Integrate MDN content into API documentation.

utils/apidoc/mdn/README.txt

Index: utils/apidoc/mdn/README.txt
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+Here's a rough walkthrough of how this works. The ultimate output file is
+database.filtered.json.
+
+search.js
+- read data/domTypes.json
+- for each dom type:
+  - search for page on www.googleapis.com
+  - write search results to output/search/<type>.json
+    . this is a list of search results and urls to pages
+
+crawl.js
+- read data/domTypes.json
+- for each dom type:
+  - for each output/search/<type>.json:
+    - for each result in the file:
+      - try to scrape that cached MDN page from webcache.googleusercontent.com
+      - write mdn page to output/crawl/<type><index of result>.html
+- write output/crawl/cache.json
+  . it maps types -> search result page urls and titles
+
+extract.sh
+- compile extract.dart to js
+- run extractRunner.js
+  - read data/domTypes.json
+  - read output/crawl/cache.json
+  - read data/dartIdl.json
+  - for each scraped search result page:
+    - create a cleaned up html page in output/extract/<type><index>.html that
+      contains the scraped content + a script tag that includes extract.dart.js.
+    - create an args file in output/extract/<type><index>.html.json with some
+      data on how that file should be processed
+    - invoke dump render tree on that file
+    - when that returns, parse the console output and add it to database.json
+    - add any errors to output/errors.json
+  - save output/database.json
+
+extract.dart
+- xhr output/extract/<type><index>.html.json
+- all sorts of shenanigans to actually pull the content out of the html
+- build a JSON object with the results
+- do a postmessage with that object so extractRunner.js can pull it out
+
+- run postProcess.dart
+  - go through the results for each type looking for the best match
+  - write output/database.html
+  - write output/examples.html
+  - write output/obsolete.html
+  - write output/database.filtered.json which is the best matches