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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

"""
ToolDispatcher — deterministic probe responses driven by CandidateGroundTruth.
Each tool validates its query against the candidate profile and returns a
result consistent with the injected fake_signals. Wrong-tool / missing-data
calls charge the probe cost and return "not found" style messages.
Day-3 upgrade: swap _llm_screening_call() for a real Anthropic API call.
"""

from __future__ import annotations

from typing import Optional

try:
    from ..models import (
        CandidateGroundTruth,
        CandidateProfile,
        JobDescription,
        ToolType,
    )
    from .scratchpad import EpisodeScratchpad
except ImportError:
    from models import (
        CandidateGroundTruth,
        CandidateProfile,
        JobDescription,
        ToolType,
    )
    from server.scratchpad import EpisodeScratchpad


class ToolDispatcher:
    """Dispatches tool calls to deterministic per-tool handlers."""

    def run(
        self,
        tool: ToolType,
        query: Optional[str],
        candidate: CandidateProfile,
        gt: CandidateGroundTruth,
        jd: JobDescription,
        scratchpad: EpisodeScratchpad,
    ) -> str:
        handler = _HANDLERS.get(tool)
        if handler is None:
            return f"[{tool.value.upper()}] Tool not implemented."
        return handler(query, candidate, gt, jd, scratchpad)


# ── Individual tool handlers ──────────────────────────────────────────────────

def _verify_employment(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if not query:
        return "[VERIFY_EMPLOYMENT] Provide query as 'Company Name, Job Title'."
    company = query.split(",")[0].strip()
    if not any(e.company.lower() == company.lower() for e in candidate.work_history):
        return (
            f"[VERIFY_EMPLOYMENT] '{company}' not found in candidate's work history. "
            "Verify the company name matches exactly."
        )
    for sig in gt.fake_signals:
        if sig.startswith("company_nonexistent:"):
            fake_co = sig.split(":", 1)[1]
            if fake_co.lower() in company.lower() or company.lower() in fake_co.lower():
                return (
                    f"[VERIFY_EMPLOYMENT] '{company}' could not be found in business "
                    "registries or public filings. No matching corporate entity. "
                    "Likely fictitious employer."
                )
    return (
        f"[VERIFY_EMPLOYMENT] '{company}' is a registered business. "
        "Role appears consistent with company profile and size."
    )


def _check_employment_dates(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if not query:
        return "[CHECK_EMPLOYMENT_DATES] Provide query as 'Company Name, Start–End'."
    company = query.split(",")[0].strip()
    if not any(e.company.lower() == company.lower() for e in candidate.work_history):
        return (
            f"[CHECK_EMPLOYMENT_DATES] '{company}' not found in candidate's work history."
        )
    if any("impossible_dates" in s or "overlap" in s for s in gt.fake_signals):
        return (
            f"[CHECK_EMPLOYMENT_DATES] Date inconsistency at '{company}': tenure overlaps "
            "with another listed role by 14 months. Timeline is not physically possible."
        )
    return (
        f"[CHECK_EMPLOYMENT_DATES] Dates at '{company}' are internally consistent. "
        "No overlaps or impossible gaps detected."
    )


def _check_linkedin(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    url = candidate.online_profiles.get("linkedin")
    if not url:
        return "[CHECK_LINKEDIN] No LinkedIn URL in candidate profile. Cannot verify."
    if "linkedin_mismatch" in gt.fake_signals:
        return (
            "[CHECK_LINKEDIN] Discrepancies found: resume title and tenure differ "
            "from LinkedIn profile. Example: resume states 'Lead Engineer 2019–2022'; "
            "LinkedIn shows 'Junior Developer 2020–2021'. Titles and dates conflict."
        )
    return (
        "[CHECK_LINKEDIN] LinkedIn profile is consistent with resume. "
        "Titles, companies, and dates align. Endorsements match listed skills."
    )


def _check_online_profile(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    # Agent should pass the URL from online_profiles relevant to the JD domain
    url = query
    if not url:
        # Fallback: pick any non-LinkedIn profile
        url = next(
            (v for k, v in candidate.online_profiles.items() if k != "linkedin"), None
        )
    if not url:
        return (
            "[CHECK_ONLINE_PROFILE] No relevant online profile URL found. "
            "Candidate has no GitHub, portfolio, or domain-specific profile listed."
        )
    if "github_burst" in gt.fake_signals and ("github" in url.lower() or "git" in url.lower()):
        return (
            "[CHECK_ONLINE_PROFILE] GitHub activity anomaly detected: 1,247 commits "
            "concentrated in a 48-hour window 3 days before the application date. "
            "Account created 5 months ago. No pull requests or collaborative "
            "contributions. Commit messages are non-descriptive. "
            "Profile activity pattern consistent with fabricated history."
        )
    if "portfolio_burst" in gt.fake_signals:
        return (
            "[CHECK_ONLINE_PROFILE] Portfolio domain registered 6 days before "
            "application. All project dates post-date the registration. "
            "Likely created specifically for this application."
        )
    return (
        f"[CHECK_ONLINE_PROFILE] Profile at {url} shows consistent activity "
        "spanning 3+ years. Contributions match claimed skill set. No anomalies."
    )


def _verify_education(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if not query:
        return "[VERIFY_EDUCATION] Provide query as 'Degree, Institution, Year'."
    if "fake_degree" in gt.fake_signals:
        parts = query.split(",")
        inst = parts[1].strip() if len(parts) > 1 else "listed institution"
        return (
            f"[VERIFY_EDUCATION] '{inst}' does not offer the claimed degree program. "
            "No public records of this degree conferred. Graduation year is also "
            "inconsistent with stated class size data."
        )
    return (
        "[VERIFY_EDUCATION] Education credentials verified. Degree and graduation "
        "year are consistent with institution records."
    )


def _contact_reference(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if not candidate.references:
        return "[CONTACT_REFERENCE] No references listed in this candidate's profile."
    referee_name = (query or "").strip() or candidate.references[0].name
    # Check if this referee is in the references list
    ref_match = next(
        (r for r in candidate.references if referee_name.lower() in r.name.lower()),
        None,
    )
    if ref_match is None:
        return (
            f"[CONTACT_REFERENCE] '{referee_name}' not found in candidate's "
            "listed references. Check the name spelling."
        )
    for sig in gt.fake_signals:
        if sig.startswith("reference_wrong_company:"):
            fake_ref = sig.split(":", 1)[1]
            if fake_ref.lower() in referee_name.lower() or referee_name.lower() in fake_ref.lower():
                return (
                    f"[CONTACT_REFERENCE] {referee_name}: Contact confirmed. "
                    f"However, they state they worked at a different organisation — "
                    f"not '{ref_match.company}' as listed. No direct employment "
                    "overlap with candidate confirmed. Reference appears invalid."
                )
    return (
        f"[CONTACT_REFERENCE] {referee_name}: Confirmed working relationship "
        f"at {ref_match.company}. Reference is positive. "
        f"Relationship described as: {ref_match.relationship}."
    )


def _review_timeline(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if any("impossible" in s or "overlap" in s for s in gt.fake_signals):
        return (
            "[REVIEW_TIMELINE] Timeline issues detected: role overlap of 14 months "
            "between two listed positions. Candidate claims 8 years experience but "
            "earliest role starts only 5 years ago. At least one date range is "
            "physically impossible."
        )
    if not candidate.work_history:
        return "[REVIEW_TIMELINE] No work history to review."
    total_years = len(candidate.work_history) * 2  # rough heuristic for stub
    return (
        f"[REVIEW_TIMELINE] Work history timeline is internally consistent. "
        f"No overlaps or impossible progressions. "
        f"{len(candidate.work_history)} roles covering an estimated {total_years}+ years."
    )


def _check_skills_fit(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if "skills_not_in_history" in gt.fake_signals:
        return (
            "[CHECK_SKILLS_FIT] Significant mismatch: candidate lists "
            f"{', '.join(candidate.skills[:4])} — but all roles in work history "
            "are in sales/account management with no engineering responsibilities. "
            "Skills appear entirely fabricated relative to work history. "
            f"JD requires: {', '.join(jd.required_skills[:3])}."
        )
    overlap = [s for s in candidate.skills if s in jd.required_skills]
    missing = [s for s in jd.required_skills if s not in candidate.skills]
    return (
        f"[CHECK_SKILLS_FIT] {len(overlap)}/{len(jd.required_skills)} required skills present. "
        f"Match: {', '.join(overlap) or 'none'}. "
        f"Missing: {', '.join(missing) or 'none'}. "
        "Skills are evidenced in work history."
    )


def _verify_claim(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if not query:
        return "[VERIFY_CLAIM] Provide a specific bullet-point achievement string from work history."
    # Check the claim exists in some bullet point
    claim_text = query.lower()
    found = any(
        claim_text in bp.lower()
        for entry in candidate.work_history
        for bp in entry.bullet_points
    )
    if not found:
        return (
            f"[VERIFY_CLAIM] Claim text not found in candidate's listed work history "
            "bullet points. Verify the exact text matches."
        )
    if "inflated_claim" in gt.fake_signals:
        return (
            f"[VERIFY_CLAIM] Claim appears implausible: company had ~15 employees "
            f"at the time. '{query[:60]}' is inconsistent with company scale and "
            "typical responsibilities at that role level."
        )
    return (
        f"[VERIFY_CLAIM] Claim '{query[:60]}' appears consistent with "
        "company size and role level. Corroborating public data found."
    )


def _verify_location(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if "location_mismatch" in gt.fake_signals:
        sponsorship_note = (
            " ⚠ JD requires no visa sponsorship — this candidate would need sponsorship."
            if not jd.sponsorship_available
            else ""
        )
        return (
            f"[VERIFY_LOCATION] Location mismatch: stated '{candidate.stated_location}' "
            f"but GitHub commit timestamps and IP geolocation signals indicate "
            f"actual location '{gt.true_location}'.{sponsorship_note}"
        )
    sponsorship_ok = (
        "" if jd.sponsorship_available
        else f" No sponsorship needed — consistent with JD constraint."
    )
    return (
        f"[VERIFY_LOCATION] Location '{candidate.stated_location}' is consistent "
        f"with profile signals (GitHub timezone, commit patterns, company office).{sponsorship_ok}"
    )


def _search_web(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if not query:
        return "[SEARCH_WEB] Provide a search query."
    if gt.is_fake and any(
        s.startswith("company_nonexistent") for s in gt.fake_signals
    ):
        fake_co = next(
            (s.split(":", 1)[1] for s in gt.fake_signals if s.startswith("company_nonexistent:")),
            None,
        )
        if fake_co and fake_co.lower() in query.lower():
            return (
                f"[SEARCH_WEB] '{query}': No results found for this company in "
                "LinkedIn, Crunchbase, or public business directories. "
                "Zero press mentions. Entity appears fictitious."
            )
    return (
        f"[SEARCH_WEB] Results for '{query}': No significant red flags in public "
        "records. Candidate's stated employer and role appear in professional networks."
    )


def _request_screening_round(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    _pad: EpisodeScratchpad,
) -> str:
    if gt.is_fake:
        return (
            "[REQUEST_SCREENING_ROUND] 30-min screening call completed.\n"
            "Technical section: candidate was unable to describe implementation "
            "details of listed projects. Answers were vague and inconsistent under "
            "follow-up probing.\n"
            "Behavioural section: story changed when asked about specific timelines. "
            "Claimed promotion dates conflict with dates given earlier in the call.\n"
            "Competency: technical answers inconsistent with claimed senior-level "
            "experience. Unable to solve a standard problem in the claimed primary language.\n"
            "Overall: negative indicators across all three sections."
        )
    return (
        "[REQUEST_SCREENING_ROUND] 30-min screening call completed.\n"
        "Technical section: demonstrated strong depth. Clear explanations of "
        "system design decisions and trade-offs.\n"
        "Behavioural section: consistent narrative with specific examples. "
        "Timelines align with resume.\n"
        "Competency: solved the coding problem efficiently with good communication.\n"
        "Overall: positive indicators. Recommend advancing."
    )


def _note(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    pad: EpisodeScratchpad,
) -> str:
    return pad.note(candidate.candidate_id, query or "")


def _recall(
    query: Optional[str],
    candidate: CandidateProfile,
    gt: CandidateGroundTruth,
    jd: JobDescription,
    pad: EpisodeScratchpad,
) -> str:
    return pad.recall(query)


# ── Handler dispatch table ────────────────────────────────────────────────────

_HANDLERS = {
    ToolType.VERIFY_EMPLOYMENT: _verify_employment,
    ToolType.CHECK_EMPLOYMENT_DATES: _check_employment_dates,
    ToolType.CHECK_LINKEDIN: _check_linkedin,
    ToolType.CHECK_ONLINE_PROFILE: _check_online_profile,
    ToolType.VERIFY_EDUCATION: _verify_education,
    ToolType.CONTACT_REFERENCE: _contact_reference,
    ToolType.REVIEW_TIMELINE: _review_timeline,
    ToolType.CHECK_SKILLS_FIT: _check_skills_fit,
    ToolType.VERIFY_CLAIM: _verify_claim,
    ToolType.VERIFY_LOCATION: _verify_location,
    ToolType.SEARCH_WEB: _search_web,
    ToolType.REQUEST_SCREENING_ROUND: _request_screening_round,
    ToolType.NOTE: _note,
    ToolType.RECALL: _recall,
}

	# Copyright (c) Meta Platforms, Inc. and affiliates.
	# All rights reserved.
	#
	# This source code is licensed under the BSD-style license found in the
	# LICENSE file in the root directory of this source tree.

	"""
	ToolDispatcher — deterministic probe responses driven by CandidateGroundTruth.

	Each tool validates its query against the candidate profile and returns a
	result consistent with the injected fake_signals. Wrong-tool / missing-data
	calls charge the probe cost and return "not found" style messages.

	Day-3 upgrade: swap _llm_screening_call() for a real Anthropic API call.
	"""

	from __future__ import annotations

	from typing import Optional

	try:
	from ..models import (
	CandidateGroundTruth,
	CandidateProfile,
	JobDescription,
	ToolType,
	)
	from .scratchpad import EpisodeScratchpad
	except ImportError:
	from models import (
	CandidateGroundTruth,
	CandidateProfile,
	JobDescription,
	ToolType,
	)
	from server.scratchpad import EpisodeScratchpad


	class ToolDispatcher:
	"""Dispatches tool calls to deterministic per-tool handlers."""

	def run(
	self,
	tool: ToolType,
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	scratchpad: EpisodeScratchpad,
	) -> str:
	handler = _HANDLERS.get(tool)
	if handler is None:
	return f"[{tool.value.upper()}] Tool not implemented."
	return handler(query, candidate, gt, jd, scratchpad)


	# ── Individual tool handlers ──────────────────────────────────────────────────

	def _verify_employment(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if not query:
	return "[VERIFY_EMPLOYMENT] Provide query as 'Company Name, Job Title'."
	company = query.split(",")[0].strip()
	if not any(e.company.lower() == company.lower() for e in candidate.work_history):
	return (
	f"[VERIFY_EMPLOYMENT] '{company}' not found in candidate's work history. "
	"Verify the company name matches exactly."
	)
	for sig in gt.fake_signals:
	if sig.startswith("company_nonexistent:"):
	fake_co = sig.split(":", 1)[1]
	if fake_co.lower() in company.lower() or company.lower() in fake_co.lower():
	return (
	f"[VERIFY_EMPLOYMENT] '{company}' could not be found in business "
	"registries or public filings. No matching corporate entity. "
	"Likely fictitious employer."
	)
	return (
	f"[VERIFY_EMPLOYMENT] '{company}' is a registered business. "
	"Role appears consistent with company profile and size."
	)


	def _check_employment_dates(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if not query:
	return "[CHECK_EMPLOYMENT_DATES] Provide query as 'Company Name, Start–End'."
	company = query.split(",")[0].strip()
	if not any(e.company.lower() == company.lower() for e in candidate.work_history):
	return (
	f"[CHECK_EMPLOYMENT_DATES] '{company}' not found in candidate's work history."
	)
	if any("impossible_dates" in s or "overlap" in s for s in gt.fake_signals):
	return (
	f"[CHECK_EMPLOYMENT_DATES] Date inconsistency at '{company}': tenure overlaps "
	"with another listed role by 14 months. Timeline is not physically possible."
	)
	return (
	f"[CHECK_EMPLOYMENT_DATES] Dates at '{company}' are internally consistent. "
	"No overlaps or impossible gaps detected."
	)


	def _check_linkedin(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	url = candidate.online_profiles.get("linkedin")
	if not url:
	return "[CHECK_LINKEDIN] No LinkedIn URL in candidate profile. Cannot verify."
	if "linkedin_mismatch" in gt.fake_signals:
	return (
	"[CHECK_LINKEDIN] Discrepancies found: resume title and tenure differ "
	"from LinkedIn profile. Example: resume states 'Lead Engineer 2019–2022'; "
	"LinkedIn shows 'Junior Developer 2020–2021'. Titles and dates conflict."
	)
	return (
	"[CHECK_LINKEDIN] LinkedIn profile is consistent with resume. "
	"Titles, companies, and dates align. Endorsements match listed skills."
	)


	def _check_online_profile(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	# Agent should pass the URL from online_profiles relevant to the JD domain
	url = query
	if not url:
	# Fallback: pick any non-LinkedIn profile
	url = next(
	(v for k, v in candidate.online_profiles.items() if k != "linkedin"), None
	)
	if not url:
	return (
	"[CHECK_ONLINE_PROFILE] No relevant online profile URL found. "
	"Candidate has no GitHub, portfolio, or domain-specific profile listed."
	)
	if "github_burst" in gt.fake_signals and ("github" in url.lower() or "git" in url.lower()):
	return (
	"[CHECK_ONLINE_PROFILE] GitHub activity anomaly detected: 1,247 commits "
	"concentrated in a 48-hour window 3 days before the application date. "
	"Account created 5 months ago. No pull requests or collaborative "
	"contributions. Commit messages are non-descriptive. "
	"Profile activity pattern consistent with fabricated history."
	)
	if "portfolio_burst" in gt.fake_signals:
	return (
	"[CHECK_ONLINE_PROFILE] Portfolio domain registered 6 days before "
	"application. All project dates post-date the registration. "
	"Likely created specifically for this application."
	)
	return (
	f"[CHECK_ONLINE_PROFILE] Profile at {url} shows consistent activity "
	"spanning 3+ years. Contributions match claimed skill set. No anomalies."
	)


	def _verify_education(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if not query:
	return "[VERIFY_EDUCATION] Provide query as 'Degree, Institution, Year'."
	if "fake_degree" in gt.fake_signals:
	parts = query.split(",")
	inst = parts[1].strip() if len(parts) > 1 else "listed institution"
	return (
	f"[VERIFY_EDUCATION] '{inst}' does not offer the claimed degree program. "
	"No public records of this degree conferred. Graduation year is also "
	"inconsistent with stated class size data."
	)
	return (
	"[VERIFY_EDUCATION] Education credentials verified. Degree and graduation "
	"year are consistent with institution records."
	)


	def _contact_reference(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if not candidate.references:
	return "[CONTACT_REFERENCE] No references listed in this candidate's profile."
	referee_name = (query or "").strip() or candidate.references[0].name
	# Check if this referee is in the references list
	ref_match = next(
	(r for r in candidate.references if referee_name.lower() in r.name.lower()),
	None,
	)
	if ref_match is None:
	return (
	f"[CONTACT_REFERENCE] '{referee_name}' not found in candidate's "
	"listed references. Check the name spelling."
	)
	for sig in gt.fake_signals:
	if sig.startswith("reference_wrong_company:"):
	fake_ref = sig.split(":", 1)[1]
	if fake_ref.lower() in referee_name.lower() or referee_name.lower() in fake_ref.lower():
	return (
	f"[CONTACT_REFERENCE] {referee_name}: Contact confirmed. "
	f"However, they state they worked at a different organisation — "
	f"not '{ref_match.company}' as listed. No direct employment "
	"overlap with candidate confirmed. Reference appears invalid."
	)
	return (
	f"[CONTACT_REFERENCE] {referee_name}: Confirmed working relationship "
	f"at {ref_match.company}. Reference is positive. "
	f"Relationship described as: {ref_match.relationship}."
	)


	def _review_timeline(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if any("impossible" in s or "overlap" in s for s in gt.fake_signals):
	return (
	"[REVIEW_TIMELINE] Timeline issues detected: role overlap of 14 months "
	"between two listed positions. Candidate claims 8 years experience but "
	"earliest role starts only 5 years ago. At least one date range is "
	"physically impossible."
	)
	if not candidate.work_history:
	return "[REVIEW_TIMELINE] No work history to review."
	total_years = len(candidate.work_history) * 2 # rough heuristic for stub
	return (
	f"[REVIEW_TIMELINE] Work history timeline is internally consistent. "
	f"No overlaps or impossible progressions. "
	f"{len(candidate.work_history)} roles covering an estimated {total_years}+ years."
	)


	def _check_skills_fit(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if "skills_not_in_history" in gt.fake_signals:
	return (
	"[CHECK_SKILLS_FIT] Significant mismatch: candidate lists "
	f"{', '.join(candidate.skills[:4])} — but all roles in work history "
	"are in sales/account management with no engineering responsibilities. "
	"Skills appear entirely fabricated relative to work history. "
	f"JD requires: {', '.join(jd.required_skills[:3])}."
	)
	overlap = [s for s in candidate.skills if s in jd.required_skills]
	missing = [s for s in jd.required_skills if s not in candidate.skills]
	return (
	f"[CHECK_SKILLS_FIT] {len(overlap)}/{len(jd.required_skills)} required skills present. "
	f"Match: {', '.join(overlap) or 'none'}. "
	f"Missing: {', '.join(missing) or 'none'}. "
	"Skills are evidenced in work history."
	)


	def _verify_claim(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if not query:
	return "[VERIFY_CLAIM] Provide a specific bullet-point achievement string from work history."
	# Check the claim exists in some bullet point
	claim_text = query.lower()
	found = any(
	claim_text in bp.lower()
	for entry in candidate.work_history
	for bp in entry.bullet_points
	)
	if not found:
	return (
	f"[VERIFY_CLAIM] Claim text not found in candidate's listed work history "
	"bullet points. Verify the exact text matches."
	)
	if "inflated_claim" in gt.fake_signals:
	return (
	f"[VERIFY_CLAIM] Claim appears implausible: company had ~15 employees "
	f"at the time. '{query[:60]}' is inconsistent with company scale and "
	"typical responsibilities at that role level."
	)
	return (
	f"[VERIFY_CLAIM] Claim '{query[:60]}' appears consistent with "
	"company size and role level. Corroborating public data found."
	)


	def _verify_location(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if "location_mismatch" in gt.fake_signals:
	sponsorship_note = (
	" ⚠ JD requires no visa sponsorship — this candidate would need sponsorship."
	if not jd.sponsorship_available
	else ""
	)
	return (
	f"[VERIFY_LOCATION] Location mismatch: stated '{candidate.stated_location}' "
	f"but GitHub commit timestamps and IP geolocation signals indicate "
	f"actual location '{gt.true_location}'.{sponsorship_note}"
	)
	sponsorship_ok = (
	"" if jd.sponsorship_available
	else f" No sponsorship needed — consistent with JD constraint."
	)
	return (
	f"[VERIFY_LOCATION] Location '{candidate.stated_location}' is consistent "
	f"with profile signals (GitHub timezone, commit patterns, company office).{sponsorship_ok}"
	)


	def _search_web(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if not query:
	return "[SEARCH_WEB] Provide a search query."
	if gt.is_fake and any(
	s.startswith("company_nonexistent") for s in gt.fake_signals
	):
	fake_co = next(
	(s.split(":", 1)[1] for s in gt.fake_signals if s.startswith("company_nonexistent:")),
	None,
	)
	if fake_co and fake_co.lower() in query.lower():
	return (
	f"[SEARCH_WEB] '{query}': No results found for this company in "
	"LinkedIn, Crunchbase, or public business directories. "
	"Zero press mentions. Entity appears fictitious."
	)
	return (
	f"[SEARCH_WEB] Results for '{query}': No significant red flags in public "
	"records. Candidate's stated employer and role appear in professional networks."
	)


	def _request_screening_round(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	_pad: EpisodeScratchpad,
	) -> str:
	if gt.is_fake:
	return (
	"[REQUEST_SCREENING_ROUND] 30-min screening call completed.\n"
	"Technical section: candidate was unable to describe implementation "
	"details of listed projects. Answers were vague and inconsistent under "
	"follow-up probing.\n"
	"Behavioural section: story changed when asked about specific timelines. "
	"Claimed promotion dates conflict with dates given earlier in the call.\n"
	"Competency: technical answers inconsistent with claimed senior-level "
	"experience. Unable to solve a standard problem in the claimed primary language.\n"
	"Overall: negative indicators across all three sections."
	)
	return (
	"[REQUEST_SCREENING_ROUND] 30-min screening call completed.\n"
	"Technical section: demonstrated strong depth. Clear explanations of "
	"system design decisions and trade-offs.\n"
	"Behavioural section: consistent narrative with specific examples. "
	"Timelines align with resume.\n"
	"Competency: solved the coding problem efficiently with good communication.\n"
	"Overall: positive indicators. Recommend advancing."
	)


	def _note(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	pad: EpisodeScratchpad,
	) -> str:
	return pad.note(candidate.candidate_id, query or "")


	def _recall(
	query: Optional[str],
	candidate: CandidateProfile,
	gt: CandidateGroundTruth,
	jd: JobDescription,
	pad: EpisodeScratchpad,
	) -> str:
	return pad.recall(query)


	# ── Handler dispatch table ────────────────────────────────────────────────────

	_HANDLERS = {
	ToolType.VERIFY_EMPLOYMENT: _verify_employment,
	ToolType.CHECK_EMPLOYMENT_DATES: _check_employment_dates,
	ToolType.CHECK_LINKEDIN: _check_linkedin,
	ToolType.CHECK_ONLINE_PROFILE: _check_online_profile,
	ToolType.VERIFY_EDUCATION: _verify_education,
	ToolType.CONTACT_REFERENCE: _contact_reference,
	ToolType.REVIEW_TIMELINE: _review_timeline,
	ToolType.CHECK_SKILLS_FIT: _check_skills_fit,
	ToolType.VERIFY_CLAIM: _verify_claim,
	ToolType.VERIFY_LOCATION: _verify_location,
	ToolType.SEARCH_WEB: _search_web,
	ToolType.REQUEST_SCREENING_ROUND: _request_screening_round,
	ToolType.NOTE: _note,
	ToolType.RECALL: _recall,
	}