renames mcp pipeline tools (#3238)

Author: rudolfix

dlt/_workspace/mcp/tools/mcp_tools.py

@@ -193,22 +193,22 @@ def register_with(self, mcp_server: FastMCP) -> None:
         pipeline_name = self.pipeline.pipeline_name
         mcp_server.add_tool(
             self.available_tables,
-            name=f"available_tables_in_pipeline_{pipeline_name}",
+            name="available_tables",
             description=f"All available tables in the pipeline {pipeline_name}",
         )
         mcp_server.add_tool(
             self.table_head,
-            name=f"table_head_in_pipeline_{pipeline_name}",
+            name="table_head",
             description=f"Get the first 10 rows of the table in the pipeline {pipeline_name}",
         )
         mcp_server.add_tool(
             self.table_schema,
-            name=f"table_schema_in_pipeline_{pipeline_name}",
+            name="table_schema",
             description=f"Get the schema of the table in the pipeline {pipeline_name}",
         )
         mcp_server.add_tool(
             self.query_sql,
-            name=f"query_sql_in_pipeline_{pipeline_name}",
+            name="query_sql",
             description=(
                 f"Executes sql statement on a given pipeline {pipeline_name} as returns the result "
                 "as | delimited csv. Use this tool for simple analysis where the number of rows is "
@@ -219,7 +219,7 @@ def register_with(self, mcp_server: FastMCP) -> None:
         )
         mcp_server.add_tool(
             self.bookmark_sql,
-            name=f"bookmark_sql_in_pipeline_{pipeline_name}",
+            name="bookmark_sql",
             description=(
                 f"Executes sql statement on a pipeline {pipeline_name} and bookmarks it under "
                 "given bookmark for further processing. Use this tool when you need to select "

docs/website/docs/hub/features/mcp-server.md

@@ -4,6 +4,106 @@ description: Install the dlt MCP with your preferred LLM-enabled IDE.
 keywords: [mcp, llm, agents, ai]
 ---
 
+# Workspace MCP Server - current status
+
+The server can do the following:
+- list pipelines in workspace
+- inspect table schema and data for dataset in particular pipeline
+- do sql queries
+
+It is the same server that is called **the open-source `dlt`** in the documentation below.
+
+Since all mcp clients work with `sse` transport, it is the default when running the server. Before we were struggling with
+launching `mcp` as a part of client process. There was no way to pass right Python virtual environment and dlt run context.
+There were also issues with `stdio` pollution from `print` statement (overall that was IMO a dead end, mcp is a server by nature.)
+
+To launch the server in workspace context:
+```sh
+dlt workspace mcp
+
+INFO:     Started server process [24925]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://127.0.0.1:43654 (Press CTRL+C to quit)
+```
+Workspace mcp server has **43654** as default port and is configured without any path (ie `/sse`) so user can just copy the link above in the right
+client.
+
+To launch the server in pipeline context:
+```sh
+dlt pipeline fruitshop mcp
+
+Starting dlt MCP server
+INFO:     Started server process [28972]
+INFO:     Waiting for application startup.
+INFO:     Application startup complete.
+INFO:     Uvicorn running on http://127.0.0.1:43656 (Press CTRL+C to quit)
+
+```
+Pipeline mcp server has **43656** as default port. Pipeline is already attached when mcp server starts. Both pipeline and workspace mcps can work side by side.
+
+
+Example client configurations
+
+Cursor, Cline
+```json
+{
+  "mcpServers": {
+    "dlt-workspace": {
+      "url": "http://127.0.0.1:43654/"
+    },
+    "dlt-pipeline-mcp": {
+      "url": "http://127.0.0.1:43656/"
+    }
+  }
+}
+```
+
+Continue: for some reason it does see mcp configuration created in dev container. Maybe someone will make it work...
+```yaml
+name: dlt mcps
+version: 0.0.1
+schema: v1
+mcpServers:
+  - name: dlt-workspace
+    type: sse
+    url: "http://localhost:43654"
+```
+
+## Configuration
+Server can still be started with `stdio` transport and different port using the command line. The plan is to allow to configure
+mcp deeply via dlt configuration system.
+
+```toml
+[workspace.mcp]
+path="/sse"
+port=888
+```
+
+```toml
+[pipelines.fruitshop.mcp]
+transport="stdio"
+```
+
+## Interactions with Runtime
+This is a heads-up on how we host mcps on runtime. To be deleted.
+
+* deployed workspace dashboard has two routes `/app` to see the notebook and `/mcp` to connect to mcp server
+* workspace dashboard in single pipeline mode `/app/fruitshop` and `/mcp/fruitshop`
+* I'm also pondering exposing some kind of mcp attached to each marimo notebook
+
+
+# Project MCP server
+
+This is our "project" mcp (**integrates with `dltHub` features** below) and can be launched with:
+```sh
+dlt project mcp
+```
+It gets **43655** port and project context is obtained before launching the server.
+
+
+
+
 # MCP Server
 
 Currently, dltHub is [building two MCP servers](https://dlthub.com/blog/deep-dive-assistants-mcp-continue) that you can run locally and integrate with your preferred IDE. One server is for the open-source `dlt` library and the other integrates with `dltHub` features ([Learn more](ai.md)).

tests/workspace/mcp/test_mcp_server.py

@@ -18,11 +18,11 @@ def test_pipeline_mcp_server(pokemon_pipeline_context: RunContextBase) -> None:
     tools = asyncio.run(mcp.list_tools())
 
     tool_names_expected = [
-        "available_tables_in_pipeline_rest_api_pokemon",
-        "table_head_in_pipeline_rest_api_pokemon",
-        "table_schema_in_pipeline_rest_api_pokemon",
-        "query_sql_in_pipeline_rest_api_pokemon",
-        "bookmark_sql_in_pipeline_rest_api_pokemon",
+        "available_tables",
+        "table_head",
+        "table_schema",
+        "query_sql",
+        "bookmark_sql",
         "read_result_from_bookmark",
         "recent_result",
     ]