Travel Assistant MCP Server
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
AI Agent Based Travel Assistant Claude API + MCP
What exists here
Piece | File | What it does |
Sandbox data layer |
| Loads TravelPlanner queries, parses |
MCP server |
| Exposes the sandbox as MCP tools over stdio |
Agent loop |
| Claude tool-use loop driving the MCP session |
Prompts |
| Versioned system prompts (planner, sole-planning, chat) |
Plan schema |
| Pydantic contract matching the benchmark's plan format |
Evaluator |
| Commonsense + hard constraint checks, benchmark-style metrics |
Batch runner |
| Runs N queries, writes traces + a metrics summary |
UI |
| Streamlit chat for participant testing |
Tests |
| 9 offline tests, no API key needed |
Setup (30 minutes)
python -m venv .venv
source .venv/bin/activate # Windows cmd: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env
python scripts/prepare_data.py
pytest -q # 9 tests, no API calls
python scripts/check_setup.py # key, dataset, MCP server, one API callThen, first real run. Use run.py and everything works the same on every
platform -- no PYTHONPATH to set:
python run.py eval.run_eval --mode sole-planning --n 5 --level easy # cheap, ~1 min
python run.py agent.loop 0 # one agentic run, prints the full trace
python run.py eval.run_eval --mode tool-use --n 5 --level easy
streamlit run src/ui/app.pyArchitecture
Streamlit UI ──┐
├──► agent/loop.py ──── Anthropic Messages API (Claude)
eval runner ───┘ │ ▲
│ tool_use blocks │ tool_result blocks
▼ │
MCP ClientSession ────────────┘
│ stdio (JSON-RPC)
▼
tp_mcp/server.py (FastMCP)
│
▼
TravelPlanner sandbox (reference_information / CSV database)Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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This server cannot be deployed
Maintenance
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