JSON Mapping & Context MCP Servers

This repo hosts two small MCP servers that showcase schema-aware JSON exploration and live data retrieval:

JSON Mapping Finder (json_mapping_server.py): Upload any JSON sample, flatten its schema, and use heuristic search (token overlap + fuzzy matching) to find paths for target field names. Great for accelerating ETL/API onboarding.

It is intentionally lightweight and fully HTTP-based for easy inspection with the MCP Inspector or Copilot Chat.

Highlights

Schema-aware parsing: Flattens nested JSON, captures types/examples, and tracks depth.
Heuristic mapping: Token overlap, substring checks, and fuzzy matching to suggest likely field paths.
Plug-and-play MCP: Uses StreamableHTTPSessionManager for modern MCP HTTP transport.
Portable: Pure Python, no external services beyond Open-Meteo.

Quick Start

Prereqs: Python 3.12+ and a virtual environment (.venv recommended).

python -m venv .venv source .venv/bin/activate pip install -r <(python - <<'PY' import tomllib, sys deps = tomllib.load(open("pyproject.toml","rb"))["project"]["dependencies"] print("\n".join(deps)) PY)

Run the JSON Mapping Finder (port 3004)

.venv/bin/python json_mapping_server.py

Then inspect with:

npx -y @modelcontextprotocol/inspector http://localhost:3004

Exposed tools:

upload_json_sample(json_data): load a JSON sample (e.g., sample_json.json) and build the schema index.
list_schema(limit=200): view flattened paths with type + example values.
search_fields(query, top_k=10): find likely paths for a single query.
map_targets(targets, top_k=5): bulk mapping suggestions for multiple field names.
clear_samples(): reset the index.

Sample Data

sample_json.json: a non-medical, nested sample for testing the JSON Mapping Finder.

Configuration

If you want to wire these into Copilot Chat, add entries like:

{ "mcpServers": { "json-mapping": { "url": "http://localhost:3004" } } }

How it works (JSON Mapping Finder)

Indexing: Walks objects/arrays, records paths ($.foo.bar[*]), types, example values, and depth.
Scoring: Combines exact/substring boosts, Jaccard token overlap, and fuzzy ratio; lightly penalizes deep paths.
Suggestions: Returns top matches with scores so you can review/accept quickly.