# AIDataParser (remote · aidataparser.com)

PDFs, images and messy text to schema-guaranteed JSON. try_parse runs a real extraction, no key.

- Trust score: 77/100 (medium)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-10-04

## Components

- remote · `aidataparser.com`: 77/100 (this document), [markdown](https://verifymcp.io/servers/shibley-aidataparser/v1-mcp.md), [page](https://verifymcp.io/servers/shibley-aidataparser/v1-mcp)

## Channel facts

- Endpoint: `https://aidataparser.com/v1/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `0.2.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-10-04.

- **Endpoint Security**: 80/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC check failed: this domain isn't protected by DNSSEC.
- **Transport & Reachability**: 100/100
  - Verified streamable-http transport via a live MCP handshake.
- **Schema Quality & AI Usability**: 72/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 1544 tokens (~193/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 40/100
  - Stability observed for 12 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 8 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 9 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 60/100
  - Spec-recency check failed: implements MCP spec 2025-06-18; the latest is 2026-07-28.

## Install

### How do I install the AIDataParser MCP server?

AIDataParser is a hosted endpoint at https://aidataparser.com/v1/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add --transport http shibley-aidataparser 'https://aidataparser.com/v1/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "shibley-aidataparser": {
      "url": "https://aidataparser.com/v1/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "shibley-aidataparser": {
      "type": "http",
      "url": "https://aidataparser.com/v1/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.shibley-aidataparser]
url = "https://aidataparser.com/v1/mcp"
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "shibley-aidataparser": {
      "type": "remote",
      "url": "https://aidataparser.com/v1/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add shibley-aidataparser --url 'https://aidataparser.com/v1/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  shibley-aidataparser:
    url: "https://aidataparser.com/v1/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "shibley-aidataparser": {
      "Transport": "http",
      "Url": "https://aidataparser.com/v1/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add shibley-aidataparser -t streamable-http -u 'https://aidataparser.com/v1/mcp'
```

### Other

```json
{
  "mcpServers": {
    "shibley-aidataparser": {
      "type": "http",
      "url": "https://aidataparser.com/v1/mcp"
    }
  }
}
```

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-10-03 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-01 (score 76, +1)

No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-30 (score 75, 0)

- [security] The server rewrote its instructions, which are the text every model session reads
- [functional regression] Schema quality: 1239 → 1544
- [functional] New tool “try_parse”

### 2026-09-28 (score 75, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-09-27 (score 74, +7)

- [security improvement] Authorization: unverified → partial

### 2026-09-25 (score 67, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-09-23 (score 66, +1)

- [functional improvement] Stability: unverified → 0.03

### 2026-09-22 (score 65)

First indexed and scored.

## MCP tools (8)

### `try_parse` (~254 tokens)

Try it now — parse text into JSON with no API key

Run a REAL extraction with no API key, no email and no signup, so you can see the output shape before committing to anything. Pass up to 4000 characters of messy text in `text` (an invoice, a receipt, a resume, scraped HTML, an email body) and optionally a `schema` or `schema_id` to constrain the result. Returns the same structured data, confidence and review_needed flag the paid tools return. Limited to 3 calls per caller per day — for real volume call create_api_key for 50 free credits, then use parse_text or parse_document.

Input parameters:

- `instructions` (string): Optional natural-language guidance for what to extract.
- `schema` (object): Optional JSON Schema describing the exact output shape you want. When provided, the returned `data` conforms to it.
- `schema_id` (string): Optional named template to use instead of a hand-written schema, e.g. "invoice", "receipt", "resume". Call list_schemas for the full set. Ignored when `schema` is provided.
- `text` (string, required): The raw text to extract structured data from, up to 4000 characters. Send a representative excerpt rather than a whole corpus.

### `create_api_key` (~159 tokens)

Get an API key with free credits

Start here if you do not have an AIDataParser API key. Provide the user's `email` and receive a live adp_live_ API key with 50 free credits — no card, no signup form. Configure it on this MCP server as `Authorization: Bearer <api_key>` to unlock parse_document, parse_text and infer_schema. The key is returned ONCE and is never shown again, so surface it to the user and store it. Ask the user for their address; do not invent one. Free — does not consume a credit.

Input parameters:

- `email` (string, required): The user's email address. The account and its free credits belong to this address; an address that already has an account is refused rather than issued a second key.

### `parse_document` (~281 tokens)

Parse a document into structured JSON

Extract clean, schema-guaranteed JSON from a PDF or image. Provide the document via `url` or `base64`. Pass an optional JSON `schema` to constrain the output shape, and `instructions` to guide extraction. Returns the extracted data plus a confidence score and a review_needed flag. Costs 1 credit per successful call.

Input parameters:

- `base64` (string): Base64-encoded document bytes (alternative to `url`). Provide `media_type` alongside it.
- `instructions` (string): Optional natural-language guidance for what to extract.
- `media_type` (string): MIME type for `base64` input, e.g. application/pdf, image/png, image/jpeg.
- `redact` (boolean): When true, PII (emails, SSNs, card numbers, phones, etc.) is masked in the output before it leaves the server.
- `schema` (object): Optional JSON Schema describing the exact output shape you want. When provided, the returned `data` conforms to it.
- `schema_id` (string): Optional named template to use instead of a hand-written schema, e.g. "invoice", "receipt", "resume". Call the list_schemas tool for the full set. Ignored when `schema` is provided.
- `url` (string): Public http(s) URL of the PDF or image to parse.

### `parse_text` (~253 tokens)

Parse raw text into structured JSON

Extract clean, schema-guaranteed JSON from raw/messy text you already have — scraped web content, email bodies, chat logs, OCR output, or pasted tables. Pass the text in `text`. Use this instead of parse_document when you don't have a file. Optional JSON `schema` (or `schema_id`) constrains the output shape and `instructions` guides extraction. Returns the extracted data plus a confidence score and a review_needed flag. Costs 1 credit per successful call.

Input parameters:

- `instructions` (string): Optional natural-language guidance for what to extract.
- `redact` (boolean): When true, PII (emails, SSNs, card numbers, phones, etc.) is masked in the output before it leaves the server.
- `schema` (object): Optional JSON Schema describing the exact output shape you want. When provided, the returned `data` conforms to it.
- `schema_id` (string): Optional named template to use instead of a hand-written schema, e.g. "invoice", "receipt", "resume". Call the list_schemas tool for the full set. Ignored when `schema` is provided.
- `text` (string, required): The raw text to extract structured data from.

### `infer_schema` (~152 tokens)

Infer a reusable JSON Schema from a sample

Given one sample document's `text`, propose a reusable JSON Schema for that document type. Use this when no built-in schema_id fits: infer a schema once, review it, then reuse it as `schema` on parse_document / parse_text across many documents for consistent output. Returns the JSON Schema plus a flat field list and an inferred doc_type. Costs 1 credit per successful call.

Input parameters:

- `doc_type` (string): Optional hint for what kind of document this is, e.g. "purchase order", "lab report".
- `instructions` (string): Optional guidance on which fields matter or how to shape the schema.
- `text` (string, required): A single representative sample of the document type, as text.

### `validate` (~151 tokens)

Validate JSON against a schema

Check whether a JSON object conforms to a JSON `schema` (or a built-in `schema_id` template) and get back a valid flag plus per-field errors. Use this to verify data you already hold — a prior parse result, your own output, or an upstream feed — before acting on it or spending a credit. Deterministic, free, and needs no API key.

Input parameters:

- `data` (required): The JSON value to validate.
- `schema` (object): JSON Schema to validate against. Takes precedence over schema_id.
- `schema_id` (string): Built-in template id to validate against instead of a hand-written schema (invoice, receipt, resume, etc.). Call list_schemas for the full set.

### `check_credits` (~30 tokens)

Check remaining credits

Return the number of extraction credits remaining on the authenticated API key. Free — does not consume a credit.

### `list_schemas` (~58 tokens)

List built-in schema templates

Return the built-in schema templates you can pass to parse_document as `schema_id` (invoice, receipt, resume, etc.), each with its id and the fields it extracts. Free — does not consume a credit and needs no API key.

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/shibley-aidataparser/v1-mcp#diagnostics

## Score history

- 2026-10-04: 77
- 2026-10-03: 77
- 2026-10-02: 76
- 2026-10-01: 76
- 2026-09-30: 75
- 2026-09-29: 75
- 2026-09-28: 75
- 2026-09-27: 74
- 2026-09-26: 67
- 2026-09-25: 67
- 2026-09-24: 66
- 2026-09-23: 66
- 2026-09-22: 65

## Common questions

### What is the AIDataParser MCP server?

AIDataParser is an MCP server listed in the public MCP registry as io.github.shibley/aidataparser. PDFs, images and messy text to schema-guaranteed JSON. try_parse runs a real extraction, no key. This page covers its hosted endpoint (https://aidataparser.com/v1/mcp).

### Is the AIDataParser MCP server safe to use?

AIDataParser scores 77 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the AIDataParser MCP server expose?

AIDataParser exposes 8 tools: try_parse, create_api_key, parse_document, parse_text, infer_schema, and 3 more. Their descriptions and schemas cost roughly 1,338 tokens of context every time the server is loaded.

### Does the AIDataParser MCP server require authentication?

No. We connected to AIDataParser without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the AIDataParser MCP server still maintained?

AIDataParser is still listed as active in the MCP registry. We last reached this channel on 4 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Remote endpoint: https://aidataparser.com/v1/mcp
- Repository: https://github.com/shibley/aidataparser-mcp-server
- Website: https://aidataparser.com/
- Changelog RSS feed: https://verifymcp.io/servers/shibley-aidataparser/v1-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/shibley-aidataparser/v1-mcp.json
- HTML version of this page: https://verifymcp.io/servers/shibley-aidataparser/v1-mcp