code-review-graph/code_review_graph/prompts.py

"""MCP prompt templates for Code Review Graph.

Provides 6 pre-built prompt workflows, all enforcing token-efficient
detail_level="minimal" first patterns with get_minimal_context entry point.

1. review_changes   - pre-commit review using detect_changes + affected_flows
2. architecture_map - architecture docs using communities, flows, Mermaid
3. debug_issue      - guided debugging using search, flow tracing
4. onboard_developer - new dev orientation using stats, architecture, flows
5. pre_merge_check  - PR readiness with risk scoring, test gaps, dead code
6. unified_review   - three-layer review: graph context + scoring + dedupe + report
"""

from __future__ import annotations

from fastmcp.prompts.prompt import Message

_TOKEN_EFFICIENCY_PREAMBLE = (  # nosec B105 — prompt template, not a password
    """\
## Rules for Token-Efficient Graph Usage
1. ALWAYS call `get_minimal_context` first with a task description.
2. Use `detail_level="minimal"` on all tool calls unless the minimal output \
is insufficient.
3. Only escalate to `detail_level="standard"` or `"verbose"` for the specific \
entities that need deeper inspection.
4. Never request more than 3 tool calls per turn unless absolutely necessary.
5. Prefer targeted queries (query_graph with a specific symbol) over broad \
scans (list_communities with full members).
6. When reviewing changes: detect_changes(detail_level="minimal") → only \
expand on high-risk items.
"""
)


def _user(content: str) -> list[Message]:
    """Wrap content as a single-message user prompt.

    fastmcp >=3.2 rejects raw dicts in prompt return values; each message
    must be a ``Message`` instance (or a plain ``str``). We standardise on
    ``Message`` so role is explicit and future multi-turn prompts compose
    naturally.
    """
    return [Message(role="user", content=content)]


def review_changes_prompt(base: str = "HEAD~1") -> list[Message]:
    """Pre-commit review workflow.

    Args:
        base: Git ref to diff against. Default: HEAD~1.
    """
    return _user(
        f"{_TOKEN_EFFICIENCY_PREAMBLE}\n"
        f"## Review Workflow\n"
        f'1. Call `get_minimal_context(task="review changes against '
        f'{base}")` to get risk overview.\n'
        f'2. If risk is "low": call '
        f'`detect_changes(detail_level="minimal")` → report summary '
        f"+ any test gaps.\n"
        f'3. If risk is "medium" or "high":\n'
        f'   a. Call `detect_changes(detail_level="standard")` for '
        f"full change list.\n"
        f"   b. For each high-risk function, call "
        f'`query_graph(pattern="callers_of", target=<func>, '
        f'detail_level="minimal")`.\n'
        f'   c. Call `get_affected_flows(detail_level="minimal")` '
        f"only if >3 changed functions.\n"
        f"4. Summarize: risk level, what changed, test gaps, "
        f"specific improvements needed.\n\n"
        f"Do NOT call get_review_context unless you need source code "
        f"snippets for a specific function."
    )


def architecture_map_prompt() -> list[Message]:
    """Architecture documentation workflow."""
    return _user(
        f"{_TOKEN_EFFICIENCY_PREAMBLE}\n"
        "## Architecture Mapping Workflow\n"
        '1. Call `get_minimal_context(task="map architecture")`.\n'
        '2. Call `get_architecture_overview(detail_level="minimal")` '
        "for community coupling summary.\n"
        '3. Call `list_flows(detail_level="minimal")` for critical '
        "flow names + criticality scores.\n"
        "4. Only call `get_community(name=<X>, "
        'detail_level="standard")` for the 1-2 communities the user '
        "is most interested in.\n"
        "5. Produce a concise Mermaid diagram showing communities as "
        "boxes and key flows as arrows."
    )


def debug_issue_prompt(description: str = "") -> list[Message]:
    """Guided debugging workflow.

    Args:
        description: Description of the issue to debug.
    """
    desc_part = description or "<description>"
    return _user(
        f"{_TOKEN_EFFICIENCY_PREAMBLE}\n"
        "## Debug Workflow\n"
        f'1. Call `get_minimal_context(task="debug: '
        f'{desc_part}")`.\n'
        "2. Call `semantic_search_nodes(query=<keywords from "
        'description>, detail_level="minimal", limit=5)`.\n'
        "3. For the top 1-2 results, call "
        '`query_graph(pattern="callers_of", target=<name>, '
        'detail_level="minimal")`.\n'
        "4. If the issue involves execution flow: call "
        "`get_flow(name=<relevant flow>)` for the single most "
        "relevant flow.\n"
        "5. Only call `get_review_context` or `get_impact_radius` "
        "if you need to trace the blast radius of a specific change."
    )


def onboard_developer_prompt() -> list[Message]:
    """New developer orientation workflow."""
    return _user(
        f"{_TOKEN_EFFICIENCY_PREAMBLE}\n"
        "## Onboarding Workflow\n"
        '1. Call `get_minimal_context(task="onboard developer")`.\n'
        "2. Call `list_graph_stats()` for technology overview.\n"
        '3. Call `get_architecture_overview(detail_level="minimal")` '
        "for the 30-second mental model.\n"
        '4. Call `list_communities(detail_level="minimal")` — '
        "present as a table of module names + sizes.\n"
        '5. Call `list_flows(detail_level="minimal")` — highlight '
        "the top 3 critical flows.\n"
        "6. Only drill into a specific community or flow if the "
        "developer asks."
    )


def pre_merge_check_prompt(base: str = "HEAD~1") -> list[Message]:
    """PR readiness check workflow.

    Args:
        base: Git ref to diff against. Default: HEAD~1.
    """
    return _user(
        f"{_TOKEN_EFFICIENCY_PREAMBLE}\n"
        "## Pre-Merge Check Workflow\n"
        '1. Call `get_minimal_context(task="pre-merge check")`.\n'
        '2. Call `detect_changes(detail_level="minimal")` for risk '
        "score and test gaps.\n"
        "3. If risk > 0.4: call "
        '`get_affected_flows(detail_level="minimal")`.\n'
        "4. If test_gap_count > 0: call "
        '`query_graph(pattern="tests_for", '
        'target=<each untested function>, detail_level="minimal")` '
        "for up to 3 functions.\n"
        '5. Call `refactor(mode="dead_code", '
        'detail_level="minimal")` to check for newly dead code.\n'
        "6. Only call `find_large_functions` or `get_impact_radius` "
        "if risk > 0.7.\n"
        "7. Output: GO/NO-GO recommendation with 1-sentence "
        "justification + list of required follow-ups."
    )


def unified_review_prompt(
    base: str = "HEAD~1",
    tier: str = "standard",
) -> list[Message]:
    """Three-layer unified review workflow (READ-ONLY).

    Fuses CRG graph context with the objective scoring metrics
    (score_review), finding merge (dedupe_findings), and the standalone
    HTML report (generate_report).  Every finding is presented for a manual
    fix decision -- this workflow never modifies code.

    Args:
        base: Git ref to diff against. Default: HEAD~1.
        tier: Review tier.  "fast" (Layer 1 + blockers only),
            "standard" (all layers), "strict" (full + per-item
            confirmation).  Default: standard.
    """
    tier_notes = {
        "fast": (
            "fast tier: run Layers 1 and the blocker check only; "
            "skip Layer 2 metrics and the report."
        ),
        "strict": (
            "strict tier: full review; every blocker and major finding "
            "needs per-item user confirmation before it is recorded."
        ),
    }.get(tier, "standard tier: run all layers.")
    return _user(
        f"{_TOKEN_EFFICIENCY_PREAMBLE}\n"
        f"## Unified Review Workflow (base={base}, tier={tier})\n"
        f"{tier_notes}\n"
        "**READ-ONLY.** Present every finding for a manual fix decision. "
        "Never modify code, commit, or push.\n"
        '1. Call `get_minimal_context(task="unified review")` for the '
        "risk overview.\n"
        '2. Call `build_or_update_graph()` to ensure the graph is '
        "current.\n"
        '3. Call `detect_changes(detail_level="minimal")` for changed '
        "files, risk score, test gaps and affected flows.\n"
        '4. Call `score_review(detail_level="standard")` for the '
        "objective metrics (sql_risk, exception_coverage, redundancy, "
        "high-risk density, vulnerability).  Trust the tool grades; "
        "LLM-judged metrics are in `llm_judged`.\n"
        "5. Review the changed source (Layer 1 chain decomposition) and "
        "produce findings with severity (blocker/major/minor), "
        "confidence (1-10), file:line and a proposed fix.\n"
        '6. Call `dedupe_findings(findings=<your findings>)` to merge '
        "by fingerprint, boost multi-source confidence and compute the "
        "PR quality score.\n"
        '7. Call `generate_report(review_data=<verdict, tier, scope, '
        'metrics, merged findings>)` to write code-review-report.html.\n'
        "8. Output: verdict (✅ PASS / ❌ FAIL), severity counts, each "
        "issue with confidence + fix, and the manual-review items.  "
        "Any blocker → verdict ❌ FAIL."
    )