MCP Series (05): Resources and Prompts Deep Dive — Dynamic Data, Parameterized URIs, and Multi-Turn Templates
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Resources vs Tools The split: Tools → actions the LLM executes (verbs) LLM decides when to call; calls may have side effects Examples: create_issue, update_status Resources → data the LLM reads (nouns) Host decides when to inject; read-only, no side effects Examples: current Sprint status, project statistics The rule: "reading a state" → Resource. "Executing an operation" → Tool. The same data can have both: get_issue as a Tool (LLM controls when to call it), jira://issue/PROJ-101 as a Resource…
1Key Takeaways
- The same data can have both: get_issue as a Tool (LLM controls when to call it), jira://issue/PROJ-101 as a Resource….
- Headline: MCP Series (05): Resources and Prompts Deep Dive — Dynamic Data, Parameterized URIs, and Multi-Turn Templates
- Category focus: Prompt Engineering — relevant for AI builders and decision-makers.
2AIWedia Score
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3Why it matters
Prompt and agent patterns spread fast; staying current saves time and token cost. DEV — Prompt Engineering reports that the same data can have both: get_issue as a Tool (LLM controls when to call it), jira://issue/PROJ-101 as a Resource…
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