# OpenMark (remote · openmark.ai)

Benchmark your own prompt on 100+ LLMs with real API calls; pick the cheapest model that passes.

- Trust score: 60/100 (medium)
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-10-23

## Components

- remote · `openmark.ai`: 60/100 (this document), [markdown](https://verifymcp.io/servers/ai-openmark-openmark/openmark.md), [page](https://verifymcp.io/servers/ai-openmark-openmark/openmark)

## Channel facts

- Endpoint: `https://openmark.ai/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.0.0`

## 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 2026-10-23.

- **Endpoint Security**: 51/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - Authorisation not fully verified: no authorisation is required to call this server, and 9 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe.
  - HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement.
  - 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**: 64/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 1773 tokens (~197/item across 9 items; 9 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 13/100
  - Stability observed for 4 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 97/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 91% 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 9 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 10 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 OpenMark MCP server?

OpenMark is a hosted endpoint at https://openmark.ai/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 ai-openmark-openmark 'https://openmark.ai/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "ai-openmark-openmark": {
      "url": "https://openmark.ai/mcp"
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "ai-openmark-openmark": {
      "type": "http",
      "url": "https://openmark.ai/mcp"
    }
  }
}
```

### Codex

```toml
[mcp_servers.ai-openmark-openmark]
url = "https://openmark.ai/mcp"
```

### opencode

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

### OpenClaw

```bash
openclaw mcp add ai-openmark-openmark --url 'https://openmark.ai/mcp' --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  ai-openmark-openmark:
    url: "https://openmark.ai/mcp"
```

### Netclaw

```json
{
  "McpServers": {
    "ai-openmark-openmark": {
      "Transport": "http",
      "Url": "https://openmark.ai/mcp"
    }
  }
}
```

### Vellum

```bash
assistant mcp add ai-openmark-openmark -t streamable-http -u 'https://openmark.ai/mcp'
```

### Other

```json
{
  "mcpServers": {
    "ai-openmark-openmark": {
      "type": "http",
      "url": "https://openmark.ai/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.

### 2026-10-23 (score 60, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-21 (score 59, +1)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “get_task_format” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: 1610 → 1773
- [functional] New tool “validate_task”
- [cosmetic] “get_task_format” added an optional parameter “topic”

### 2026-10-20 (score 58, −2)

- [security] The server rewrote its instructions, which are the text every model session reads
- [security] Tool “get_results” rewrote its description, which is the text the model reads
- [security] Tool “list_models” rewrote its description, which is the text the model reads
- [security] Tool “prepare_attachments” rewrote its description, which is the text the model reads
- [security] Tool “run_benchmark” rewrote its description, which is the text the model reads
- [functional regression] Schema quality: pass → fail
- [functional improvement] Tool coverage: 85% → 91%
- [functional improvement] Stability: unverified → 0.03
- [functional] New tool “estimate_cost”
- [cosmetic] “get_results” added an optional parameter “show_misses”
- [cosmetic] “run_benchmark” added an optional parameter “custom_timeout”
- [cosmetic] “run_benchmark” added an optional parameter “fail_fast”
- [cosmetic] “run_benchmark” added an optional parameter “max_credits”
- [cosmetic] “run_benchmark” added an optional parameter “max_tokens”
- [cosmetic] “run_benchmark” added an optional parameter “preset”
- [cosmetic] “run_benchmark” added an optional parameter “temp_preference”
- [cosmetic] “run_benchmark” added an optional parameter “temperature_discovery”
- [cosmetic] “run_benchmark” added an optional parameter “timeout_profile”

### 2026-10-19 (score 60)

First indexed and scored.

## MCP tools (9)

### `get_task_format` (~112 tokens)

Task YAML format

How to write an OpenMark task: the YAML structure, all 18 scoring modes, and tips. Call this before writing a task. Pass a topic for the full detail on one subject (the app's Task Guide): fields, scoring, composite, chaining, documents, examples, best_practices, or a scoring mode name.

Input parameters:

- `topic` (string): Optional: fields, scoring, composite, chaining, documents, examples, best_practices, or a scoring mode name such as regex_match

### `validate_task` (~73 tokens)

Check a task

Check a task YAML the way OpenMark will before running it: structure, scoring settings, and the test limit of the user's plan. Free, saves nothing. Returns the problems to fix and warnings worth a look.

Input parameters:

- `task_yaml` (string, required): The full task YAML (a JSON object with the same fields also works)

### `list_models` (~121 tokens)

List models

Current OpenMark model ids with list prices ($ per 1M tokens), as text and as a structured list. Use these ids in run_benchmark. Append @low, @medium or @high to a reasoning model id to set its reasoning effort.

Input parameters:

- `images_only` (boolean): Only models that accept image attachments
- `max_output_price` (number): Only models at or below this $ per 1M output tokens
- `search` (string): Filter by id, name or provider, e.g. 'claude', 'gemini flash'

### `prepare_attachments` (~92 tokens)

Prepare file attachments

Get an upload_uid and a one-hour upload link (no API key needed) for images, PDFs or text documents, with the exact curl command. Upload the files, reference them by file name in each test's attachments list, then pass the upload_uid to estimate_cost / run_benchmark.

Input parameters:

- `file_names` (array): Optional: the local file paths to upload, to get a ready-to-run command

### `estimate_cost` (~384 tokens)

Estimate a run's cost

Credits a run_benchmark call would use, before spending anything: the expected total, the split per model, the balance needed to start, the worst case, and the user's balance. Same inputs and run settings as run_benchmark. Use it to drop an expensive model, or to compare settings (e.g. max_tokens), before running.

Input parameters:

- `custom_timeout` (integer): Seconds per answer, with timeout_profile custom
- `fail_fast` (boolean): Stop a model after repeated errors to save credits. Set false to let every model finish, e.g. to diagnose failures
- `max_tokens` (integer): Output token limit per answer, 16 to 16000; 0 = no limit. Keep the default unless results show it was too low: a higher limit raises the credits needed to start. get_results flags models whose answer…
- `models` (array, required)
- `preset` (string): Like the app's presets: standard = 2 passes + snappy timeouts (the default), thorough = 3 passes + patient timeouts. Explicit stability_runs / timeout_profile override it
- `stability_runs` (integer): Passes per model; 2+ shows which models are consistent
- `task_yaml` (string, required): The full task YAML (a JSON object with the same fields also works)
- `temp_preference` (string): With temperature_discovery: which temperature wins a tie
- `temperature_discovery` (boolean): Find each model's best temperature for this task. Costs about 6x as much
- `timeout_profile` (string): How long to wait per answer: snappy 45s, patient 90s, custom = custom_timeout
- `upload_uid` (string): From prepare_attachments, so image and document tokens are counted

### `run_benchmark` (~441 tokens)

Run a benchmark

Save a task (YAML, see get_task_format) to the user's OpenMark account and run it on the given models with real API calls. Returns a job_id and the expected cost; poll get_results. Spends the user's credits; pass max_credits to only start within a budget. Optional run settings match the app's Configure & Run panel (preset, stability_runs, max_tokens, temperature_discovery, temp_preference, timeout_profile, custom_timeout, fail_fast).

Input parameters:

- `custom_timeout` (integer): Seconds per answer, with timeout_profile custom
- `fail_fast` (boolean): Stop a model after repeated errors to save credits. Set false to let every model finish, e.g. to diagnose failures
- `max_credits` (integer): Budget: if the expected cost is higher, nothing runs and you get the quote instead
- `max_tokens` (integer): Output token limit per answer, 16 to 16000; 0 = no limit. Keep the default unless results show it was too low: a higher limit raises the credits needed to start. get_results flags models whose answer…
- `models` (array, required): Model ids from list_models
- `preset` (string): Like the app's presets: standard = 2 passes + snappy timeouts (the default), thorough = 3 passes + patient timeouts. Explicit stability_runs / timeout_profile override it
- `stability_runs` (integer): Passes per model; 2+ shows which models are consistent
- `task_yaml` (string, required): The full task YAML (a JSON object with the same fields also works)
- `temp_preference` (string): With temperature_discovery: which temperature wins a tie
- `temperature_discovery` (boolean): Find each model's best temperature for this task. Costs about 6x as much
- `timeout_profile` (string): How long to wait per answer: snappy 45s, patient 90s, custom = custom_timeout
- `upload_uid` (string): From prepare_attachments, when tests use attachments

### `get_results` (~157 tokens)

Get benchmark results

Status of a benchmark job, and once completed the ranked results (score, stability, cost per run, latency), the tests each model missed with its answer, a recommendation for the chosen objective and quick picks for the others.

Input parameters:

- `job_id` (string, required)
- `optimize_for` (string): accuracy (same order as the app's results table): best score, then cheapest, then fastest. cost / speed / stability: cheapest / fastest / most consistent model within `tolerance` points of the top sc…
- `show_misses` (boolean): List the tests each model got wrong, with the expected answer and the model's reply
- `tolerance` (number): Score points below the top score that still count as good enough

### `get_account` (~46 tokens)

Account, credits and plans

The user's plan, credit balance and limits, and what a credit pack, Pro or Expert would unlock. Use it before large runs, or when a run is refused for credits or limits.

### `watch_task` (~95 tokens)

Watch a task for better models

Watch the task of a completed job: when new or cheaper models ship, OpenMark re-tests up to 4 of them next to the user's pick and emails the user only when one beats it. Each check uses the user's credits at the normal rate.

Input parameters:

- `job_id` (string, required)
- `model` (string): The model to keep as the pick (default: the best-scoring model). Use the model you recommended.

## Diagnostics

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

## Score history

- 2026-10-23: 60
- 2026-10-22: 59
- 2026-10-21: 59
- 2026-10-20: 58
- 2026-10-19: 60

## Common questions

### What is the OpenMark MCP server?

OpenMark is an MCP server listed in the public MCP registry as ai.openmark/openmark. Benchmark your own prompt on 100+ LLMs with real API calls; pick the cheapest model that passes. This page covers its hosted endpoint (https://openmark.ai/mcp).

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

OpenMark scores 60 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 OpenMark MCP server expose?

OpenMark exposes 9 tools: get_task_format, validate_task, list_models, prepare_attachments, estimate_cost, and 4 more. Their descriptions and schemas cost roughly 1,521 tokens of context every time the server is loaded.

### Does the OpenMark MCP server require authentication?

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

### Is the OpenMark MCP server still maintained?

OpenMark is still listed as active in the MCP registry. We last reached this channel on 23 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://openmark.ai/mcp
- Authorisation metadata: https://openmark.ai/.well-known/oauth-protected-resource/mcp
- Website: https://openmark.ai/mcp
- Changelog RSS feed: https://verifymcp.io/servers/ai-openmark-openmark/openmark.xml
- Changelog JSON feed: https://verifymcp.io/servers/ai-openmark-openmark/openmark.json
- HTML version of this page: https://verifymcp.io/servers/ai-openmark-openmark/openmark