updated deps

Author: fredericlepied

NEWS.md

@@ -1,5 +1,29 @@
 # DCI MCP Server - Change Log
 
+## [2026-01-27]
+
+### Improvements
+
+- Simplified prompt implementations to use FastMCP's modern API (direct string returns instead of PromptMessage/TextContent wrappers)
+
+### Dependencies
+
+- Updated all project dependencies to their latest versions:
+  - black: 25.1.0 → 26.1.0
+  - ruff: 0.12.8 → 0.14.14
+  - pytest: 8.4.1 → 9.0.2
+  - fastapi: 0.116.1 → 0.128.0
+  - fastmcp: 2.11.3 → 2.14.4
+  - mcp: 1.12.4 → 1.26.0
+  - pydantic: 2.11.7 → 2.12.5
+  - isort: 6.0.1 → 7.0.0
+  - cryptography: 45.0.6 → 46.0.3
+  - dciclient: 4.1.0.post202509100810 → 4.1.0.post202510221005
+  - google-api-python-client: 2.181.0 → 2.188.0
+  - uvicorn: 0.35.0 → 0.40.0
+  - pre-commit: 4.3.0 → 4.5.1
+  - And many other dependency updates
+
 ## [2025-11-21]
 
 ### Improvements

mcp_server/prompts/prompts.py

@@ -1,5 +1,5 @@
 #
-# Copyright (C) 2025 Red Hat, Inc.
+# Copyright (C) 2025-2026 Red Hat, Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may
 # not use this file except in compliance with the License. You may obtain
@@ -17,8 +17,6 @@
 
 from typing import Annotated
 
-from fastmcp.prompts.prompt import PromptMessage, TextContent
-
 
 def register_prompts(mcp):
     """Register prompts with the MCP server."""
@@ -28,14 +26,14 @@ async def rca(
         dci_job_id: str = Annotated[
             str, "The DCI job ID for which to perform root cause analysis (RCA)."
         ],
-    ) -> PromptMessage:
+    ) -> str:
         """
         Prompt for instructions on how to do a Root Cause Analysis (RCA) of a failing DCI job. Always use this prompt when analysing a failing DCI job.
 
         Returns:
             A prompt message with instructions on how to perform RCA of a failing DCI job.
         """
-        content = f"""Conduct a root cause analysis (RCA) on the following DCI job: {dci_job_id}. Store all the downloaded files at /tmp/dci/<job id>, so as not to download them twice. Create a report with your findings at /tmp/dci/rca-<job id>.md. Be sure to include details about the timeline of events and the DCI job information in the report, such as the components, the topic, and the pipeline name. If there is a CILAB-<num> comment, replace it with https://issues.redhat.com/browse/CILAB-<num>. Include a hyperlink each time you refer to the DCI job ID.
+        return f"""Conduct a root cause analysis (RCA) on the following DCI job: {dci_job_id}. Store all the downloaded files at /tmp/dci/<job id>, so as not to download them twice. Create a report with your findings at /tmp/dci/rca-<job id>.md. Be sure to include details about the timeline of events and the DCI job information in the report, such as the components, the topic, and the pipeline name. If there is a CILAB-<num> comment, replace it with https://issues.redhat.com/browse/CILAB-<num>. Include a hyperlink in the form https://distributed-ci.io/jobs/<job id> each time you refer to the DCI job ID.
 
 First step is to review ansible.log (overview of the CI job execution). Then the logjuicer.txt (for regular files) and logjuicer_omg.txt (for must_gather) files that compare the logs from a previous successful run.
 
@@ -49,65 +47,54 @@ async def rca(
 
 Check it the associated JIRA ticket is consistent with your findings.
 """
-        return PromptMessage(
-            role="user", content=TextContent(type="text", text=content)
-        )
 
     @mcp.prompt()
     async def weekly(
         subject: str = Annotated[
             str, "The subject of the analysis (team name or id, remoteci name or id)."
         ],
-    ) -> PromptMessage:
+    ) -> str:
         """
         Prompt for instructions on how to analyze DCI jobs for a week.
 
         Returns:
             A prompt message with instructions on how to analyze DCI jobs for a week.
         """
-        content = f"""Analyze the DCI jobs for the last week for {subject}. Provide a summary of the number of jobs, the number of failures, and the failure rate. Identify the top 3 reasons for failures and provide recommendations for improvement. If there are any CILAB-<num> comments, replace them with https://issues.redhat.com/browse/CILAB-<num>. Include hyperlinks each time you refer to a DCI job ID.
+        return f"""Analyze the DCI jobs for the last week for {subject}. Provide a summary of the number of jobs, the number of failures, and the failure rate. Identify the top 3 reasons for failures and provide recommendations for improvement. If there are any CILAB-<num> comments, replace them with https://issues.redhat.com/browse/CILAB-<num>. Include hyperlinks in the form https://distributed-ci.io/jobs/<job id> each time you refer to a DCI job ID.
 
 Create a report with your findings in the /tmp/dci directory (create the directory if it doesn't exist). Be sure to include a summary,  statistics and anomaly detection if applicable. Use markdown formatting for the report.
         """
 
-        return PromptMessage(
-            role="user", content=TextContent(type="text", text=content)
-        )
-
     @mcp.prompt()
     async def biweekly(
         subject: str = Annotated[
             str, "The subject of the analysis (team name or id, remoteci name or id)."
         ],
-    ) -> PromptMessage:
+    ) -> str:
         """
         Prompt for instructions on how to analyze DCI jobs for 2 weeks.
 
         Returns:
             A prompt message with instructions on how to analyze DCI jobs for a week.
         """
-        content = f"""Analyze the DCI jobs for the last 2 weeks for {subject}. Provide a summary of the number of jobs, the number of failures, and the failure rate. Identify the top 3 reasons for failures and provide recommendations for improvement. If there are any CILAB-<num> comments, replace them with https://issues.redhat.com/browse/CILAB-<num>. Include hyperlinks each time you refer to a DCI job ID.
+        return f"""Analyze the DCI jobs for the last 2 weeks for {subject}. Provide a summary of the number of jobs, the number of failures, and the failure rate. Identify the top 3 reasons for failures and provide recommendations for improvement. If there are any CILAB-<num> comments, replace them with https://issues.redhat.com/browse/CILAB-<num>. Include hyperlinks in the form https://distributed-ci.io/jobs/<job id> each time you refer to a DCI job ID.
 
 Create a report with your findings in the /tmp/dci directory (create the directory if it doesn't exist). Be sure to include a summary, statistics and anomaly detection if applicable. Use markdown formatting for the report.
         """
 
-        return PromptMessage(
-            role="user", content=TextContent(type="text", text=content)
-        )
-
     @mcp.prompt()
     async def quarterly(
         subject: str = Annotated[
             str, "The subject of the analysis (remoteci name or id)."
         ],
-    ) -> PromptMessage:
+    ) -> str:
         """
         Prompt for instructions on how to analyze DCI jobs for a quarter (3 months).
 
         Returns:
             A prompt message with instructions on how to analyze DCI jobs for a quarter.
         """
-        content = f"""Analyze the DCI jobs for the last quarter (3 months) for {subject}. Due to the large volume of data, you must use a multi-step approach with caching to avoid exhausting the context window.
+        return f"""Analyze the DCI jobs for the last quarter (3 months) for {subject}. Due to the large volume of data, you must use a multi-step approach with caching to avoid exhausting the context window.
 
 ## Step 1: Data Collection and Caching
 
@@ -256,9 +243,6 @@ async def quarterly(
 - Prioritize statistics that reveal patterns and trends
 - Be thorough but concise in the report
 """
-        return PromptMessage(
-            role="user", content=TextContent(type="text", text=content)
-        )
 
 
 # prompts.py ends here