# Autism Motor Research Knowledge Graph (MCP) (`cognilabs/autism-research-mcp`) Actor

Query a knowledge graph of 1,216 peer-reviewed autism papers. Five MCP tools: hybrid keyword and semantic search, entity lookup, community overviews, and multi-source synthesis. Deepest on motor control, praxis, gait and cerebellar work. Results are scored.

- **URL**: https://apify.com/cognilabs/autism-research-mcp.md
- **Developed by:** [cognilabs](https://apify.com/cognilabs) (community)
- **Categories:** AI, MCP servers
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 search queries

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Autism Research Knowledge Graph (MCP)

An MCP server over a knowledge graph built from **1,216 peer-reviewed research papers** on
autism and neurodevelopmental research. Connect any MCP client — Claude, or your
own agent — and query the literature directly.

### Read this before you buy

**The corpus is narrower than "autism research" suggests.** It centres heavily on
**motor control, praxis and neuromotor work**: imitation, postural control, gait,
cerebellar and mirror-neuron research. Measured by entity coverage, motor topics
outweigh social-cognitive ones roughly **65 to 1**.

If your work is on motor development, praxis, coordination or cerebellar involvement in autism, the depth here is unusual.

Every search returns a relevance score, and a weak best match is **flagged in a
warning field** rather than dressed up as an answer.

### Tools

| Tool | What it does |
|---|---|
| `search_papers` | Hybrid BM25 + semantic search across the graph. Sub-second. |
| `get_entity` | Look up a named concept — a brain region, syndrome, assessment instrument, theory. Exact-title match preferred. |
| `get_community_overview` | Survey a topic at a chosen granularity, from broad themes to specific clusters. |
| `list_entities` | Browse entities by type. Metadata listing, not a relevance search. |
| `deep_search` | Synthesise an answer across multiple sources. Several seconds. |

Ten entity types: `BRAIN_REGION`, `BODY_STRUCTURE`, `MOTOR_PHENOMENON`,
`SYNDROME`, `SYMPTOM_DIMENSION`, `ASSESSMENT_INSTRUMENT`,
`EXPERIMENTAL_PARADIGM`, `ANALYSIS_TECHNIQUE`, `FINDING`, `THEORY`.

### What it is built on

Source PDFs were processed within a GraphRAG: entity extraction, community
detection, and per-entity summary cards. The result — 258,278 artifacts spanning
entity cards, community summaries and source passages — lives in a vector store
supporting both keyword and semantic retrieval.

`deep_search` retrieves on two legs — graph-level summaries for synthesis and the
underlying source passages for detail — then synthesises across both.

**Source documents are not identified.** The corpus was assembled under access
that permits querying it, not redistributing it, and some of the underlying
documents are copyrighted. Results carry no filenames or identifiers, and
`deep_search` attributes findings generally — "one study found", "several studies
report" — rather than naming authors, journals or years. If you need to cite a
specific source in your own work, this index will point you at the finding, not
at the paper.

### Pricing

| Event | Price |
|---|---|
| `search` — any lookup, browse, or search call | $0.01 |
| `deep-search` — multi-source synthesis with a language model | $0.05 |

`deep_search` is priced higher because it runs a language model over the
retrieved context. The other four tools are sub-second retrieval.

### Connecting

The Actor runs in Standby mode as an always-ready HTTP server. Point an MCP
client at the Standby URL with `/mcp` as the path, using your Apify API token as
a Bearer token.

### Known limits

- **Coverage is uneven.** See the scope note above.
- **No source citations.** See above — results identify findings, not documents.
- **Entity typing is imperfect.** Types were assigned by a language model over
  extracted text and corrected in a later pass, but edge cases remain — some
  cerebellar cell types sit under `BRAIN_REGION` where `BODY_STRUCTURE` would
  arguably be better.
- **This is a research index, not a clinical resource.** Nothing it returns is
  medical advice.

# Actor input Schema

## Actor input object example

```json
{}
```

# Actor output Schema

## `mcpServer` (type: `string`):

The running server. A GET returns the server description and the collection it is serving; the MCP endpoint itself is POST /mcp on the same host. Under Standby, use the stable Standby URL from the Standby tab rather than this per-run container URL.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("cognilabs/autism-research-mcp").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("cognilabs/autism-research-mcp").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call cognilabs/autism-research-mcp --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,cognilabs/autism-research-mcp"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/hlelVZmuDhmwirn7f/builds/XjdLesyv6ebC0pVGR/openapi.json