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import asyncio
from pathlib import Path
from ace.integrations.mcp.registry import SessionRegistry
from ace.integrations.mcp.models import (
AskRequest,
AskResponse,
LearnSampleRequest,
LearnSampleResponse,
LearnFeedbackRequest,
LearnFeedbackResponse,
SkillbookGetRequest,
SkillbookGetResponse,
SkillbookSaveRequest,
SkillbookSaveResponse,
SkillbookLoadRequest,
SkillbookLoadResponse,
SkillItem,
)
from ace.integrations.mcp.config import MCPServerConfig
from ace.integrations.mcp.errors import (
ACEMCPError,
ForbiddenInSafeModeError,
InternalError,
SaveLoadDisabledError,
TimeoutError as MCPTimeoutError,
ValidationError,
)
from ace.core.environments import Sample
class MCPHandlers:
def __init__(self, registry: SessionRegistry, config: MCPServerConfig):
self.registry = registry
self.config = config
def _get_session_kwargs(self, config_model) -> tuple[str | None, dict[str, Any]]:
target_model = None
kwargs: dict[str, Any] = {}
if config_model:
target_model = config_model.model # may be None per contract
if config_model.temperature is not None:
kwargs["temperature"] = config_model.temperature
if config_model.max_tokens is not None:
kwargs["max_tokens"] = config_model.max_tokens
return target_model, kwargs
def _enforce_prompt_limit(self, char_count: int, field_name: str) -> None:
if char_count > self.config.max_prompt_chars:
raise ValidationError(
f"{field_name} exceeds max_prompt_chars ({self.config.max_prompt_chars})",
details={
"field": field_name,
"char_count": char_count,
"max_prompt_chars": self.config.max_prompt_chars,
},
)
def _resolve_skillbook_path(self, path: str) -> str:
"""Resolve a user-provided path and validate it against skillbook_root.
Returns the resolved absolute path string so callers use the
validated path — not the raw user input — for file operations,
eliminating TOCTOU races with symlinks or ``..`` components.
"""
resolved = str(Path(path).expanduser().resolve())
if not self.config.skillbook_root:
return resolved
root = Path(self.config.skillbook_root).expanduser().resolve()
try:
Path(resolved).relative_to(root)
except ValueError as exc:
raise ValidationError(
"Path is outside configured skillbook_root",
details={
"path": resolved,
"skillbook_root": str(root),
},
) from exc
return resolved
async def handle_ask(self, request: AskRequest) -> AskResponse:
self._enforce_prompt_limit(len(request.question) + len(request.context), "ask")
target_model, kwargs = self._get_session_kwargs(request.session_config)
session = await self.registry.get_or_create(
request.session_id, model=target_model, **kwargs
)
async with session.lock:
try:
answer = await asyncio.to_thread(
session.runner.ask, request.question, request.context
)
skill_count = len(session.runner.skillbook.skills())
return AskResponse(
session_id=request.session_id,
answer=str(answer),
skill_count=skill_count,
)
except ACEMCPError:
raise
except Exception as e:
raise InternalError(str(e))
async def handle_skillbook_get(
self, request: SkillbookGetRequest
) -> SkillbookGetResponse:
session = await self.registry.get(request.session_id)
async with session.lock:
try:
skillbook = session.runner.skillbook
skills = skillbook.skills(include_invalid=request.include_invalid)
limited_skills: list[SkillItem] = []
for s in skills:
content = getattr(s, "insight", None) or getattr(s, "issue", None)
limited_skills.append(
SkillItem(
id=getattr(s, "id", str(len(limited_skills))),
content=content if content is not None else str(s),
topic=getattr(s, "section", None),
helpful=getattr(s, "helpful_count", None),
harmful=getattr(s, "harmful_count", None),
neutral=getattr(s, "neutral_count", None),
)
)
limited_skills = limited_skills[: request.limit]
stats = skillbook.stats()
return SkillbookGetResponse(
session_id=request.session_id, stats=stats, skills=limited_skills
)
except ACEMCPError:
raise
except Exception as e:
raise InternalError(str(e))
async def handle_learn_sample(
self, request: LearnSampleRequest
) -> LearnSampleResponse:
if self.config.safe_mode:
raise ForbiddenInSafeModeError("ace.learn.sample")
if len(request.samples) > self.config.max_samples_per_call:
raise ValidationError(
f"samples exceeds max_samples_per_call ({self.config.max_samples_per_call})",
details={
"sample_count": len(request.samples),
"max_samples_per_call": self.config.max_samples_per_call,
},
)
for idx, s in enumerate(request.samples):
self._enforce_prompt_limit(
len(s.question) + len(s.context),
f"samples[{idx}]",
)
target_model, kwargs = self._get_session_kwargs(request.session_config)
session = await self.registry.get_or_create(
request.session_id, model=target_model, **kwargs
)
async with session.lock:
try:
samples = []
for s in request.samples:
samples.append(
Sample(
question=s.question,
context=s.context,
ground_truth=s.ground_truth,
metadata=s.metadata or {},
)
)
count_before = len(session.runner.skillbook.skills())
results = await asyncio.wait_for(
asyncio.to_thread(
session.runner.learn,
samples,
None,
request.epochs,
),
timeout=self.config.learn_timeout_seconds,
)
failed = sum(1 for r in results if r.error is not None)
count_after = len(session.runner.skillbook.skills())
return LearnSampleResponse(
session_id=request.session_id,
processed=len(samples) - failed,
failed=failed,
skill_count_before=count_before,
skill_count_after=count_after,
new_skill_count=max(0, count_after - count_before),
)
except ACEMCPError:
raise
except asyncio.TimeoutError:
raise MCPTimeoutError(
f"learn.sample timed out after {self.config.learn_timeout_seconds}s"
)
except Exception as e:
raise InternalError(str(e))
async def handle_learn_feedback(
self, request: LearnFeedbackRequest
) -> LearnFeedbackResponse:
if self.config.safe_mode:
raise ForbiddenInSafeModeError("ace.learn.feedback")
self._enforce_prompt_limit(
len(request.question)
+ len(request.context)
+ len(request.answer)
+ len(request.feedback)
+ len(request.ground_truth or ""),
"learn.feedback",
)
target_model, kwargs = self._get_session_kwargs(request.session_config)
session = await self.registry.get_or_create(
request.session_id, model=target_model, **kwargs
)
async with session.lock:
try:
count_before = len(session.runner.skillbook.skills())
# Prefer the direct feedback path when a prior ask exists;
# fall back to learn_from_traces for standalone feedback.
timeout = self.config.learn_timeout_seconds
learned = await asyncio.wait_for(
asyncio.to_thread(
session.runner.learn_from_feedback,
request.feedback,
request.ground_truth or None,
),
timeout=timeout,
)
if not learned:
# No prior ask interaction — build a trace and learn
trace: dict[str, object] = {
"question": request.question,
"context": request.context,
"answer": request.answer,
"skill_ids": [],
"feedback": request.feedback,
"ground_truth": request.ground_truth,
}
await asyncio.wait_for(
asyncio.to_thread(session.runner.learn_from_traces, [trace]),
timeout=timeout,
)
count_after = len(session.runner.skillbook.skills())
return LearnFeedbackResponse(
session_id=request.session_id,
learned=True,
skill_count_before=count_before,
skill_count_after=count_after,
new_skill_count=max(0, count_after - count_before),
)
except ACEMCPError:
raise
except asyncio.TimeoutError:
raise MCPTimeoutError(
f"learn.feedback timed out after {self.config.learn_timeout_seconds}s"
)
except Exception as e:
raise InternalError(str(e))
async def handle_skillbook_save(
self, request: SkillbookSaveRequest
) -> SkillbookSaveResponse:
if self.config.safe_mode:
raise ForbiddenInSafeModeError("ace.skillbook.save")
if not self.config.allow_save_load:
raise SaveLoadDisabledError("ace.skillbook.save")
resolved = self._resolve_skillbook_path(request.path)
session = await self.registry.get(request.session_id)
async with session.lock:
try:
await asyncio.to_thread(session.runner.save, resolved)
skill_count = len(session.runner.skillbook.skills())
return SkillbookSaveResponse(
session_id=request.session_id,
path=resolved,
saved_skill_count=skill_count,
)
except ACEMCPError:
raise
except Exception as e:
raise InternalError(str(e))
async def handle_skillbook_load(
self, request: SkillbookLoadRequest
) -> SkillbookLoadResponse:
if self.config.safe_mode:
raise ForbiddenInSafeModeError("ace.skillbook.load")
if not self.config.allow_save_load:
raise SaveLoadDisabledError("ace.skillbook.load")
resolved = self._resolve_skillbook_path(request.path)
session = await self.registry.get_or_create(request.session_id)
async with session.lock:
try:
await asyncio.to_thread(session.runner.load, resolved)
skill_count = len(session.runner.skillbook.skills())
return SkillbookLoadResponse(
session_id=request.session_id,
path=resolved,
skill_count=skill_count,
)
except ACEMCPError:
raise
except Exception as e:
raise InternalError(str(e))
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