torbjorn/my-pal-mcp-server
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: https://github.com/BeehiveInnovations/zen-mcp-server/issues/194

Browse Source
This commit is contained in:
Fahad
2025-10-02 23:12:52 +04:00
parent 547feb26a8
commit 8b3a2867fb
3 changed files with 80 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files

32
tools/shared/base_tool.py
View File

@@ -21,7 +21,7 @@ if TYPE_CHECKING:
from config import MCP_PROMPT_SIZE_LIMIT
from providers import ModelProvider, ModelProviderRegistry
from utils import check_token_limit
from utils import estimate_tokens
from utils.conversation_memory import (
ConversationTurn,
get_conversation_file_list,
@@ -647,22 +647,38 @@ class BaseTool(ABC):
def _validate_token_limit(self, content: str, content_type: str = "Content") -> None:
"""
Validate that content doesn't exceed the MCP prompt size limit.
Validate that user-provided content doesn't exceed the MCP prompt size limit.
This enforcement is strictly for text crossing the MCP transport boundary
(i.e., user input). Internal prompt construction may exceed this size and is
governed by model-specific token limits.
Args:
content: The content to validate
content: The user-originated content to validate
content_type: Description of the content type for error messages
Raises:
ValueError: If content exceeds size limit
ValueError: If content exceeds the character size limit
"""
is_valid, token_count = check_token_limit(content, MCP_PROMPT_SIZE_LIMIT)
if not is_valid:
error_msg = f"~{token_count:,} tokens. Maximum is {MCP_PROMPT_SIZE_LIMIT:,} tokens."
if not content:
logger.debug(f"{self.name} tool {content_type.lower()} validation skipped (no content)")
return
char_count = len(content)
if char_count > MCP_PROMPT_SIZE_LIMIT:
token_estimate = estimate_tokens(content)
error_msg = (
f"{char_count:,} characters (~{token_estimate:,} tokens). "
f"Maximum is {MCP_PROMPT_SIZE_LIMIT:,} characters."
)
logger.error(f"{self.name} tool {content_type.lower()} validation failed: {error_msg}")
raise ValueError(f"{content_type} too large: {error_msg}")
logger.debug(f"{self.name} tool {content_type.lower()} token validation passed: {token_count:,} tokens")
token_estimate = estimate_tokens(content)
logger.debug(
f"{self.name} tool {content_type.lower()} validation passed: "
f"{char_count:,} characters (~{token_estimate:,} tokens)"
)
def get_model_provider(self, model_name: str) -> ModelProvider:
"""