CCAR-F Tool Design and MCP Integration Practice Question
An architect is reviewing an MCP server where one tool returns a 2 MB JSON payload of raw database rows. The model's responses have become slow and occasionally drop details from the middle of the payload. Which change best addresses the observed behavior while preserving the tool's usefulness?
⚠ Common exam trap
The trap here is equating a larger context window with the ability to use very large tool results effectively, when relevance and signal density matter more than raw capacity.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Return a summarized or paginated subset of the rows, with the tool schema exposing parameters for filtering and page size.
Oversized tool results degrade latency and cause the model to lose details buried in low-signal data. Returning a summarized or paginated subset, with schema parameters that let the model filter and page, keeps each result within a size the model can attend to while preserving access to the full dataset on demand. Expanding context, compressing bytes, or deferring to a resource does not reduce the semantic volume the model must process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Return a summarized or paginated subset of the rows, with the tool schema exposing parameters for filtering and page size.
Why this is correct
Returning a bounded, relevant subset keeps the payload within a size the model can attend to reliably, and exposing filter and pagination parameters lets the model request more data when needed. This preserves the tool's usefulness while eliminating the middle-loss and latency caused by oversized results. It also aligns response size with the query's actual intent.
- ✗
Increase the model's context window and continue returning the full 2 MB payload on every call.
Why it's wrong here
A larger context window raises cost and latency for every subsequent turn and does not fix the tendency to lose details buried in a very long, low-signal payload. The raw rows still consume disproportionate attention relative to their relevance. Trimming or summarizing the payload is the more direct remedy than expanding the window.
- ✗
Compress the 2 MB payload with gzip and return the binary blob in the tool result.
Why it's wrong here
Compression reduces bytes on the wire but the model still must interpret the content, and binary blobs are not directly usable as tool result content. It adds a decompression step without reducing the semantic volume the model has to process. The latency and detail-loss problems remain because the underlying result is still enormous.
- ✗
Move the payload into an MCP resource and have the tool return only a resource URI for the model to fetch.
Why it's wrong here
Resources are appropriate for data the user or application attaches to the conversation, not for large query results the model must reason over immediately. Returning a URI still requires a fetch and does not bound the size the model eventually reads. This shifts the problem rather than reducing the payload the model must handle.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
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