Tool Results Are Too Verbose and Bloat the Context Window
Symptom
- Database query returns all 10,000 records — full result in context
- Web page scrape includes navigation, ads, scripts — 200KB of noise
- Agent reads an entire 5,000-line log file when looking for one error
- Context window fills up after a few tool calls
- Each turn costs 50,000+ tokens mostly from old tool results
Root Cause
Tool results are included verbatim in the conversation context. The model processes all tokens every turn — including tool results from 5 turns ago. Without trimming, filtering, or summarizing tool results, the context fills quickly with data that’s no longer actively needed.
Fix
Option 1: Limit results at the tool level
def search_database(query: str, limit: int = 20) -> dict:
"""
Search database. Returns max 20 results to prevent context bloat.
Use 'offset' parameter to paginate if more results needed.
"""
results = db.execute(query).fetchmany(limit)
total = db.execute(f"SELECT COUNT(*) FROM ({query})").fetchone()[0]
return {
"results": results,
"returned": len(results),
"total": total,
"has_more": total > limit,
"note": f"Showing {len(results)} of {total} results. Use limit/offset to get more."
}
def read_log_file(path: str, search: str = None, tail_lines: int = 100) -> dict:
"""
Read log file. By default returns last 100 lines, not entire file.
Use search parameter to find specific patterns.
"""
lines = open(path).readlines()
if search:
matching = [l for l in lines if search in l]
return {
"query": search,
"matches": matching[:50],
"total_matches": len(matching),
"note": f"Found {len(matching)} lines matching '{search}'"
}
# Return tail only
return {
"lines": lines[-tail_lines:],
"total_lines": len(lines),
"note": f"Showing last {tail_lines} of {len(lines)} lines"
}
Option 2: Compress tool results before adding to context
MAX_TOOL_RESULT_CHARS = 2000 # ~500 tokens
def compress_tool_result(result: str, tool_name: str) -> str:
"""Trim tool result to fit within token budget"""
if len(result) <= MAX_TOOL_RESULT_CHARS:
return result
half = MAX_TOOL_RESULT_CHARS // 2
compressed = (
result[:half] +
f"\n\n[... {len(result) - MAX_TOOL_RESULT_CHARS} characters truncated "
f"(tool: {tool_name}, total: {len(result)} chars) ...]\n\n" +
result[-half:]
)
return compressed
def execute_tool_with_compression(tool_name: str, tool_input: dict) -> str:
result = dispatch_tool(tool_name, tool_input)
result_str = str(result)
return compress_tool_result(result_str, tool_name)
Option 3: Summarize old tool results in context
async def compress_old_tool_results(history: list, agent, keep_recent: int = 2) -> list:
"""Summarize tool results older than N turns"""
tool_result_indices = [
i for i, msg in enumerate(history)
if isinstance(msg.get("content"), list) and
any(block.get("type") == "tool_result" for block in msg.get("content", []))
]
# Keep the most recent N tool results verbatim
old_indices = tool_result_indices[:-keep_recent] if len(tool_result_indices) > keep_recent else []
for idx in old_indices:
content_blocks = history[idx].get("content", [])
for block in content_blocks:
if block.get("type") == "tool_result":
original = str(block.get("content", ""))
if len(original) > 500:
# Summarize this old tool result
summary = await agent.complete([{
"role": "user",
"content": f"Summarize this tool result in 1-2 sentences, keeping key facts:\n{original[:2000]}"
}])
block["content"] = f"[Summarized: {summary}]"
return history
Option 4: Extract only relevant parts of tool results
import re
from bs4 import BeautifulSoup
def extract_relevant_content(tool_result: str, tool_name: str, user_query: str) -> str:
"""Extract only query-relevant content from large tool results"""
if tool_name == "web_scrape":
# Strip HTML, ads, navigation
soup = BeautifulSoup(tool_result, "html.parser")
# Remove script, style, nav elements
for tag in soup(["script", "style", "nav", "header", "footer", "aside"]):
tag.decompose()
text = soup.get_text(separator="\n", strip=True)
# Keep only paragraphs containing relevant keywords
keywords = user_query.lower().split()
relevant_paragraphs = [
p for p in text.split("\n\n")
if any(kw in p.lower() for kw in keywords)
]
return "\n\n".join(relevant_paragraphs[:10]) # Max 10 relevant paragraphs
if tool_name == "read_file" and len(tool_result) > 5000:
# For large files, find relevant section
lines = tool_result.split("\n")
keywords = user_query.lower().split()
relevant_lines = []
for i, line in enumerate(lines):
if any(kw in line.lower() for kw in keywords):
# Include surrounding context
start = max(0, i - 2)
end = min(len(lines), i + 5)
relevant_lines.extend(lines[start:end])
relevant_lines.append("...")
return "\n".join(relevant_lines[:100]) if relevant_lines else tool_result[:2000]
return tool_result
Option 5: Replace old tool results with references
class ToolResultStore:
"""Store full tool results externally, reference them in context"""
def __init__(self):
self._store = {}
self._counter = 0
def save(self, result: str, tool_name: str) -> str:
"""Save result, return compact reference"""
key = f"TOOL_RESULT_{self._counter}"
self._counter += 1
self._store[key] = result
# Return compact summary for context
preview = result[:200]
return (
f"[{key}: {tool_name} result, {len(result)} chars. "
f"Preview: {preview}... "
f"Request full result by name if needed]"
)
def retrieve(self, key: str) -> str:
return self._store.get(key, f"Result {key} not found")
store = ToolResultStore()
# In tool execution:
full_result = execute_tool(tool_name, tool_input)
compact_reference = store.save(full_result, tool_name)
# compact_reference goes into context — full result retrievable when needed
Token Usage by Tool Result Size
| Result size | Tokens (approx) | Impact |
|---|---|---|
| 100 chars | 25 | Negligible |
| 1,000 chars | 250 | Fine |
| 10,000 chars | 2,500 | Significant |
| 100,000 chars | 25,000 | Critical |
| 500,000 chars | 125,000 | Over context limit |
Expected Token Savings
Uncompressed DB result (10K rows): ~50,000 tokens Limit 20 rows + total count: ~500 tokens (99% reduction)
Environment
- Any agent calling database queries, web scraping, or file reading tools
- Source: direct experience; tool result bloat is the #1 cause of unexpected context exhaustion
Wasting tokens on this error?
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