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Add tool description to tool start event #880

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Add tool description to tool start event #880

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ca2b538
Add tool description to tool start event
moshemorad Aug 20, 2025
1c9ae81
Merge branch 'master' into ROB-1963-add-params-description-to-tool-start
moshemorad Aug 20, 2025
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14 changes: 13 additions & 1 deletion holmes/core/tool_calling_llm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -698,9 +698,21 @@ def call_stream(
tool_number=tool_index,
)
)
tool_params = (
json.loads(t.function.arguments) if t.function.arguments else {}
)
tool = self.tool_executor.get_tool_by_name(t.function.name)
description = (
tool.get_parameterized_one_liner(tool_params) if tool else None
)
Comment on lines +701 to +707
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⚠️ Potential issue

Guard against invalid JSON and custom tool calls in streaming path

Two issues:

  • json.loads on t.function.arguments is unguarded and can raise, breaking streaming.
  • Assumes t.function exists; custom tool calls (non-function) will raise AttributeError. _invoke_tool correctly guards via hasattr; mirror that here.

Apply this safer parsing and resolution:

-                    tool_params = (
-                        json.loads(t.function.arguments) if t.function.arguments else {}
-                    )
-                    tool = self.tool_executor.get_tool_by_name(t.function.name)
-                    description = (
-                        tool.get_parameterized_one_liner(tool_params) if tool else None
-                    )
+                    tool_name = None
+                    tool_params = None
+                    description = None
+                    if hasattr(t, "function"):
+                        tool_name = t.function.name
+                        args_str = getattr(t.function, "arguments", None)
+                        if args_str:
+                            try:
+                                parsed = json.loads(args_str)
+                                tool_params = parsed if isinstance(parsed, dict) else {}
+                            except Exception:
+                                logging.warning(
+                                    f"Failed to parse arguments for tool: {tool_name}. args: {args_str}"
+                                )
+                                tool_params = {}
+                        tool_obj = self.tool_executor.get_tool_by_name(tool_name)
+                        if tool_obj:
+                            try:
+                                description = tool_obj.get_parameterized_one_liner(tool_params or {})
+                            except Exception:
+                                logging.exception(
+                                    f"Failed to compute parameterized description for tool: {tool_name}"
+                                )
+                    else:
+                        logging.error(f"Unsupported custom tool call: {t}")
+                        tool_name = "unknown"
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
tool_params = (
json.loads(t.function.arguments) if t.function.arguments else {}
)
tool = self.tool_executor.get_tool_by_name(t.function.name)
description = (
tool.get_parameterized_one_liner(tool_params) if tool else None
)
tool_name = None
tool_params = None
description = None
if hasattr(t, "function"):
tool_name = t.function.name
args_str = getattr(t.function, "arguments", None)
if args_str:
try:
parsed = json.loads(args_str)
tool_params = parsed if isinstance(parsed, dict) else {}
except Exception:
logging.warning(
f"Failed to parse arguments for tool: {tool_name}. args: {args_str}"
)
tool_params = {}
tool_obj = self.tool_executor.get_tool_by_name(tool_name)
if tool_obj:
try:
description = tool_obj.get_parameterized_one_liner(tool_params or {})
except Exception:
logging.exception(
f"Failed to compute parameterized description for tool: {tool_name}"
)
else:
logging.error(f"Unsupported custom tool call: {t}")
tool_name = "unknown"

yield StreamMessage(
event=StreamEvents.START_TOOL,
data={"tool_name": t.function.name, "id": t.id},
data={
"tool_name": t.function.name,
"id": t.id,
"params": tool_params,
"description": description,
},
Comment on lines +710 to +715
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⚠️ Potential issue

Use computed tool_name and normalized params in START_TOOL payload

Follow-up to the parsing fix: rely on the precomputed variables and avoid direct t.function access. Also normalize params to a dict to keep payload shape stable.

-                        data={
-                            "tool_name": t.function.name,
-                            "id": t.id,
-                            "params": tool_params,
-                            "description": description,
-                        },
+                        data={
+                            "tool_name": tool_name,
+                            "id": t.id,
+                            "params": tool_params or {},
+                            "description": description,
+                        },
📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
data={
"tool_name": t.function.name,
"id": t.id,
"params": tool_params,
"description": description,
},
data={
"tool_name": tool_name,
"id": t.id,
"params": tool_params or {},
"description": description,
},
🤖 Prompt for AI Agents 
In holmes/core/tool_calling_llm.py around lines 710 to 715, the START_TOOL
payload is using t.function.name and raw tool_params directly; update it to use
the precomputed tool_name variable and a normalized params dict (e.g., ensure
params are converted/validated to a plain dict called normalized_params) so the
payload shape stays stable; replace "tool_name": t.function.name with
"tool_name": tool_name and "params": tool_params with "params":
normalized_params (or ensure tool_params is normalized to a dict before use).

)

for future in concurrent.futures.as_completed(futures):
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