intigriti-mcp

MCP server for the Intigriti bug bounty platform. Lists programs, pulls scope details, diffs scope changes over time, and recommends the best-fit programs based on your skills profile — with a boost for programs whose scope was recently updated.

Implements the Intigriti Researcher API v1. Endpoint paths (/v1/programs, /v1/programs/{id}), response shapes (pagination envelope, enum/money view-models), and field mappings are verified against Intigriti's published OpenAPI spec.

Tools

Related MCP server: VulneraMCP

Setup

npm install
npm run build

Set environment variables:

export INTIGRITI_API_TOKEN="your-researcher-api-token"          # required
export INTIGRITI_SKILLS_PATH="./skills.json"                    # optional, default shown
export INTIGRITI_SNAPSHOT_PATH="./data/scope-snapshots.json"    # optional, default shown

Generate a Personal Access Token (PAT) from your Intigriti profile: Personal access tokens. Auth is Authorization: Bearer <PAT>.

Copy skills.example.json to skills.json and edit it to reflect your strengths — each entry is a skill/vuln-class, a confidence weight (0–1), and the asset types it applies to:

{ "skill": "ssrf", "weight": 0.8, "appliesTo": ["api", "url"] }

skills.json is git-ignored since it's personal.

Running

Standalone:

npm start

As an MCP server in your client config (point at the built entry point with the env vars above):

{
  "mcp": {
    "intigriti": {
      "type": "local",
      "command": ["node", "/path/to/intigriti-mcp/dist/index.js"],
      "enabled": true,
      "environment": {
        "INTIGRITI_API_TOKEN": "{env:INTIGRITI_API_TOKEN}",
        "INTIGRITI_SKILLS_PATH": "/path/to/skills.json",
        "INTIGRITI_SNAPSHOT_PATH": "/path/to/scope-snapshots.json"
      }
    }
  }
}

How scoring works

recommend_program fetches every active program's scope, then for each skill in your manifest counts how many in-scope assets match that skill's declared asset types (appliesTo):

score = Σ(skill weight × matching asset count) / total in-scope assets

Normalizing by asset count means a program isn't favored just for being bigger. See src/scoring.ts; the matching logic is intentionally simple to start — extend it there as your manifest gets richer.

Recency boost

Intigriti publishes a scope version timestamp per program (domains.createdAt). If it's within the recency window (default 14 days), the score is multiplied by a factor that decays linearly from a max boost (default 1.25× for a just-updated scope) down to 1.0 at the window edge. This surfaces freshly-changed programs before they cool off. Boosted results include a note like:

Recency boost 1.214x — scope published 2.0d ago (window 14d, max 1.25x)

Tune it per call: recommend_program(topN=10, recencyWindowDays=7, maxRecencyBoost=1.5).

Scope fidelity

• Asset types are normalized to the manifest's vocabulary (website/wildcard → url, web_service → api).

• Out-of-scope assets (tier "Out Of Scope") are excluded from scoring.

Persistence

Scope snapshots are stored as flat JSON at INTIGRITI_SNAPSHOT_PATH (default ./data/scope-snapshots.json), keyed by program ID. Swap ScopeStore for a SQLite-backed version if this grows large or you want historical diff queries instead of just "since last check".

Project layout

• src/intigritiClient.ts — API client + response mapping

• src/scoring.ts — program scoring and recency boost

• src/scopeStore.ts — scope snapshot persistence and diffing

• src/skillsManifest.ts — loads your skills.json

• src/index.ts — MCP tool wiring