forgeplan context

forgeplan context returns everything an AI agent needs to reason about an artifact in a single call: the artifact body, its parent/child relationships, validation results, R_eff score, and related memory. Instead of making five round-trips (get, validate, score, graph, memory), the agent asks once and receives a complete picture.

It is the primary data endpoint for the Forgeplan MCP server — every forgeplan_context tool call goes through this command. CLI use is mostly for debugging what the agent actually sees.

When to use

An AI agent (via MCP) needs full state of an artifact before editing or reasoning
Debugging what context is passed to forgeplan reason or forgeplan generate
Writing an external script that analyzes artifact health and relationships
Auditing whether an artifact has enough linked evidence to be trustworthy
Preparing input for a manual review — single output covers everything

When NOT to use

You only need the artifact body — use forgeplan get <ID> (lighter payload)
You only need validation results — use forgeplan validate <ID>
You only need the score — use forgeplan score <ID>
You are rendering a human dashboard — use forgeplan health or forgeplan status

Usage

forgeplan context [OPTIONS] <ID>

Arguments

  <ID>  Artifact ID (PRD, RFC, ADR, Epic, Problem, ...)

Options

      --json     Output as JSON for machine consumption (primary mode for AI agents)
  -h, --help     Print help
  -V, --version  Print version

Examples

Example 1: Full context for a PRD

forgeplan context PRD-001

Prints the PRD body, validation verdict, R_eff score, upstream/downstream links, and any related notes. Human-readable markdown by default.

Example 2: JSON payload for an agent or script

forgeplan context PRD-001 --json

Emits structured JSON with top-level keys:

{
  "artifact": { "id": "PRD-001", "kind": "prd", "status": "active", "body": "..." },
  "validation": { "verdict": "PASS", "must": [], "should": [] },
  "scoring": { "r_eff": 0.82, "evidence_count": 4 },
  "graph": {
    "parents": [{ "id": "EPIC-002", "relation": "child_of" }],
    "children": [{ "id": "RFC-018", "relation": "implements" }],
    "evidence": [{ "id": "EVID-042", "verdict": "supports" }]
  },
  "memory": [{ "key": "decision", "value": "..." }]
}

Example 3: Debugging MCP agent behavior

forgeplan context PRD-019 --json | jq '.graph.evidence'

If an agent claims “no evidence linked” but you disagree, run this to see exactly what the MCP layer is returning.

Output interpretation

The JSON payload has five sections; each is optional if empty:

artifact — frontmatter + markdown body exactly as stored on disk
validation — verdict is PASS or FAIL; must and should list rule violations
scoring — r_eff is the weakest-link evidence score (0.0-1.0); evidence_count is the number of linked EvidencePacks
graph — parents, children, evidence arrays with IDs and relation type
memory — decision-memory entries scoped to this artifact

Red flags:

r_eff: 0.0 and evidence_count: 0 on an active artifact — blind spot, create evidence
validation.verdict: "FAIL" on an active artifact — should never happen, shows ingestion drift
Empty graph.parents on a PRD that claims to implement an Epic — missing link

How it fits the workflow

Shape → Validate → Reason → Code → Evidence → Activate
                    ^                   ^
                context feeds here   context used in audits

Called automatically by forgeplan reason, forgeplan generate, and MCP tools
Use manually to preview what an agent will see before invoking reasoning
Pair with forgeplan audit for bulk context sweeps across many artifacts