This is my understanding of the two:
- MCPs can help LLM use ‘tool’ and ‘resources’ and combine with knowledge it was trained on to suggest a response to the prompt.
- Post-training is a way to tune LLMs on specialized datasets that are not available online.
Could someone explain to me that if we have those specialized ‘resources’ and ‘tools’ mentioned in MCP, wouldn’t we not need post-training?
The reason I am asking is that we are trying to build out LLM-driven Geographic data analysis, reasoning, recommendations, visualization. Would we post-train LLMS on Geographic database and python code to perform operations, or use the resources and tools as a part of MCP?
Thank you
Understanding Model Context Protocol (MCP) vs Post-Training
When working with LLMs, it’s important to distinguish between Model Context Protocol (MCP) and Post-Training:
Model Context Protocol (MCP)
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MCP allows the model to access external tools and resources at inference time.
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The model uses its pre-trained weights and combines them with contextual data fetched via tools.
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It does not train on the data — it simply reads and reasons over it dynamically.
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Ideal for scenarios where data is frequently changing or too large to embed directly into the model.
Post-Training
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Post-training involves fine-tuning the model on a specialized dataset.
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The model learns and internalizes domain-specific knowledge by updating its weights.
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Useful when the data is static, specialized, and needs to be deeply understood by the model.
When to Use MCP
Consider the following:
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If you want to dynamically retrieve geographic data and perform inference on it — especially if the data is continuously changing (e.g., weather, traffic) — then MCP is the best choice.
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If you lack resources (data, compute, time) for post-training, MCP provides a flexible and scalable alternative.
When to Use post-training:
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Your data is static and specialized.
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You want the model to generate insights without external tools.
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You need deep domain adaptation.