways involved in cancer and apoptosis.
+
+**LLM Steps:**
+
+1. Call `search_pathways("cancer")`
+2. Call `search_pathways("apoptosis")`
+3. Aggregate and compare results
+
+**LLM Response (example):**
+Cancer and apoptosis share overlapping pathways such as those related to cell survival and programmed cell death.
+
+---
+
+## Example 4: LangChain Integration
+
+Below is a minimal example showing how Reactome APIs can be wrapped as tools and used in an LLM agent.
+
+```python
+from langchain.tools import tool
+from langchain.agents import initialize_agent
+from langchain.chat_models import ChatOpenAI
+import requests
+
+@tool
+def search_pathways(query: str):
+    """Search biological pathways in Reactome"""
+    url = f"https://reactome.org/ContentService/search/query?q={query}"
+    response = requests.get(url)
+    return response.json()
+
+llm = ChatOpenAI(temperature=0)
+
+tools = [search_pathways]
+
+agent = initialize_agent(
+    tools,
+    llm,
+    agent="zero-shot-react-description",
+    verbose=True
+)
+
+agent.run("What pathways are related to cancer?")