Files
AutoGPT/autogpt_platform/backend/backend/copilot/tools/web_fetch.py
Zamil Majdy 7176cecf25 perf(copilot): reduce tool schema token cost by 34% (#12398)
## Summary

Reduce CoPilot per-turn token overhead by systematically trimming tool
descriptions, parameter schemas, and system prompt content. All 35 MCP
tool schemas are passed on every SDK call — this PR reduces their size.

### Strategy

1. **Tool descriptions**: Trimmed verbose multi-sentence explanations to
concise single-sentence summaries while preserving meaning
2. **Parameter schemas**: Shortened parameter descriptions to essential
info, removed some `default` values (handled in code)
3. **System prompt**: Condensed `_SHARED_TOOL_NOTES` and storage
supplement template in `prompting.py`
4. **Cross-tool references**: Removed duplicate workflow hints (e.g.
"call find_block before run_block" appeared in BOTH tools — kept only in
the dependent tool). Critical cross-tool references retained (e.g.
`continue_run_block` in `run_block`, `fix_agent_graph` in
`validate_agent`, `get_doc_page` in `search_docs`, `web_fetch`
preference in `browser_navigate`)

### Token Impact

| Metric | Before | After | Reduction |
|--------|--------|-------|-----------|
| System Prompt | ~865 tokens | ~497 tokens | 43% |
| Tool Schemas | ~9,744 tokens | ~6,470 tokens | 34% |
| **Grand Total** | **~10,609 tokens** | **~6,967 tokens** | **34%** |

Saves **~3,642 tokens per conversation turn**.

### Key Decisions

- **Mostly description changes**: Tool logic, parameters, and types
unchanged. However, some schema-level `default` fields were removed
(e.g. `save` in `customize_agent`) — these are machine-readable
metadata, not just prose, and may affect LLM behavior.
- **Quality preserved**: All descriptions still convey what the tool
does and essential usage patterns
- **Cross-references trimmed carefully**: Kept prerequisite hints in the
dependent tool (run_block mentions find_block) but removed the reverse
(find_block no longer mentions run_block). Critical cross-tool guidance
retained where removal would degrade model behavior.
- **`run_time` description fixed**: Added missing supported values
(today, last 30 days, ISO datetime) per review feedback

### Future Optimization

The SDK passes all 35 tools on every call. The MCP protocol's
`list_tools()` handler supports dynamic tool registration — a follow-up
PR could implement lazy tool loading (register core tools + a discovery
meta-tool) to further reduce per-turn token cost.

### Changes

- Trimmed descriptions across 25 tool files
- Condensed `_SHARED_TOOL_NOTES` and `_build_storage_supplement` in
`prompting.py`
- Fixed `run_time` schema description in `agent_output.py`

### Checklist

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] All 273 copilot tests pass locally
  - [x] All 35 tools load and produce valid schemas
  - [x] Before/after token dumps compared
  - [x] Formatting passes (`poetry run format`)
  - [x] CI green
2026-03-23 08:27:24 +00:00

144 lines
4.2 KiB
Python

"""Web fetch tool — safely retrieve public web page content."""
import logging
from typing import Any
import aiohttp
import html2text
from backend.copilot.model import ChatSession
from backend.util.request import Requests
from .base import BaseTool
from .models import ErrorResponse, ToolResponseBase, WebFetchResponse
logger = logging.getLogger(__name__)
# Limits
_MAX_CONTENT_BYTES = 102_400 # 100 KB download cap
_REQUEST_TIMEOUT = aiohttp.ClientTimeout(total=15)
# Content types we'll read as text
_TEXT_CONTENT_TYPES = {
"text/html",
"text/plain",
"text/xml",
"text/csv",
"text/markdown",
"application/json",
"application/xml",
"application/xhtml+xml",
"application/rss+xml",
"application/atom+xml",
# RFC 7807 — JSON problem details; used by many REST APIs for error responses
"application/problem+json",
"application/problem+xml",
"application/ld+json",
}
def _is_text_content(content_type: str) -> bool:
base = content_type.split(";")[0].strip().lower()
return base in _TEXT_CONTENT_TYPES or base.startswith("text/")
def _html_to_text(html: str) -> str:
h = html2text.HTML2Text()
h.ignore_links = False
h.ignore_images = True
h.body_width = 0
return h.handle(html)
class WebFetchTool(BaseTool):
"""Safely fetch content from a public URL using SSRF-protected HTTP."""
@property
def name(self) -> str:
return "web_fetch"
@property
def description(self) -> str:
return "Fetch a public web page. Public URLs only — internal addresses blocked. Returns readable text from HTML by default."
@property
def parameters(self) -> dict[str, Any]:
return {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "Public HTTP/HTTPS URL.",
},
"extract_text": {
"type": "boolean",
"description": "Extract text from HTML (default: true).",
"default": True,
},
},
"required": ["url"],
}
@property
def requires_auth(self) -> bool:
return False
async def _execute(
self,
user_id: str | None,
session: ChatSession,
**kwargs: Any,
) -> ToolResponseBase:
url: str = (kwargs.get("url") or "").strip()
extract_text: bool = kwargs.get("extract_text", True)
session_id = session.session_id if session else None
if not url:
return ErrorResponse(
message="Please provide a URL to fetch.",
error="missing_url",
session_id=session_id,
)
try:
client = Requests(raise_for_status=False, retry_max_attempts=1)
response = await client.get(url, timeout=_REQUEST_TIMEOUT)
except ValueError as e:
# validate_url raises ValueError for SSRF / blocked IPs
return ErrorResponse(
message=f"URL blocked: {e}",
error="url_blocked",
session_id=session_id,
)
except Exception as e:
logger.warning(f"[web_fetch] Request failed for {url}: {e}")
return ErrorResponse(
message=f"Failed to fetch URL: {e}",
error="fetch_failed",
session_id=session_id,
)
content_type = response.headers.get("content-type", "")
if not _is_text_content(content_type):
return ErrorResponse(
message=f"Non-text content type: {content_type.split(';')[0]}",
error="unsupported_content_type",
session_id=session_id,
)
raw = response.content[:_MAX_CONTENT_BYTES]
text = raw.decode("utf-8", errors="replace")
if extract_text and "html" in content_type.lower():
text = _html_to_text(text)
return WebFetchResponse(
message=f"Fetched {url}",
url=response.url,
status_code=response.status,
content_type=content_type.split(";")[0].strip(),
content=text,
truncated=False,
session_id=session_id,
)