Handling Token Limitations for Complex Queries

Note:This section applies to advanced scenarios.

When executing complex or large-scale queries through Revenera MCP Server, users may encounter failures due to the context window (token limit) of the selected LLM during downstream processing. These limitations typically arise because responses are verbose in nature and are not pre-processed to optimize LLM consumption.

To address this constraint, it is recommended that publishers implement an intermediate orchestration layer within their own environment (for example, using Copilot Studio or equivalent tooling). This approach is a publisher-side enhancement and is not part of the Revenera-managed infrastructure.

Orchestration Layer Overview

The orchestration layer acts as an intelligent controller between the LLM and Revenera MCP Server, enabling efficient handling of large or complex queries. Its key responsibilities include:

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Query decomposition—Breaking down a complex user request into smaller, manageable sub-queries.

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Iterative retrieval—Sequentially retrieving data for each sub-query.

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Incremental summarization—Reducing the size of each response while preserving relevant insights.

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Aggregation—Combining summarized outputs into a coherent and compact representation.

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Reasoning enablement—Providing the LLM with concise, processed inputs suitable for analysis within token limits.

By ensuring that only compact, insight-ready summaries are passed to the LLM rather than full, verbose responses, this approach significantly improves scalability and reduces the likelihood of token overflow errors.

Processing Pattern

The recommended execution workflow can be summarized as shown below:

Recommended Query Processing Workflow

Adopting this orchestration pattern (Decompose > Retrieve > Summarize > Aggregate > Reason) allows publishers to reliably support large-scale analytical queries while operating within LLM constraints, without requiring any changes to the Revenera MCP Server itself.