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Add Gemini CLI extension doc, troubleshooting doc and refactor (#652)
* remove references to dataclass objects
* Fix a copy-paste error.
* remove extra file
* Create placeholders for LLM pages
* rename to MCP
* complete rename
* remove old file
* Changes from Keyur
* Add target tag
* Rewording suggested by Keyur
* Remove extraneous text
* Fix a copy-paste error.
* Migrate MCP docs to docsite
* Restructuring
* Remove unused file
* Add TOCs etc.
* Update var formatting
* comments from Keyur
* another change from Keyur
* Update mcp/index.md
Co-authored-by: Christie Ellks <[email protected]>
* Changes from Christie
* Apply suggestion from @clincoln8
Co-authored-by: Christie Ellks <[email protected]>
* More changes from Christie
* Changes from Dan
* Link fixes
* Remove MCP inspector info
* Add more context to agent dev doc
* fix formatting
* Create placeholders for LLM pages
* merge
* Slight rewording based on comment from Christie
* Lots of restructuring
* More work on Gemini CLI extension
* More changes
* more changes
* more changes
* Update mcp/run_tools.md
Co-authored-by: Christie Ellks <[email protected]>
* implement suggestions from Christie
* Add some bullets to comparison section
* Fix update section
* rename "prompt" to "context"
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Co-authored-by: Christie Ellks <[email protected]>
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@@ -15,7 +15,7 @@ has_children: true
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The Data Commons [Model Context Protocol (MCP)](https://modelcontextprotocol.io/docs/getting-started/intro) service gives AI agents access to the Data Commons knowledge graph and returns data related to statistical variables, topics, and observations. It allows end users to formulate complex natural-language queries interactively, get data in textual, structured or unstructured formats, and download the data as desired. For example, depending on the agent, a user can answer high-level questions such as "give me the economic indicators of the BRICS countries", view simple tables, and download a CSV file of the data in tabular format.
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The MCP server returns data from datacommons.org by default or can be configured for a Custom Data Commons instance.
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The MCP server returns data from datacommons.org by default or can be configured to query a Custom Data Commons instance.
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The server is a Python binary based on the [FastMCP 2.0 framework](https://gofastmcp.com). A prebuilt package is available at <https://pypi.org/project/datacommons-mcp/>.
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- Stdio: For clients that connect directly using local processes
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- Streamable HTTP: For clients that connect remotely or otherwise require HTTP (e.g. Typescript)
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See [Run and connect to the server](run_tools.md) for procedures for using [Gemini CLI](https://github.com/google-gemini/gemini-cli).
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See [Run MCP tools](run_tools.md) for procedures for using [Gemini CLI](https://github.com/google-gemini/gemini-cli) and the [Gemini CLI Data Commons Extension](https://geminicli.com/extensions/).
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## Unsupported features
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At the current time, the following are not supported:
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- Non-geographical ("custom") entities
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- Events
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- Exploring nodes and relationships in the graph
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- Returning data formatted for graphic visualizations
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