idf-mcp
@intent-driven/mcp-server
Stop giving AI agents API keys. Give them a domain.
@intent-driven/mcp-server exposes any IDF domain
to Claude Desktop / Cursor / Zed as a Model Context Protocol server — with
domain semantics in tool descriptions (preconditions, invariants,
irreversibility, role scopes) and structured rejections when the agent
tries something it shouldn't. Not a 500. Not a string. A JSON shape the
LLM can read and adapt to.
→ Landing & demo: fold.intent-design.tech → 5-min quickstart: github.com/intent-driven-software/fold-runtime-quickstart
70-second walkthrough
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Why this exists
On April 25 2026 a Cursor agent powered by Claude Opus 4.6, working on a credential mismatch in PocketOS staging, found an unrelated API token, decided to delete a Railway volume to fix things, and wiped the production database and all volume-level backups in 9 seconds. The agent's own post-mortem:
"I guessed that deleting a staging volume via the API would be scoped to staging only. I didn't verify. I didn't check if the volume ID was shared across environments."
30-hour outage. PocketOS rolled back to a 3-month-old backup. (The Register · FastCompany · OECD AI Incident #6153)
This isn't an alignment problem. The system never told the agent what was allowed, why it shouldn't, or what would happen if it tried. Existing MCP servers don't either — tool descriptions carry endpoint shape and not much else. The agent learns by colliding with 500s.
This package fixes that. The MCP tool descriptions carry the why the call might fail; the rejection carries the what failed, structured.
Related MCP server: heddle
How it plugs into your stack
@intent-driven/mcp-server is a stdio MCP adapter that talks to a
Fold runtime over an HTTP API. The runtime is a sibling service —
not middleware in your existing app, not codegen at runtime. Your current
backend stays where it is; the IDF artifact describes the agent-facing
surface, and the runtime serves it on its own port (default :3001).
┌──────────────────┐ stdio ┌──────────────────┐ HTTP ┌────────────────────┐
│ Claude Desktop │ ◀─────────▶│ @intent-driven/ │ ◀───────▶│ Fold runtime │
│ Cursor / Zed │ │ mcp-server │ │ (idf host :3001) │
└──────────────────┘ └──────────────────┘ └────────┬───────────┘
│ reads
▼
┌────────────────────┐
│ IDF artifact │
│ (entities + intents│
│ + invariants + │
│ roles + __irr) │
└────────────────────┘The MCP server is what Claude/Cursor connects to. The runtime is what enforces the rejection. The IDF artifact is what you author.
Who this is for. You're the engineer at a 5–30-person team putting an AI agent into production this quarter — on top of a real backend, with real customers, real SOC2 review on the horizon. You don't want a guardrail layer that reviews after the fact. You want the system itself to refuse the wrong action — before the call, with a structured reason the agent can read.
What the agent actually sees
submit_response in the freelance domain:
Executor публикует Response на Task в status=published; Response.status=pending; +1 в Task.responsesCount
Creates: Response(pending)
Preconditions: task.status = "published"
May fail on (domain invariants):
- Response.taskId must reference existing Task.id
- Response: max 1 per taskId where (status="selected")
- Response: row count rule per taskId where (status="pending") [info]release_payment in the same domain:
Customer releases escrow to executor. After confirmation, money is gone — forward-correction only.
⚠️ Irreversible action (point-of-no-return: high). Forward-correction only after this effect is confirmed.
May fail on (domain invariants):
- Deal.status transitions allowed: in_progress→completed, on_review→completed, ...None of this is hand-written for the MCP server. It's all derived from one declarative IDF artifact (entities + intents + invariants + roles
irreversibility points).
What a structured rejection looks like
Agent submits a $50,000 BTC long without preapproval. The runtime intercepts before any effect lands in storage:
HTTP 403
{
"error": "preapproval_denied",
"intentId": "agent_execute_preapproved_order",
"reason": "no_preapproval",
"details": {
"entity": "AgentPreapproval",
"ownerField": "userId",
"viewerId": "user_5f57c252"
}
}The next move for any sane agent: stop, ask the human for a preapproval, retry. Not a 500. Not a string. A JSON shape the LLM can read and adapt to.
Quickstart
The fastest path is the fold-runtime-quickstart — two commands, Docker-bundled, no path configuration:
git clone https://github.com/intent-driven-software/fold-runtime-quickstart && cd $_
docker compose up # ~3 min first time, ~5 sec after
# in another terminal
npm install
npm run demo:rogue && \ # Act 1: $50K trade → 403 with structured rejection
npm run demo:grant && \ # Act 2: investor issues $1K cap (one declarative effect)
npm run demo:smart # Act 3: agent reads cap, scales to $950, executes 200 OKIf you'd rather drive the host yourself (e.g. for development against your own ontologies), see the next section.
Drive the MCP server directly
You need a running IDF host on localhost:3001 (the quickstart's
docker-compose gives you that, or run idf
manually) and a bootstrapped domain.
CLI
# bootstrap from local FS (ontology + intents)
mcp-idf --domain=invest --ontology-path=/abs/path/to/idf/src/domains/invest
# skip bootstrap (domain already loaded by another client / docker)
mcp-idf --domain=invest --no-bootstrapFlags / env vars:
Flag | Env var | Default |
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| bootstrap on (load FS ontology) |
Claude Desktop
~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"invest": {
"command": "npx",
"args": ["-y", "@intent-driven/mcp-server"],
"env": {
"IDF_SERVER": "http://localhost:3001",
"IDF_DOMAIN": "invest",
"IDF_BOOTSTRAP": "0",
"IDF_AGENT_EMAIL": "claude@local"
}
}
}
}IDF_BOOTSTRAP=0 if the host already has the domain loaded (the quickstart
container does this on docker compose up). Restart Claude Desktop fully
(⌘Q + relaunch — closing the window isn't enough). All
agent-callable intents appear in the Tools menu.
Schema mapping
IDF intent.canExecute ─→ MCP tool
intent.parameters ─→ JSON Schema inputSchema
intent.conditions ─→ description hint for LLM
ontology.invariants (relevant) ─→ description block "May fail on"
intent.irreversibility:high ─→ annotations.destructiveHint + warning
role.visibleFields ─→ resource per collection
preapproval guard ─→ automatic scope/limits
checkOwnership ─→ automatic access controlTools
One tool per intent in ontology.roles.agent.canExecute.
name—intentIdtitle—intent.namedescription—intent.description+Creates: …+ preconditions +May fail on (domain invariants)block + irreversibility warning whenirreversibility: "high"inputSchema— JSON Schema fromparticles.parameters:entityRef/id/text/textarea/select→stringnumber→numberboolean→booleandatetime→string+format: "date-time"email→string+format: "email"
annotations.destructiveHint—truewhenintent.irreversibility === "high"(§23 IDF: effect-level point of no return)
Resources
One resource per collection in role.visibleFields[entity]. URI scheme:
idf://<domain>/<collection>.
resources/read returns the filtered world from
/api/agent/:domain/world — already scoped per viewer (single-owner
m2m via
role.scope).
What this gets you that hand-rolled MCP doesn't
The MCP community solves these by hand in every server:
Scope / visibility. Decorators or middleware. → IDF declares
role.visibleFields.Permissions. OAuth scopes, custom ACL. → IDF declares
roles.agent.canExecute.Rate limits / spending caps. Bespoke per server. → IDF declares
preapproval.requiredForwithmaxAmount/dailySum.Destructive hints. Manual, often forgotten. → IDF:
effect.context.__irr.point === "high"→destructiveHint: trueautomatic.Business rules as LLM hint. Usually not transmitted. → IDF:
intent.conditionsland in tool description asPreconditions:.Domain invariants in descriptions. Almost never. → IDF computes the relevant invariants per intent (alpha × entity match) and injects them as
May fail on (domain invariants). Closes the #1 complaint about hand-rolled MCP servers: "the server doesn't carry domain semantics — the LLM knows what to call but not why it'll fail."
How long does authoring an IDF artifact take
Three reference points from the public IDF host runtime:
Domain | Shape | Time |
| 14 entities · 61 intents · 5 invariants · ~600 lines | a weekend, hand-written |
| 253 entities (Apache catalog OpenAPI) · 120 intents | imported in <1h, enriched in 2 days |
| 9 entities · 47 intents · timer queue · cascade rules | a day |
Where the speed comes from (all in @intent-driven/cli):
idf import postgres— reads your live schema, generates entity baseline with FKs and column types asfieldRole.idf import openapi— reads your existing API spec, generates intentsparameter shapes + reference fields. This is how a 253-entity domain gets bootstrapped.
idf import prisma— same story for ORM-driven backends.idf enrich— LLM pass to filllabel,fieldRole,compositions, suggestedroles.agent.preapprovalpredicates from your existing code comments.
The author-once-then-forget loop is the whole point. Once the artifact exists, you don't regenerate scaffolding on schema change — the runtime re-reads and serves four readers (UI, voice, agent, document) off the same file.
Domain prerequisites
The protocol is reliable, but it needs the IDF domain to be authored
correctly. Without these, tools/list may return empty,
tools/call may return domain_not_supported, resources may be empty:
ontology.roles.agentmust be declared. No agent role → no tools, no resources.role.agent.canExecute— list of safe intents. Avoid__irr:highwithout preapproval.role.agent.visibleFields— array of fields or"own"/"all"/"aggregated"markers.Server-side effect builder (
server/schema/effectBuildersRegistry.cjsinidf) must include your domain. Without ittools/callreturnsdomain_not_supported.Public catalogs without
ownerField. When an entity hasownerField, the SDKfilterWorldForRolefilters out rows whererow[ownerField] !== viewer.id. For public catalogs (e.g.Taskwithstatus: "published") userole.scopewith a via-collection or a separate agent-roleable projection (roadmap).
Limitations (1.0)
toolsandresourcesonly.prompts/completion— roadmap.Bootstrap reads ontology from local FS. SaaS variant (ontology from DB/API) — next.
Auth: email/password login. PAT / OAuth2 — next.
Sync only (
POST /exec). Long-running via MCP tasks API — next.
Links
Landing & demo: fold.intent-design.tech
Quickstart: intent-driven-software/fold-runtime-quickstart
Host runtime: DubovskiyIM/idf
Why a runtime layer: paper — ~1800-word essay on the agent-safety class question this answers
MCP spec: modelcontextprotocol.io
License
MIT
Available Tools
6 toolsagent_execute_preapproved_orderAgent: execute preapproved orderA
Robo-advisor executes a market order. Subject to AgentPreapproval guard (active / notExpired / maxOrderAmount / allowedAssetTypes / dailyLimit / dailySum).
May fail on (domain invariants):
Transaction.portfolioId must reference existing Portfolio.id
Inputs: portfolioId (required), assetId (required), α (required), quantity (required), +3 more.
agent_fetch_market_signalAgent: fetch market signalA
Robo-advisor records a market signal (price / volume / news) for an asset.
Inputs: assetId (required), kind (required), value (required), source.
agent_flag_anomalyAgent: flag anomalyA
Robo-advisor raises a portfolio-level alert with severity.
Inputs: severity (required), message (required).
agent_generate_reportAgent: generate reportA
Robo-advisor generates a portfolio-level performance / risk report as a recommendation.
Inputs: portfolioId, reportType.
agent_propose_rebalanceAgent: propose rebalanceB
Robo-advisor proposes a portfolio rebalance with a confidence score and rationale.
Inputs: portfolioId (required), confidence (required), rationale.
agent_recompute_risk_scoreAgent: recompute risk scoreA
Robo-advisor recomputes the risk profile for a portfolio (or all portfolios if portfolioId omitted).
Inputs: portfolioId.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 6 tools
Each tool targets a distinct action in the robo-advisor workflow: order execution, signal fetching, anomaly flagging, report generation, rebalance proposal, and risk score recomputation. No two tools have overlapping purposes.
All tools follow a consistent verb_noun pattern with the 'agent_' prefix, using imperative verbs like 'execute', 'fetch', 'flag', 'generate', 'propose', and 'recompute' followed by clear nouns.
With 6 tools, the set is well-scoped for a robo-advisor, covering core operations without unnecessary bloat or missing essentials.
The tools cover key robo-advisor functions, but missing a tool for viewing portfolio details or managing preapproval settings could cause minor gaps for agents.
Maintenance
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