# Synthesize Bio MCP

```json
{
  "name": "Synthesize Bio MCP",
  "slug": "synthesize_bio_mcp",
  "url": "https://composio.dev/toolkits/synthesize_bio_mcp",
  "markdown_url": "https://composio.dev/toolkits/synthesize_bio_mcp.md",
  "logo_url": "https://logos.composio.dev/api/synthesize_bio_mcp",
  "categories": [
    "ai & machine learning"
  ],
  "is_composio_managed": false,
  "updated_at": "2026-09-07T05:35:53.750Z"
}
```

![Synthesize Bio MCP logo](https://logos.composio.dev/api/synthesize_bio_mcp)

## Description

Securely connect your AI agents and chatbots (Claude, ChatGPT, Cursor, etc) with Synthesize Bio MCP or direct API to start analyses, monitor job status, fetch results, and manage experiment configurations through natural language.

## Summary

Synthesize Bio MCP is a toolkit that runs bulk and single-cell gene-expression analyses from natural-language experiment requests.
Use it to translate experiment intents into reproducible pipelines and monitor results without bespoke integration.

## Categories

- ai & machine learning

## Toolkit Details

- Tools: 5

## Images

- Logo: https://logos.composio.dev/api/synthesize_bio_mcp

## Authentication

- **Dcr Oauth**
  - Type: `custom`
  - Description: Dcr Oauth authentication for Synthesize Bio MCP.
  - Setup:
    - Configure Dcr Oauth credentials for Synthesize Bio MCP.
    - Use the credentials when creating an auth config in Composio.

## Suggested Prompts

- Run differential expression on latest samples
- Start single-cell pipeline for patient sample
- Fetch results and generate QC summary report

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `SYNTHESIZE_BIO_MCP_ANALYZE_GENE_EXPRESSION` | Analyze gene expression | Starts a differential gene expression analysis using Synthesize Bio's AI platform. Requires the resolution_id returned by resolve_sample_metadata; raw natural-language prompts are not accepted. Requires `user_confirmed_metadata: true`. When the flag is missing or false, the call is rejected with failure_kind `user_confirmation_required`. Optional workspace_id selects which workspace owns the generated dataset. When omitted, the workspace from resolve_sample_metadata is used automatically. When the account has more than one workspace and the resolution has no workspace, the call is rejected with failure_kind `workspace_selection_required` and a `workspaces` list of `{ name, workspace_id }`. Returns a job_id immediately; get_analysis_results accepts that job_id and returns analysis status or results. The pipeline runs two steps: (1) GEM-1 — Synthesize Bio's Gene Expression Model inference; (2) Differential expression — GPU-accelerated DESeq2 (negative-binomial GLM with Wald test, Cook's outlier filter, and Benjamini-Hochberg padj). All genes are tested; pre-filtering is handled by DESeq2's independent filtering. If the query is unsupported, later polling responses include failure_kind `unsupported_query` and suggested_queries. Quota and monthly-limit errors include a request-higher-limits URL; a previous successful resolve does not grant an extra run when the account is out of budget. |
| `SYNTHESIZE_BIO_MCP_GET_ANALYSIS_RESULTS` | Get analysis results | Polls the status of a gene expression analysis. Each call waits server-side for a short bounded window and may return earlier if progress is detected. Responses always include a `structuredContent` object (declared by the tool's `outputSchema`); MCP clients read from `structuredContent` directly rather than re-parsing JSON out of the human-readable text. `structuredContent` always has `status` (one of `running`, `complete`, `failed`), `job_id`, and `steps_completed`. While running, it also has `step` (`gem_model` or `diff_expr`), `message`, and `progress_label`/`progress_percent`/`progress_bar`. Failed responses include `error`, and may also include `failure_kind`, `user_action_required`, and `suggested_queries`. When `status` is `complete`, `structuredContent` carries: `metadata` (prompt, modality, groups, plus summary counts such as `significant_genes`, `significant_up`, `significant_down`, `total_genes_tested`); `results` — up to 1000 differential expression rows (each with `gene_id`, `gene_symbol`, `log2FoldChange`, `pvalue`, `padj`, `neg_log10_padj` (pre-computed `-log10(padj)`, clamped to 300 if padj underflows), `direction`, `significant`) suitable for downstream analysis or visualization (e.g. a volcano plot with x = `log2FoldChange`, y = `neg_log10_padj`); `plot_results` — the top ~200 most significant rows (same per-row shape, pre-sorted most-significant-first), pre-sliced for charting; the full `results` array is better suited to tables, summaries, and analysis; `results_returned` and `results_total` for truncation accounting; `plot_results_returned` for the plotted subset size; `dataset_link` — `{ dataset_id, title, url }` for the Synthesize Bio platform dataset (or `null`). The accompanying `content[0].text` is a human-readable Markdown summary of the same data. For hosts that do not surface `structuredContent` (e.g. claude.ai), it inlines only the top ~200 most significant rows as an array of objects under a top-level `results` key — same per-row schema as `structuredContent.results`, including the pre-computed `neg_log10_padj` field — so chart-widget code can use those rows directly. The full result set remains available via `structuredContent.results` and the dataset link when present. |
| `SYNTHESIZE_BIO_MCP_GET_COUNTS_DATA_URL` | Get counts data url | Returns a presigned S3 URL to download the raw gene expression counts data (JSON) produced by a completed or in-progress analysis job. The data is typically large (20,000+ genes by N samples) and requires an environment with direct network access. The JSON has the following structure: { gene_order: string[] (Ensembl IDs), outputs: [{ counts: number[], metadata: object }], model_version: string }. Each entry in 'outputs' corresponds to one sample; 'counts' is aligned with 'gene_order'. The response also returns a second presigned URL to a small (~500 KB) Parquet file mapping every Ensembl `gene_id` in `gene_order` to its HGNC gene_name — both files join on gene_id to label genes by symbol. Both URLs expire after 1 hour; fresh responses contain fresh URLs. Available after analyze_gene_expression has completed the GEM-1 step. |
| `SYNTHESIZE_BIO_MCP_GET_METADATA_SCHEMA` | Get metadata schema | Returns the structured-metadata schema used to turn a natural-language experiment description into sample groups. Response fields include `group_schema` (the JSON shape of one sample group), the `fields` and `perturbation_fields` inventory for the active metadata version, and `instructions` for building the `groups` array. The resulting `groups` array is the input to resolve_sample_metadata. This tool makes no AI calls and consumes no usage budget. |
| `SYNTHESIZE_BIO_MCP_RESOLVE_SAMPLE_METADATA` | Resolve sample metadata | Deterministically harmonizes already-structured sample `groups` to Synthesize Bio's controlled ontology vocabulary. Does not accept a natural-language prompt and makes no AI calls. Input: `groups` — a JSON array of sample-group objects matching get_metadata_schema's `group_schema`. Returns a resolution_id and a per-group breakdown including tissue, disease, cell type/line (with resolved ontology ids), sex, age, and full perturbation details (type, label/id, gene mechanism and mechanism type, dose, dose count, timepoint). Each resolved field also carries ranked candidate options for disambiguation. If a value resolves to the wrong id, correcting the term in `groups` and calling again produces a new resolution. The `warnings` array flags issues such as a described drug that did not match the ontology. When status is `resolving`, the same resolution_id identifies the pending request and can be passed again to poll. A completed resolution_id is required by analyze_gene_expression. Optional workspace_id selects which workspace the resolution is stored under. When omitted, a single-workspace account is assigned automatically. When the account has more than one workspace and workspace_id is omitted, the call is rejected with failure_kind `workspace_selection_required` and a `workspaces` list of `{ name, workspace_id }`; no metadata resolution runs until the user picks and the tool is retried with workspace_id. Quota and monthly-limit errors include a request-higher-limits URL and indicate remaining samples/cells when available. |

## Supported Triggers

None listed.

## Installation and MCP Setup

### Path 1: SDK Installation

#### Path 1, Step 1: Install Composio

Install the Composio SDK
```python
pip install composio_openai
```

```typescript
npm install @composio/openai
```

#### Path 1, Step 2: Initialize Composio and Create Tool Router Session

Import and initialize Composio client, then create a Tool Router session
```python
from openai import OpenAI
from composio import Composio
from composio_openai import OpenAIResponsesProvider

composio = Composio(provider=OpenAIResponsesProvider())
openai = OpenAI()
session = composio.create(user_id='your-user-id')
```

```typescript
import OpenAI from 'openai';
import { Composio } from '@composio/core';
import { OpenAIResponsesProvider } from '@composio/openai';

const composio = new Composio({
  provider: new OpenAIResponsesProvider(),
});
const openai = new OpenAI({});
const session = await composio.create('your-user-id');
```

#### Path 1, Step 3: Execute Synthesize Bio MCP Tools via Tool Router with Your Agent

Get tools from Tool Router session and execute Synthesize Bio MCP actions with your Agent
```python
tools = session.tools
response = openai.responses.create(
  model='gpt-4.1',
  tools=tools,
  input=[{
    'role': 'user',
    'content': 'YOUR_SPECIFIC_PROMPT_HERE'
  }]
)
result = composio.provider.handle_tool_calls(
  response=response,
  user_id='your-user-id'
)
print(result)
```

```typescript
const tools = session.tools;
const response = await openai.responses.create({
  model: 'gpt-4.1',
  tools: tools,
  input: [{
    role: 'user',
    content: 'YOUR_SPECIFIC_PROMPT_HERE'
  }],
});
const result = await composio.provider.handleToolCalls(
  'your-user-id',
  response.output
);
console.log(result);
```

### Path 2: MCP Server Setup

#### Path 2, Step 1: Install Composio

Install the Composio SDK for Python or TypeScript
```python
pip install composio claude-agent-sdk
```

```typescript
npm install @composio/core ai @ai-sdk/openai @ai-sdk/mcp
```

#### Path 2, Step 2: Initialize Client and Create Tool Router Session

Import and initialize the Composio client, then create a Tool Router session for Synthesize Bio MCP
```python
from composio import Composio
from claude_agent_sdk import ClaudeSDKClient, ClaudeAgentOptions

composio = Composio(api_key='your-composio-api-key')
session = composio.create(user_id='your-user-id')
url = session.mcp.url
```

```typescript
import { Composio } from '@composio/core';

const composio = new Composio({ apiKey: 'your-api-key' });
const session = await composio.create('your-user-id');
console.log(`Tool Router session created: ${session.mcp.url}`);
```

#### Path 2, Step 3: Connect to AI Agent

Use the MCP server with your AI agent (Anthropic Claude or Mastra)
```python
import asyncio

options = ClaudeAgentOptions(
    permission_mode='bypassPermissions',
    mcp_servers={
        'tool_router': {
            'type': 'http',
            'url': url,
            'headers': {
                'x-api-key': 'your-composio-api-key'
            }
        }
    },
    system_prompt='You are a helpful assistant with access to Synthesize Bio MCP tools.',
    max_turns=10
)

async def main():
    async with ClaudeSDKClient(options=options) as client:
        await client.query('YOUR_SPECIFIC_PROMPT_HERE')
        async for message in client.receive_response():
            if hasattr(message, 'content'):
                for block in message.content:
                    if hasattr(block, 'text'):
                        print(block.text)

asyncio.run(main())
```

```typescript
import { openai } from '@ai-sdk/openai';
import { experimental_createMCPClient as createMCPClient } from '@ai-sdk/mcp';
import { generateText } from 'ai';

const client = await createMCPClient({
  transport: {
    type: 'http',
    url: session.mcp.url,
    headers: {
      'x-api-key': 'your-composio-api-key',
    },
  },
});

const tools = await client.tools();
const { text } = await generateText({
  model: openai('gpt-4o'),
  tools,
  messages: [{
    role: 'user',
    content: 'YOUR_SPECIFIC_PROMPT_HERE'
  }],
  maxSteps: 5,
});

console.log(`Agent: ${text}`);
```

## Why Use Composio?

### 1. AI Native Synthesize Bio MCP Integration

- Supports both Synthesize Bio MCP and direct API based integrations
- Structured, LLM-friendly schemas for reliable tool execution
- Rich coverage for reading, writing, and querying your Synthesize Bio MCP data

### 2. Managed Auth

- Built-in OAuth handling with automatic token refresh and rotation
- Central place to manage, scope, and revoke Synthesize Bio MCP access
- Per user and per environment credentials instead of hard-coded keys

### 3. Agent Optimized Design

- Tools are tuned using real error and success rates to improve reliability over time
- Comprehensive execution logs so you always know what ran, when, and on whose behalf

### 4. Enterprise Grade Security

- Fine-grained RBAC so you control which agents and users can access Synthesize Bio MCP
- Scoped, least privilege access to Synthesize Bio MCP resources
- Full audit trail of agent actions to support review and compliance

## Use Synthesize Bio MCP with any AI Agent Framework

Choose a framework you want to connect Synthesize Bio MCP with:

- [ChatGPT Work](https://composio.dev/toolkits/synthesize_bio_mcp/framework/chatgpt)
- [Claude Cowork](https://composio.dev/toolkits/synthesize_bio_mcp/framework/claude-cowork)
- [Hermes](https://composio.dev/toolkits/synthesize_bio_mcp/framework/hermes-agent)

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## Frequently Asked Questions

### Do I need my own developer credentials to use Synthesize Bio MCP with Composio?

Yes, Synthesize Bio MCP requires you to configure your own Dcr Oauth credentials. Once set up, Composio handles secure credential storage and management for you.

### Can I use multiple toolkits together?

Yes! Composio's Tool Router enables agents to use multiple toolkits. [Learn more](https://docs.composio.dev/tool-router/overview).

### Is Composio secure?

Composio is SOC 2 and ISO 27001 compliant with all data encrypted in transit and at rest. [Learn more](https://trust.composio.dev).

### What if the API changes?

Composio maintains and updates all toolkit integrations automatically, so your agents always work with the latest API versions.

---
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