Files
sim/apps/docs/content/docs/en/blocks/function.mdx
Waleed 147ac89672 feat(docs): fill documentation gaps across platform features (#4110)
* feat(docs): fill documentation gaps across platform features

* fix(docs): address PR review comments on chat OTP cookies and MCP env var placeholders

* fix(docs): replace smart quotes with straight quotes in JSX attributes

* update(docs): update mcp, custom tools, and variables docs

* Fix grammar

* mothership docs, tags, connectors, api, chat deploy, etc

* more info

* more

* feat(docs): auto-generate per-provider trigger documentation

Extends scripts/generate-docs.ts to produce one MDX page per trigger
provider (39 pages) in apps/docs/content/docs/en/triggers/. The 5
hand-written pages (index, start, schedule, webhook, rss) are never
touched.

Key additions to the generation script:
- resolveConstVariable() resolves module-level const spreads so
  providers like Vercel that build outputs from const variables (not
  just functions) are fully documented
- resolveTriggerBuilderFunction() extended to expand variable spreads
  (...varName) in addition to function-call spreads (...fn())
- groupTriggersByProvider() deduplicates v1/v2 trigger variants by
  name, keeping the highest-versioned one per provider
- writeIconMapping() adds bare-name aliases for versioned block types
  (github_v2 → github, fireflies_v2 → fireflies, etc.) so
  BlockInfoCard resolves icons for all 39 trigger providers
- extractTriggerConfigFields() filters readOnly display blocks (webhook
  URL displays, sample payloads, curl examples) from config tables

Each generated page includes: BlockInfoCard with correct icon/color,
trigger count, polling note where applicable, Configuration table, and
Output table for every trigger. No "Type:" lines.

* refactor(docs): align trigger docs structure with tools docs

- Use ### `trigger_id` headings (matching ### `tool_id` in tools docs)
- Wrap all trigger sections under a ## Triggers header
- Rename Configuration/Output to #### level (matching #### Input/Output)
- Use Parameter column header to match tools docs table style
- Map UI widget types to semantic types: short-input/long-input/dropdown
  → string, switch → boolean, slider → number, oauth-input → string

* refactor(docs): use human-readable names for trigger section headings

Trigger IDs are internal identifiers; users scan by name. Switch from
### `trigger_id` to ### Trigger Name for cleaner sidebar navigation
and better readability.

* fix(docs): resolve subBlock builder functions for all trigger Config sections

Extends generate-docs.ts to parse subBlock builder functions so all 15
providers previously missing Configuration sections now generate them.

Handles three patterns:
- `buildTriggerSubBlocks({extraFields: buildX(...)})` — extracts extra
  fields from the call site and resolves them from the provider's utils.ts
- `return [...]` — direct array return (Attio, Confluence, etc.)
- `blocks.push(...)` — imperative push pattern (Linear, Ashby)

Also resolves const-reference field IDs (SCREAMING_CASE) by searching
the webhook provider constants cache, fixing Gong's `gongJwtPublicKeyPem`
field which was previously unresolvable. Adds title-as-description fallback
for OAuth credential fields that have no explicit description.

* fix(docs): correctly destructure nested implicit-object trigger outputs

Fixes a parser bug where output fields with no top-level `type` key but
child fields each having their own `type`/`description` were incorrectly
parsed. The `type:` and `description:` regex matches were not
depth-aware, so values from nested children bled into the parent field.

Changes:
- Add `isAtDepthZero()` helper for brace-depth-aware regex matching
- Fix `parseFieldContent` to only match `type:` at brace depth 0
- Fix `extractDescription` to only match `description:` at brace depth 0
- Add implicit-object fallback: when no top-level `type` exists but child
  fields have their own types, treat as `object` with `properties`
- Regenerate all affected trigger docs (Cal.com payload, Linear data,
  Jira issue.fields, Ashby application, Greenhouse candidate, etc.)

* chore(docs): update static trigger and start page images

* feat(providers): add claude-opus-4-7 model with adaptive thinking support

* Add workflow version screenshots

* Add function block screenshots

---------

Co-authored-by: Theodore Li <theo@sim.ai>
2026-04-16 11:51:49 -07:00

202 lines
8.4 KiB
Plaintext

---
title: Function
---
import { Callout } from 'fumadocs-ui/components/callout'
import { Tab, Tabs } from 'fumadocs-ui/components/tabs'
import { Image } from '@/components/ui/image'
import { FAQ } from '@/components/ui/faq'
The Function block executes custom JavaScript, TypeScript, or Python code in your workflows. Transform data, perform calculations, or implement custom logic.
<div className="flex justify-center">
<Image
src="/static/blocks/function.png"
alt="Function Block with Code Editor"
width={500}
height={400}
className="my-6"
/>
</div>
## Outputs
- **`<function.result>`**: The value returned from your function
- **`<function.stdout>`**: Console.log() output from your code
## Example Use Cases
**Data Processing Pipeline** - Transform API response into structured data
```
API (Fetch) → Function (Process & Validate) → Function (Calculate Metrics) → Response
```
**Business Logic Implementation** - Calculate loyalty scores and tiers
```
Agent (Get History) → Function (Calculate Score) → Function (Determine Tier) → Condition (Route)
```
**Data Validation and Sanitization** - Validate and clean user input
```
Input → Function (Validate & Sanitize) → API (Save to Database)
```
### Example: Loyalty Score Calculator
<Tabs items={['JavaScript', 'Python']}>
<Tab value="JavaScript">
```javascript title="loyalty-calculator.js"
// Process customer data and calculate loyalty score
const { purchaseHistory, accountAge, supportTickets } = <agent>;
// Calculate metrics
const totalSpent = purchaseHistory.reduce((sum, purchase) => sum + purchase.amount, 0);
const purchaseFrequency = purchaseHistory.length / (accountAge / 365);
const ticketRatio = supportTickets.resolved / supportTickets.total;
// Calculate loyalty score (0-100)
const spendScore = Math.min(totalSpent / 1000 * 30, 30);
const frequencyScore = Math.min(purchaseFrequency * 20, 40);
const supportScore = ticketRatio * 30;
const loyaltyScore = Math.round(spendScore + frequencyScore + supportScore);
return {
customer: <agent.name>,
loyaltyScore,
loyaltyTier: loyaltyScore >= 80 ? "Platinum" : loyaltyScore >= 60 ? "Gold" : "Silver",
metrics: { spendScore, frequencyScore, supportScore }
};
```
</Tab>
<Tab value="Python">
```python title="loyalty-calculator.py"
import json
# Reference outputs from other blocks using angle bracket syntax
data = json.loads('<agent>')
purchase_history = data["purchaseHistory"]
account_age = data["accountAge"]
support_tickets = data["supportTickets"]
# Calculate metrics
total_spent = sum(p["amount"] for p in purchase_history)
purchase_frequency = len(purchase_history) / (account_age / 365)
ticket_ratio = support_tickets["resolved"] / support_tickets["total"]
# Calculate loyalty score (0-100)
spend_score = min(total_spent / 1000 * 30, 30)
frequency_score = min(purchase_frequency * 20, 40)
support_score = ticket_ratio * 30
loyalty_score = round(spend_score + frequency_score + support_score)
tier = "Platinum" if loyalty_score >= 80 else "Gold" if loyalty_score >= 60 else "Silver"
result = {
"customer": data["name"],
"loyaltyScore": loyalty_score,
"loyaltyTier": tier,
"metrics": {
"spendScore": spend_score,
"frequencyScore": frequency_score,
"supportScore": support_score
}
}
print(json.dumps(result))
```
</Tab>
</Tabs>
## Python Support
The Function block supports Python as an alternative to JavaScript. Python code runs in a secure [E2B](https://e2b.dev) cloud sandbox.
<div className="flex justify-center">
<Image
src="/static/blocks/function-python.png"
alt="Function block with Python selected"
width={400}
height={500}
className="my-6"
/>
</div>
### Enabling Python
Select **Python** from the language dropdown in the Function block. Python execution requires E2B to be enabled on your Sim instance.
<Callout type="warn">
If you don't see Python as an option in the language dropdown, E2B is not enabled. This only applies to self-hosted instances — E2B is enabled by default on sim.ai.
</Callout>
<Callout type="info">
Python code always runs in the E2B sandbox, even for simple scripts without imports. This ensures a secure, isolated execution environment.
</Callout>
### Returning Results
In Python, print your result as JSON to stdout. The Function block captures stdout and makes it available via `<function.result>`:
```python title="example.py"
import json
data = {"status": "processed", "count": 42}
print(json.dumps(data))
```
### Available Libraries
The E2B sandbox includes the Python standard library (`json`, `re`, `datetime`, `math`, `os`, `collections`, etc.) and common packages like `matplotlib` for visualization. Charts generated with matplotlib are captured as images automatically.
<Callout type="info">
The exact set of pre-installed packages depends on the E2B sandbox configuration. If a package you need isn't available, consider calling an external API from your code instead.
</Callout>
### Matplotlib Charts
When your Python code generates matplotlib figures, they are automatically captured and returned as base64-encoded PNG images in the output:
```python title="chart.py"
import matplotlib.pyplot as plt
import json
data = json.loads('<api.data>')
plt.figure(figsize=(10, 6))
plt.bar(data["labels"], data["values"])
plt.title("Monthly Revenue")
plt.xlabel("Month")
plt.ylabel("Revenue ($)")
plt.savefig("chart.png")
plt.show()
```
{/* TODO: Screenshot of Python code execution output in the logs panel */}
### JavaScript vs. Python
| | JavaScript | Python |
|--|-----------|--------|
| **Execution** | Local VM (fast) or E2B sandbox (with imports) | Always E2B sandbox |
| **Returning results** | `return { ... }` | `print(json.dumps({ ... }))` |
| **HTTP requests** | `fetch()` built-in | `requests` or `httpx` |
| **Best for** | Quick transforms, JSON manipulation | Data science, charting, complex math |
## Best Practices
- **Keep functions focused**: Write functions that do one thing well to improve maintainability and debugging
- **Handle errors gracefully**: Use try/catch blocks to handle potential errors and provide meaningful error messages
- **Test edge cases**: Ensure your code handles unusual inputs, null values, and boundary conditions correctly
- **Optimize for performance**: Be mindful of computational complexity and memory usage for large datasets
- **Use console.log() for debugging**: Leverage stdout output to debug and monitor function execution
<FAQ items={[
{ question: "What languages does the Function block support?", answer: "The Function block supports JavaScript and Python. JavaScript is the default. Python support requires the E2B feature to be enabled, as Python code always runs in a secure E2B sandbox environment." },
{ question: "When does code run locally vs. in a sandbox?", answer: "JavaScript code without external imports runs in a local isolated VM for fast execution. JavaScript code that uses import or require statements requires E2B and runs in a secure sandbox. Python code always runs in the E2B sandbox regardless of whether it has imports." },
{ question: "How do I reference outputs from other blocks inside my code?", answer: "Use the angle bracket syntax directly in your code, like <agent.content> or <api.data>. Do not wrap these references in quotes — the system replaces them with actual values before execution. For environment variables, use double curly braces: {{API_KEY}}." },
{ question: "What does the Function block return?", answer: "The Function block has two outputs: result (the return value of your code, accessed via <function.result>) and stdout (anything logged with console.log(), accessed via <function.stdout>). Make sure your code includes a return statement if you need to pass data to downstream blocks." },
{ question: "Can I make HTTP requests from a Function block?", answer: "Yes. The fetch() API is available in the JavaScript execution environment. You can use async/await with fetch to call external APIs. However, you cannot use libraries like axios or request — only the built-in fetch is supported. Your code runs inside an async context automatically, so you can use await directly." },
{ question: "Is there a timeout for Function block execution?", answer: "Yes. Function blocks have a configurable execution timeout. If your code exceeds the timeout, the execution is terminated and the block reports an error. Keep this in mind when making external API calls or processing large datasets." },
]} />