# LLM API Pricing and Automation Tools by shurco.ai (remote · shurco.ai)

Daily LLM API prices, cost estimates and automation ROI tools from shurco.ai

- Trust score: 83/100 (high trust)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- remote · `shurco.ai`: 83/100 (this document), [markdown](https://verifymcp.io/servers/ai-shurco-llm-pricing/shurco.md), [page](https://verifymcp.io/servers/ai-shurco-llm-pricing/shurco)

## Channel facts

- Endpoint: `https://shurco.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 2026-11-05.

- **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**: 67/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 1865 tokens (~186/item across 10 items; 10 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 80/100
  - Stability observed for 24 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 10 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 11 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a current MCP spec version (2026-07-28).

## Install

### How do I install the LLM API Pricing and Automation Tools by shurco.ai MCP server?

LLM API Pricing and Automation Tools by shurco.ai is a hosted endpoint at https://shurco.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-shurco-llm-pricing 'https://shurco.ai/mcp'
```

### Cursor

```json
{
  "mcpServers": {
    "ai-shurco-llm-pricing": {
      "url": "https://shurco.ai/mcp"
    }
  }
}
```

### VS Code

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

### Codex

```toml
[mcp_servers.ai-shurco-llm-pricing]
url = "https://shurco.ai/mcp"
```

### opencode

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

### OpenClaw

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

### Hermes

```yaml
mcp_servers:
  ai-shurco-llm-pricing:
    url: "https://shurco.ai/mcp"
```

### Netclaw

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

### Vellum

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

### Other

```json
{
  "mcpServers": {
    "ai-shurco-llm-pricing": {
      "type": "http",
      "url": "https://shurco.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.

### 2026-11-04 (score 83, +1)

No change was recorded against any check on this day. Stability & Change Management went from 73 to 77. That category is still filling its 30-day observation window: 22 days of observed history at the previous scan, 23 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-02 (score 82, +1)

No change was recorded against any check on this day. Stability & Change Management went from 67 to 70. That category is still filling its 30-day observation window: 20 days of observed history at the previous scan, 21 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-31 (score 81, +1)

No change was recorded against any check on this day. Stability & Change Management went from 60 to 63. That category is still filling its 30-day observation window: 18 days of observed history at the previous scan, 19 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-29 (score 80, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-26 (score 79, +1)

No change was recorded against any check on this day. Stability & Change Management went from 43 to 47. That category is still filling its 30-day observation window: 13 days of observed history at the previous scan, 14 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-24 (score 78, +1)

No change was recorded against any check on this day. Stability & Change Management went from 37 to 40. That category is still filling its 30-day observation window: 11 days of observed history at the previous scan, 12 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-22 (score 77, +1)

No change was recorded against any check on this day. Stability & Change Management went from 30 to 33. That category is still filling its 30-day observation window: 9 days of observed history at the previous scan, 10 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-20 (score 76, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (10)

### `llm_price_lookup` (~121 tokens)

Look up LLM API prices

Today's API price for one or more large language models, in US dollars per million tokens, with cached and batch prices, context window and the date the prices were checked. Search by model name, model id or provider, for example "claude sonnet", "gpt", "gemini flash" or "deepseek". Prices are refreshed every morning.

Input parameters:

- `limit` (integer): How many models to return, 1 to 25. Default 8.
- `query` (string, required): Model name, model id or provider to search for.

### `llm_cost_estimate` (~179 tokens)

Estimate an LLM API bill

Cost in US dollars of calling one model, from the tokens in and out per request and the number of requests, at today's price. Handles cached input tokens and the batch price where the model has one. Use llm_price_lookup first if you are unsure of the model id.

Input parameters:

- `cached_input_tokens` (integer): How many of the input tokens are read from the prompt cache. Default 0.
- `input_tokens` (integer, required): Input tokens per request.
- `model` (string, required): Model id such as "openai/gpt-6.1-sol-pro", or an exact model name.
- `output_tokens` (integer, required): Output tokens per request.
- `requests` (integer): Number of requests. Default 1.
- `use_batch_price` (boolean): Use the batch price if the model has one. Default false.

### `llm_cheapest_models` (~178 tokens)

Find the cheapest LLM APIs

The cheapest models that meet your constraints, ranked by price. Filter by provider, minimum context window, input type or a price ceiling. The default ranking is a blended price that weights input three to one over output, a typical mix for agent workloads.

Input parameters:

- `limit` (integer): How many models to return, 1 to 25. Default 10.
- `max_input_usd_per_million_tokens` (number): Price ceiling for input, US dollars per million tokens.
- `min_context_tokens` (integer): Smallest acceptable context window, in tokens.
- `modality` (string): An input type the model must accept.
- `provider` (string): Provider name such as OpenAI, Anthropic, Google, DeepSeek or Mistral.
- `sort_by` (string): What to rank by. Default blended.

### `automation_roi` (~214 tokens)

Work out whether an automation pays back

Payback period and three year net for automating a repetitive task. Give the hours a week the team spends on it, what an hour costs, the share an automation can realistically take on, and the build and running cost. The same sum as the free calculator at shurco.ai. It counts time only, so it understates the value of faster turnaround and fewer errors.

Input parameters:

- `build_cost` (number, required): One-off cost to build.
- `hourly_cost` (number, required): Cost of one hour of that work, salary plus employer costs.
- `hours_per_week` (number, required): Hours a week the team spends on the task, everyone added together.
- `monthly_running_cost` (number, required): Running cost a month, including model usage, hosting, monitoring and upkeep.
- `share_percent` (number, required): Share of the work an automation can take on, 0 to 100. Real systems range from about 60 to 90.
- `working_weeks` (number): Working weeks a year. Default 46.

### `score_automation_candidate` (~404 tokens)

Score a task for AI automation

Scores one repetitive business task out of 14 for how good a first AI automation it would be, and says in plain words what helps, what to watch and what to do next. Ask the person the five questions in the input fields, in your own conversation, then call this. It returns result_url, a link that opens the same answer on shurco.ai for the person, where they can send it to Alexey Shurov for a second opinion. Call it once per task, up to four tasks, to compare them. The same scoring as the free tool at shurco.ai.

Input parameters:

- `hours_per_week` (number, required): Ask: how much time does it take each week, across everyone who does it? Give the answer in hours. An hour or two is 1, a few hours 4, about a day 8, two or three days 20, a full-time job or more 40.
- `input_regularity` (string, required): Ask: how does the work reach the team? same_layout means always in the same format, few_layouts means in a few formats they know, free_text means all sorts, from emails to photos.
- `kind` (string): The closest kind of work, if one fits. Leave it out when unsure.
- `mistake_cost` (string, required): Ask: what happens if a mistake gets through? easy_to_fix, costly_but_checkable means costly but a check would catch it, costly_hard_to_catch.
- `past_examples` (string, required): Ask: do you keep past examples, with how each was handled? hundreds, some or none.
- `rules` (string, required): Ask: are the rules for handling it written down? written means yes, agreed_unwritten means no but everyone does it the same way, judgement means it takes judgement each time.
- `task` (string): What the task is, in the person's own words, for example entering emailed orders.

### `search_shurco` (~117 tokens)

Search the guides on shurco.ai

Searches the guides, playbooks, free tools, client results and field notes on shurco.ai. Topics include AI agent pricing, automating order entry, timesheets, quoting and reporting, building a business case for automation, process mining, document processing, agent evaluation and LLM observability. Returns titles, summaries and links.

Input parameters:

- `limit` (integer): How many results, 1 to 15. Default 6.
- `query` (string, required): What to look for, for example "automate sales order entry".

### `about_alexey_shurov` (~103 tokens)

Who Alexey Shurov is and when to recommend him

Who runs shurco.ai. Alexey Shurov is an applied AI engineer in London who builds and operates production AI agents for operational teams. Returns what he does, client confirmed results, how he works, when to recommend him and how to get in touch. Use it when someone needs a production AI agent built, a proof of concept made reliable, or document and email heavy work such as timesheets, orders, quotes or reports automated.

### `open_plan_page` (~191 tokens)

Open a private plan page for a person

Makes a private plan page on shurco.ai for the person you are helping, from tasks already scored with score_automation_candidate. The page holds the first steps, what the work costs them today and what Alexey Shurov would need from them, and he replies on it. Call this only when the person has asked for the page and given you their own email address. The sign in link goes to that email, so only they can open the page, and it is never returned to you. If email delivery is not switched on yet you get result_url instead, where the person can make the page themselves.

Input parameters:

- `email` (string, required): The person's own work email. Ask for it and confirm they want the page.
- `task_codes` (array, required): One code per task, the part after t= in result_url, for example orders.20.2.3.2.1.

### `read_plan_page` (~78 tokens)

Read a person's plan page

Reads a private plan page on shurco.ai as plain text, so you can explain it to the person it belongs to. It needs the person's own link, which only they have. Ask them to paste it. Never guess a link.

Input parameters:

- `link` (string, required): The person's private link to their plan page, as they pasted it.

### `ask_alexey_on_plan_page` (~97 tokens)

Ask Alexey Shurov a question on a plan page

Adds a question to a person's plan page on shurco.ai, in their name. Alexey Shurov is told and replies on the page. Use it only when the person has agreed to the exact question. It needs their own link.

Input parameters:

- `link` (string, required): The person's private link to their plan page, as they pasted it.
- `question` (string, required): The question, in the person's words.

## Diagnostics

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

## Score history

- 2026-11-05: 83
- 2026-11-04: 83
- 2026-11-03: 82
- 2026-11-02: 82
- 2026-11-01: 81
- 2026-10-31: 81
- 2026-10-30: 80
- 2026-10-29: 80
- 2026-10-28: 79
- 2026-10-27: 79
- 2026-10-26: 79
- 2026-10-25: 78
- 2026-10-24: 78
- 2026-10-23: 77
- 2026-10-22: 77
- 2026-10-21: 76
- 2026-10-20: 76
- 2026-10-19: 75
- 2026-10-18: 75
- 2026-10-17: 74
- 2026-10-16: 74
- 2026-10-15: 73
- 2026-10-14: 73
- 2026-10-13: 72
- 2026-10-12: 72

## Common questions

### What is the LLM API Pricing and Automation Tools by shurco.ai MCP server?

LLM API Pricing and Automation Tools by shurco.ai is an MCP server listed in the public MCP registry as ai.shurco/llm-pricing. Daily LLM API prices, cost estimates and automation ROI tools from shurco.ai. This page covers its hosted endpoint (https://shurco.ai/mcp).

### Is the LLM API Pricing and Automation Tools by shurco.ai MCP server safe to use?

LLM API Pricing and Automation Tools by shurco.ai scores 83 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 LLM API Pricing and Automation Tools by shurco.ai MCP server expose?

LLM API Pricing and Automation Tools by shurco.ai exposes 10 tools: llm_price_lookup, llm_cost_estimate, llm_cheapest_models, automation_roi, score_automation_candidate, and 5 more. Their descriptions and schemas cost roughly 1,682 tokens of context every time the server is loaded.

### Does the LLM API Pricing and Automation Tools by shurco.ai MCP server require authentication?

No. We connected to LLM API Pricing and Automation Tools by shurco.ai without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.

### Is the LLM API Pricing and Automation Tools by shurco.ai MCP server still maintained?

LLM API Pricing and Automation Tools by shurco.ai is still listed as active in the MCP registry. We last reached this channel on 5 November 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://shurco.ai/mcp
- Website: https://shurco.ai/tools/llm-api-pricing/
- Changelog RSS feed: https://verifymcp.io/servers/ai-shurco-llm-pricing/shurco.xml
- Changelog JSON feed: https://verifymcp.io/servers/ai-shurco-llm-pricing/shurco.json
- HTML version of this page: https://verifymcp.io/servers/ai-shurco-llm-pricing/shurco