Workflow · saas · workflow
Cursor's dynamic context discovery reduces agent token usage by 46.9% for MCP tool runs
Coding agents using static context include all tool descriptions and long tool responses regardless of relevance, causing context bloat, potential data loss from truncation, and quality degradation after lossy summarization.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Long tool output written to file
Long tool responses are written to a file so the agent can read only what it needs.
Tools used
CursorMCPAgent Skillsgreptailsemantic search
Outcome
Dynamic context discovery reduced total agent tokens by 46.9% for runs that called an MCP tool and resulted in fewer unnecessary summarizations, while giving the agent the ability to recover details from chat history after summarization.
What failed first
Truncating long tool responses causes data loss, and statically including all MCP tool descriptions bloats the context window even though most tools go unused in any given run.
Results
Volume46.9%
Grounding & classification
Source type: technical build writeup
27 fields verified against source quotes, 2 dropped as unverifiable.
agentic workflowai agentknowledge searchragsummarizationchat transcriptcode diff prknowledge basefailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecost reductionemployee productivitytechnical build writeupagentic task executionrag answering