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my-pal-mcp-server/tools/consensus.py
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perf: tweaks to schema descriptions, aiming to reduce token usage without performance degradation
2025-10-01 22:39:12 +04:00

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"""
Consensus tool - Step-by-step multi-model consensus with expert analysis
This tool provides a structured workflow for gathering consensus from multiple models.
It guides the CLI agent through systematic steps where the CLI agent first provides its own analysis,
then consults each requested model one by one, and finally synthesizes all perspectives.
Key features:
- Step-by-step consensus workflow with progress tracking
- The CLI agent's initial neutral analysis followed by model-specific consultations
- Context-aware file embedding
- Support for stance-based analysis (for/against/neutral)
- Final synthesis combining all perspectives
"""
from __future__ import annotations
import json
import logging
from typing import TYPE_CHECKING, Any
from pydantic import Field, model_validator
if TYPE_CHECKING:
from tools.models import ToolModelCategory
from mcp.types import TextContent
from config import TEMPERATURE_ANALYTICAL
from systemprompts import CONSENSUS_PROMPT
from tools.shared.base_models import WorkflowRequest
from .workflow.base import WorkflowTool
logger = logging.getLogger(__name__)
# Tool-specific field descriptions for consensus workflow
CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS = {
"step": (
"Consensus prompt. Step 1: write the exact proposal/question every model will see (use 'Evaluate…', not meta commentary). "
"Steps 2+: capture internal notes about the latest model response—these notes are NOT sent to other models."
),
"step_number": "Current step index (starts at 1). Step 1 is your analysis; steps 2+ handle each model response.",
"total_steps": "Total steps = number of models consulted plus the final synthesis step.",
"next_step_required": "True if more model consultations remain; set false when ready to synthesize.",
"findings": (
"Step 1: your independent analysis for later synthesis (not shared with other models). Steps 2+: summarize the newest model response."
),
"relevant_files": "Optional supporting files that help the consensus analysis. Must be absolute full, non-abbreviated paths.",
"models": (
"List of models to consult. Each entry may include model, stance (for/against/neutral), and stance_prompt. "
"Each (model, stance) pair must be unique, e.g. [{'model':'o3','stance':'for'}, {'model':'o3','stance':'against'}]."
),
"current_model_index": "0-based index of the next model to consult (managed internally).",
"model_responses": "Internal log of responses gathered so far.",
"images": "Optional absolute image paths or base64 references that add helpful visual context.",
}
class ConsensusRequest(WorkflowRequest):
"""Request model for consensus workflow steps"""
# Required fields for each step
step: str = Field(..., description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["step"])
step_number: int = Field(..., description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["step_number"])
total_steps: int = Field(..., description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["total_steps"])
next_step_required: bool = Field(..., description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["next_step_required"])
# Investigation tracking fields
findings: str = Field(..., description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["findings"])
confidence: str = Field(default="exploring", exclude=True, description="Not used")
# Consensus-specific fields (only needed in step 1)
models: list[dict] | None = Field(None, description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["models"])
relevant_files: list[str] | None = Field(
default_factory=list,
description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["relevant_files"],
)
# Internal tracking fields
current_model_index: int | None = Field(
0,
description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["current_model_index"],
)
model_responses: list[dict] | None = Field(
default_factory=list,
description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["model_responses"],
)
# Optional images for visual debugging
images: list[str] | None = Field(default=None, description=CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["images"])
# Override inherited fields to exclude them from schema
temperature: float | None = Field(default=None, exclude=True)
thinking_mode: str | None = Field(default=None, exclude=True)
# Not used in consensus workflow
files_checked: list[str] | None = Field(default_factory=list, exclude=True)
relevant_context: list[str] | None = Field(default_factory=list, exclude=True)
issues_found: list[dict] | None = Field(default_factory=list, exclude=True)
hypothesis: str | None = Field(None, exclude=True)
backtrack_from_step: int | None = Field(None, exclude=True)
@model_validator(mode="after")
def validate_step_one_requirements(self):
"""Ensure step 1 has required models field and unique model+stance combinations."""
if self.step_number == 1:
if not self.models:
raise ValueError("Step 1 requires 'models' field to specify which models to consult")
# Check for unique model + stance combinations
seen_combinations = set()
for model_config in self.models:
model_name = model_config.get("model", "")
stance = model_config.get("stance", "neutral")
combination = f"{model_name}:{stance}"
if combination in seen_combinations:
raise ValueError(
f"Duplicate model + stance combination found: {model_name} with stance '{stance}'. "
f"Each model + stance combination must be unique."
)
seen_combinations.add(combination)
return self
class ConsensusTool(WorkflowTool):
"""
Consensus workflow tool for step-by-step multi-model consensus gathering.
This tool implements a structured consensus workflow where the CLI agent first provides
its own neutral analysis, then consults each specified model individually,
and finally synthesizes all perspectives into a unified recommendation.
"""
def __init__(self):
super().__init__()
self.initial_prompt: str | None = None
self.original_proposal: str | None = None # Store the original proposal separately
self.models_to_consult: list[dict] = []
self.accumulated_responses: list[dict] = []
self._current_arguments: dict[str, Any] = {}
def get_name(self) -> str:
return "consensus"
def get_description(self) -> str:
return (
"Builds multi-model consensus through systematic analysis and structured debate. "
"Use for complex decisions, architectural choices, feature proposals, and technology evaluations. "
"Consults multiple models with different stances to synthesize comprehensive recommendations."
)
def get_system_prompt(self) -> str:
# For the CLI agent's initial analysis, use a neutral version of the consensus prompt
return CONSENSUS_PROMPT.replace(
"{stance_prompt}",
"""BALANCED PERSPECTIVE
Provide objective analysis considering both positive and negative aspects. However, if there is overwhelming evidence
that the proposal clearly leans toward being exceptionally good or particularly problematic, you MUST accurately
reflect this reality. Being "balanced" means being truthful about the weight of evidence, not artificially creating
50/50 splits when the reality is 90/10.
Your analysis should:
- Present all significant pros and cons discovered
- Weight them according to actual impact and likelihood
- If evidence strongly favors one conclusion, clearly state this
- Provide proportional coverage based on the strength of arguments
- Help the questioner see the true balance of considerations
Remember: Artificial balance that misrepresents reality is not helpful. True balance means accurate representation
of the evidence, even when it strongly points in one direction.""",
)
def get_default_temperature(self) -> float:
return TEMPERATURE_ANALYTICAL
def get_model_category(self) -> ToolModelCategory:
"""Consensus workflow requires extended reasoning"""
from tools.models import ToolModelCategory
return ToolModelCategory.EXTENDED_REASONING
def get_workflow_request_model(self):
"""Return the consensus workflow-specific request model."""
return ConsensusRequest
def get_input_schema(self) -> dict[str, Any]:
"""Generate input schema for consensus workflow."""
from .workflow.schema_builders import WorkflowSchemaBuilder
# Consensus tool-specific field definitions
consensus_field_overrides = {
# Override standard workflow fields that need consensus-specific descriptions
"step": {
"type": "string",
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["step"],
},
"step_number": {
"type": "integer",
"minimum": 1,
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["step_number"],
},
"total_steps": {
"type": "integer",
"minimum": 1,
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["total_steps"],
},
"next_step_required": {
"type": "boolean",
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["next_step_required"],
},
"findings": {
"type": "string",
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["findings"],
},
"relevant_files": {
"type": "array",
"items": {"type": "string"},
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["relevant_files"],
},
# consensus-specific fields (not in base workflow)
"models": {
"type": "array",
"items": {
"type": "object",
"properties": {
"model": {"type": "string"},
"stance": {"type": "string", "enum": ["for", "against", "neutral"], "default": "neutral"},
"stance_prompt": {"type": "string"},
},
"required": ["model"],
},
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["models"],
},
"current_model_index": {
"type": "integer",
"minimum": 0,
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["current_model_index"],
},
"model_responses": {
"type": "array",
"items": {"type": "object"},
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["model_responses"],
},
"images": {
"type": "array",
"items": {"type": "string"},
"description": CONSENSUS_WORKFLOW_FIELD_DESCRIPTIONS["images"],
},
}
# Define excluded fields for consensus workflow
excluded_workflow_fields = [
"files_checked", # Not used in consensus workflow
"relevant_context", # Not used in consensus workflow
"issues_found", # Not used in consensus workflow
"hypothesis", # Not used in consensus workflow
"backtrack_from_step", # Not used in consensus workflow
"confidence", # Not used in consensus workflow
]
excluded_common_fields = [
"model", # Consensus uses 'models' field instead
"temperature", # Not used in consensus workflow
"thinking_mode", # Not used in consensus workflow
]
# Build schema with proper field exclusion
# Include model field for compatibility but don't require it
schema = WorkflowSchemaBuilder.build_schema(
tool_specific_fields=consensus_field_overrides,
model_field_schema=self.get_model_field_schema(),
auto_mode=False, # Consensus doesn't require model at MCP boundary
tool_name=self.get_name(),
excluded_workflow_fields=excluded_workflow_fields,
excluded_common_fields=excluded_common_fields,
)
return schema
def get_required_actions(
self, step_number: int, confidence: str, findings: str, total_steps: int, request=None
) -> list[str]: # noqa: ARG002
"""Define required actions for each consensus phase.
Now includes request parameter for continuation-aware decisions.
Note: confidence parameter is kept for compatibility with base class but not used.
"""
if step_number == 1:
# CLI Agent's initial analysis
return [
"You've provided your initial analysis. The tool will now consult other models.",
"Wait for the next step to receive the first model's response.",
]
elif step_number < total_steps - 1:
# Processing individual model responses
return [
"Review the model response provided in this step",
"Note key agreements and disagreements with previous analyses",
"Wait for the next model's response",
]
else:
# Ready for final synthesis
return [
"All models have been consulted",
"Synthesize all perspectives into a comprehensive recommendation",
"Identify key points of agreement and disagreement",
"Provide clear, actionable guidance based on the consensus",
]
def should_call_expert_analysis(self, consolidated_findings, request=None) -> bool:
"""Consensus workflow doesn't use traditional expert analysis - it consults models step by step."""
return False
def prepare_expert_analysis_context(self, consolidated_findings) -> str:
"""Not used in consensus workflow."""
return ""
def requires_expert_analysis(self) -> bool:
"""Consensus workflow handles its own model consultations."""
return False
def requires_model(self) -> bool:
"""
Consensus tool doesn't require model resolution at the MCP boundary.
Uses it's own set of models
Returns:
bool: False
"""
return False
# Hook method overrides for consensus-specific behavior
def prepare_step_data(self, request) -> dict:
"""Prepare consensus-specific step data."""
step_data = {
"step": request.step,
"step_number": request.step_number,
"findings": request.findings,
"files_checked": [], # Not used
"relevant_files": request.relevant_files or [],
"relevant_context": [], # Not used
"issues_found": [], # Not used
"confidence": "exploring", # Not used, kept for compatibility
"hypothesis": None, # Not used
"images": request.images or [], # Now used for visual context
}
return step_data
async def handle_work_completion(self, response_data: dict, request, arguments: dict) -> dict: # noqa: ARG002
"""Handle consensus workflow completion - no expert analysis, just final synthesis."""
response_data["consensus_complete"] = True
response_data["status"] = "consensus_workflow_complete"
# Prepare final synthesis data
response_data["complete_consensus"] = {
"initial_prompt": self.original_proposal if self.original_proposal else self.initial_prompt,
"models_consulted": [m["model"] + ":" + m.get("stance", "neutral") for m in self.accumulated_responses],
"total_responses": len(self.accumulated_responses),
"consensus_confidence": "high", # Consensus complete
}
response_data["next_steps"] = (
"CONSENSUS GATHERING IS COMPLETE. You MUST now synthesize all perspectives and present:\n"
"1. Key points of AGREEMENT across models\n"
"2. Key points of DISAGREEMENT and why they differ\n"
"3. Your final consolidated recommendation\n"
"4. Specific, actionable next steps for implementation\n"
"5. Critical risks or concerns that must be addressed"
)
return response_data
def handle_work_continuation(self, response_data: dict, request) -> dict:
"""Handle continuation between consensus steps."""
current_idx = request.current_model_index or 0
if request.step_number == 1:
# After CLI Agent's initial analysis, prepare to consult first model
response_data["status"] = "consulting_models"
response_data["next_model"] = self.models_to_consult[0] if self.models_to_consult else None
response_data["next_steps"] = (
"Your initial analysis is complete. The tool will now consult the specified models."
)
elif current_idx < len(self.models_to_consult):
next_model = self.models_to_consult[current_idx]
response_data["status"] = "consulting_next_model"
response_data["next_model"] = next_model
response_data["models_remaining"] = len(self.models_to_consult) - current_idx
response_data["next_steps"] = f"Model consultation in progress. Next: {next_model['model']}"
else:
response_data["status"] = "ready_for_synthesis"
response_data["next_steps"] = "All models consulted. Ready for final synthesis."
return response_data
async def execute_workflow(self, arguments: dict[str, Any]) -> list:
"""Override execute_workflow to handle model consultations between steps."""
# Store arguments
self._current_arguments = arguments
# Validate request
request = self.get_workflow_request_model()(**arguments)
# On first step, store the models to consult
if request.step_number == 1:
# Store the original proposal from step 1 - this is what all models should see
self.original_proposal = request.step
self.initial_prompt = request.step # Keep for backward compatibility
self.models_to_consult = request.models or []
self.accumulated_responses = []
# Set total steps: len(models) (each step includes consultation + response)
request.total_steps = len(self.models_to_consult)
# For all steps (1 through total_steps), consult the corresponding model
if request.step_number <= request.total_steps:
# Calculate which model to consult for this step
model_idx = request.step_number - 1 # 0-based index
if model_idx < len(self.models_to_consult):
# Consult the model for this step
model_response = await self._consult_model(self.models_to_consult[model_idx], request)
# Add to accumulated responses
self.accumulated_responses.append(model_response)
# Include the model response in the step data
response_data = {
"status": "model_consulted",
"step_number": request.step_number,
"total_steps": request.total_steps,
"model_consulted": model_response["model"],
"model_stance": model_response.get("stance", "neutral"),
"model_response": model_response,
"current_model_index": model_idx + 1,
"next_step_required": request.step_number < request.total_steps,
}
# Add CLAI Agent's analysis to step 1
if request.step_number == 1:
response_data["agent_analysis"] = {
"initial_analysis": request.step,
"findings": request.findings,
}
response_data["status"] = "analysis_and_first_model_consulted"
# Check if this is the final step
if request.step_number == request.total_steps:
response_data["status"] = "consensus_workflow_complete"
response_data["consensus_complete"] = True
response_data["complete_consensus"] = {
"initial_prompt": self.original_proposal if self.original_proposal else self.initial_prompt,
"models_consulted": [
f"{m['model']}:{m.get('stance', 'neutral')}" for m in self.accumulated_responses
],
"total_responses": len(self.accumulated_responses),
"consensus_confidence": "high",
}
response_data["next_steps"] = (
"CONSENSUS GATHERING IS COMPLETE. Synthesize all perspectives and present:\n"
"1. Key points of AGREEMENT across models\n"
"2. Key points of DISAGREEMENT and why they differ\n"
"3. Your final consolidated recommendation\n"
"4. Specific, actionable next steps for implementation\n"
"5. Critical risks or concerns that must be addressed"
)
else:
response_data["next_steps"] = (
f"Model {model_response['model']} has provided its {model_response.get('stance', 'neutral')} "
f"perspective. Please analyze this response and call {self.get_name()} again with:\n"
f"- step_number: {request.step_number + 1}\n"
f"- findings: Summarize key points from this model's response"
)
# Add accumulated responses for tracking
response_data["accumulated_responses"] = self.accumulated_responses
# Add metadata (since we're bypassing the base class metadata addition)
model_name = self.get_request_model_name(request)
provider = self.get_model_provider(model_name)
response_data["metadata"] = {
"tool_name": self.get_name(),
"model_name": model_name,
"model_used": model_name,
"provider_used": provider.get_provider_type().value,
}
return [TextContent(type="text", text=json.dumps(response_data, indent=2, ensure_ascii=False))]
# Otherwise, use standard workflow execution
return await super().execute_workflow(arguments)
async def _consult_model(self, model_config: dict, request) -> dict:
"""Consult a single model and return its response."""
try:
# Import and create ModelContext once at the beginning
from utils.model_context import ModelContext
# Get the provider for this model
model_name = model_config["model"]
provider = self.get_model_provider(model_name)
# Create model context once and reuse for both file processing and temperature validation
model_context = ModelContext(model_name=model_name)
# Prepare the prompt with any relevant files
# Use continuation_id=None for blinded consensus - each model should only see
# original prompt + files, not conversation history or other model responses
# CRITICAL: Use the original proposal from step 1, NOT what's in request.step for steps 2+!
# Steps 2+ contain summaries/notes that must NEVER be sent to other models
prompt = self.original_proposal if self.original_proposal else self.initial_prompt
if request.relevant_files:
file_content, _ = self._prepare_file_content_for_prompt(
request.relevant_files,
None, # Use None instead of request.continuation_id for blinded consensus
"Context files",
model_context=model_context,
)
if file_content:
prompt = f"{prompt}\n\n=== CONTEXT FILES ===\n{file_content}\n=== END CONTEXT ==="
# Get stance-specific system prompt
stance = model_config.get("stance", "neutral")
stance_prompt = model_config.get("stance_prompt")
system_prompt = self._get_stance_enhanced_prompt(stance, stance_prompt)
# Validate temperature against model constraints (respects supports_temperature)
validated_temperature, temp_warnings = self.validate_and_correct_temperature(
self.get_default_temperature(), model_context
)
# Log any temperature corrections
for warning in temp_warnings:
logger.warning(warning)
# Call the model with validated temperature
response = provider.generate_content(
prompt=prompt,
model_name=model_name,
system_prompt=system_prompt,
temperature=validated_temperature,
thinking_mode="medium",
images=request.images if request.images else None,
)
return {
"model": model_name,
"stance": stance,
"status": "success",
"verdict": response.content,
"metadata": {
"provider": provider.get_provider_type().value,
"model_name": model_name,
},
}
except Exception as e:
logger.exception("Error consulting model %s", model_config)
return {
"model": model_config.get("model", "unknown"),
"stance": model_config.get("stance", "neutral"),
"status": "error",
"error": str(e),
}
def _get_stance_enhanced_prompt(self, stance: str, custom_stance_prompt: str | None = None) -> str:
"""Get the system prompt with stance injection."""
base_prompt = CONSENSUS_PROMPT
if custom_stance_prompt:
return base_prompt.replace("{stance_prompt}", custom_stance_prompt)
stance_prompts = {
"for": """SUPPORTIVE PERSPECTIVE WITH INTEGRITY
You are tasked with advocating FOR this proposal, but with CRITICAL GUARDRAILS:
MANDATORY ETHICAL CONSTRAINTS:
- This is NOT a debate for entertainment. You MUST act in good faith and in the best interest of the questioner
- You MUST think deeply about whether supporting this idea is safe, sound, and passes essential requirements
- You MUST be direct and unequivocal in saying "this is a bad idea" when it truly is
- There must be at least ONE COMPELLING reason to be optimistic, otherwise DO NOT support it
WHEN TO REFUSE SUPPORT (MUST OVERRIDE STANCE):
- If the idea is fundamentally harmful to users, project, or stakeholders
- If implementation would violate security, privacy, or ethical standards
- If the proposal is technically infeasible within realistic constraints
- If costs/risks dramatically outweigh any potential benefits
YOUR SUPPORTIVE ANALYSIS SHOULD:
- Identify genuine strengths and opportunities
- Propose solutions to overcome legitimate challenges
- Highlight synergies with existing systems
- Suggest optimizations that enhance value
- Present realistic implementation pathways
Remember: Being "for" means finding the BEST possible version of the idea IF it has merit, not blindly supporting bad ideas.""",
"against": """CRITICAL PERSPECTIVE WITH RESPONSIBILITY
You are tasked with critiquing this proposal, but with ESSENTIAL BOUNDARIES:
MANDATORY FAIRNESS CONSTRAINTS:
- You MUST NOT oppose genuinely excellent, common-sense ideas just to be contrarian
- You MUST acknowledge when a proposal is fundamentally sound and well-conceived
- You CANNOT give harmful advice or recommend against beneficial changes
- If the idea is outstanding, say so clearly while offering constructive refinements
WHEN TO MODERATE CRITICISM (MUST OVERRIDE STANCE):
- If the proposal addresses critical user needs effectively
- If it follows established best practices with good reason
- If benefits clearly and substantially outweigh risks
- If it's the obvious right solution to the problem
YOUR CRITICAL ANALYSIS SHOULD:
- Identify legitimate risks and failure modes
- Point out overlooked complexities
- Suggest more efficient alternatives
- Highlight potential negative consequences
- Question assumptions that may be flawed
Remember: Being "against" means rigorous scrutiny to ensure quality, not undermining good ideas that deserve support.""",
"neutral": """BALANCED PERSPECTIVE
Provide objective analysis considering both positive and negative aspects. However, if there is overwhelming evidence
that the proposal clearly leans toward being exceptionally good or particularly problematic, you MUST accurately
reflect this reality. Being "balanced" means being truthful about the weight of evidence, not artificially creating
50/50 splits when the reality is 90/10.
Your analysis should:
- Present all significant pros and cons discovered
- Weight them according to actual impact and likelihood
- If evidence strongly favors one conclusion, clearly state this
- Provide proportional coverage based on the strength of arguments
- Help the questioner see the true balance of considerations
Remember: Artificial balance that misrepresents reality is not helpful. True balance means accurate representation
of the evidence, even when it strongly points in one direction.""",
}
stance_prompt = stance_prompts.get(stance, stance_prompts["neutral"])
return base_prompt.replace("{stance_prompt}", stance_prompt)
def customize_workflow_response(self, response_data: dict, request) -> dict:
"""Customize response for consensus workflow."""
# Store model responses in the response for tracking
if self.accumulated_responses:
response_data["accumulated_responses"] = self.accumulated_responses
# Add consensus-specific fields
if request.step_number == 1:
response_data["consensus_workflow_status"] = "initial_analysis_complete"
elif request.step_number < request.total_steps - 1:
response_data["consensus_workflow_status"] = "consulting_models"
else:
response_data["consensus_workflow_status"] = "ready_for_synthesis"
# Customize metadata for consensus workflow
self._customize_consensus_metadata(response_data, request)
return response_data
def _customize_consensus_metadata(self, response_data: dict, request) -> None:
"""
Customize metadata for consensus workflow to accurately reflect multi-model nature.
The default workflow metadata shows the model running Agent's analysis steps,
but consensus is a multi-model tool that consults different models. We need
to provide accurate metadata that reflects this.
"""
if "metadata" not in response_data:
response_data["metadata"] = {}
metadata = response_data["metadata"]
# Always preserve tool_name
metadata["tool_name"] = self.get_name()
if request.step_number == request.total_steps:
# Final step - show comprehensive consensus metadata
models_consulted = []
if self.models_to_consult:
models_consulted = [f"{m['model']}:{m.get('stance', 'neutral')}" for m in self.models_to_consult]
metadata.update(
{
"workflow_type": "multi_model_consensus",
"models_consulted": models_consulted,
"consensus_complete": True,
"total_models": len(self.models_to_consult) if self.models_to_consult else 0,
}
)
# Remove the misleading single model metadata
metadata.pop("model_used", None)
metadata.pop("provider_used", None)
else:
# Intermediate steps - show consensus workflow in progress
models_to_consult = []
if self.models_to_consult:
models_to_consult = [f"{m['model']}:{m.get('stance', 'neutral')}" for m in self.models_to_consult]
metadata.update(
{
"workflow_type": "multi_model_consensus",
"models_to_consult": models_to_consult,
"consultation_step": request.step_number,
"total_consultation_steps": request.total_steps,
}
)
# Remove the misleading single model metadata that shows Agent's execution model
# instead of the models being consulted
metadata.pop("model_used", None)
metadata.pop("provider_used", None)
def _add_workflow_metadata(self, response_data: dict, arguments: dict[str, Any]) -> None:
"""
Override workflow metadata addition for consensus tool.
The consensus tool doesn't use single model metadata because it's a multi-model
workflow. Instead, we provide consensus-specific metadata that accurately
reflects the models being consulted.
"""
# Initialize metadata if not present
if "metadata" not in response_data:
response_data["metadata"] = {}
# Add basic tool metadata
response_data["metadata"]["tool_name"] = self.get_name()
# The consensus-specific metadata is already added by _customize_consensus_metadata
# which is called from customize_workflow_response. We don't add the standard
# single-model metadata (model_used, provider_used) because it's misleading
# for a multi-model consensus workflow.
logger.debug(
f"[CONSENSUS_METADATA] {self.get_name()}: Using consensus-specific metadata instead of single-model metadata"
)
def store_initial_issue(self, step_description: str):
"""Store initial prompt for model consultations."""
self.original_proposal = step_description
self.initial_prompt = step_description # Keep for backward compatibility
# Required abstract methods from BaseTool
def get_request_model(self):
"""Return the consensus workflow-specific request model."""
return ConsensusRequest
async def prepare_prompt(self, request) -> str: # noqa: ARG002
"""Not used - workflow tools use execute_workflow()."""
return "" # Workflow tools use execute_workflow() directly
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