ai.shurco/llm-pricing

endpoint

https://shurco.ai/mcp

protocol

streamable-http ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing · is it yours? claim it

karma

0 · newcomer

Is ai.shurco/llm-pricing live?

Yes — it answered the hub's last check (checked 1h ago). It answered 100% of checks over the last 30 days.

Is ai.shurco/llm-pricing free to use?

Yes — the hub reached it with no key and no payment.

What tools does ai.shurco/llm-pricing have?

7 tools: llm_cost_estimate, llm_price_lookup, score_automation_candidate, search_shurco, automation_roi, about_alexey_shurov, llm_cheapest_models.

Is ai.shurco/llm-pricing safe to connect?

The hub found no text in its card or tool descriptions aimed at the agent reading them. It measures what the server answers, not its code — grant it only the access its tools need.

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

• about_alexey_shurov open 1h ago

  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.

  mcp-tool

  1234

  {
    "type": "object",
    "additionalProperties": false
  }

  arguments 4 lines copy
• llm_cheapest_models open 1h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "limit": {
        "type": "integer",
        "maximum": 25,
        "minimum": 1,
        "description": "How many models to return, 1 to 25. Default 10."
      },
      "sort_by": {
        "enum": [
          "blended",
          "input",
          "output"
        ],
        "type": "string",
        "description": "What to rank by. Default blended."
      },
      "modality": {
        "enum": [
          "text",
          "image",
          "file",
          "audio",
          "video"
        ],
        "type": "string",
        "description": "An input type the model must accept."
      },
      "provider": {
        "type": "string",
        "description": "Provider name such as OpenAI, Anthropic, Google, DeepSeek or Mistral."
      },
      "min_context_tokens": {
        "type": "integer",
        "minimum": 0,
        "description": "Smallest acceptable context window, in tokens."
      },
      "max_input_usd_per_million_tokens": {
        "type": "number",
        "minimum": 0,
        "description": "Price ceiling for input, US dollars per million tokens."
      }
    }
  }

  arguments 45 lines copy
• llm_cost_estimate unknown never probed

  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.

  mcp-tool

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

  arguments 38 lines copy
• llm_price_lookup unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "query"
    ],
    "properties": {
      "limit": {
        "type": "integer",
        "maximum": 25,
        "minimum": 1,
        "description": "How many models to return, 1 to 25. Default 8."
      },
      "query": {
        "type": "string",
        "description": "Model name, model id or provider to search for."
      }
    }
  }

  arguments 18 lines copy
• score_automation_candidate unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "hours_per_week",
      "input_regularity",
      "rules",
      "past_examples",
      "mistake_cost"
    ],
    "properties": {
      "kind": {
        "enum": [
          "customer_orders",
          "timesheets",
          "quotes",
          "daily_reports",
          "other"
        ],
        "type": "string",
        "description": "The closest kind of work, if one fits. Leave it out when unsure."
      },
      "task": {
        "type": "string",
        "description": "What the task is, in the person's own words, for example entering emailed orders."
      },
      "rules": {
        "enum": [
          "written",
          "agreed_unwritten",
          "judgement"
        ],
        "type": "string",
        "description": "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."
      },
      "mistake_cost": {
        "enum": [
          "easy_to_fix",
          "costly_but_checkable",
          "costly_hard_to_catch"
        ],
        "type": "string",
        "description": "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": {
        "enum": [
          "hundreds",
          "some",
          "none"
        ],
        "type": "string",
        "description": "Ask: do you keep past examples, with how each was handled? hundreds, some or none."
      },
      "hours_per_week": {
        "type": "number",
        "minimum": 0,
        "description": "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": {
        "enum": [
          "same_layout",
          "few_layouts",
          "free_text"
        ],
        "type": "string",
        "description": "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."
      }
    }
  }

  arguments 68 lines copy
• search_shurco unknown never probed

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "query"
    ],
    "properties": {
      "limit": {
        "type": "integer",
        "maximum": 15,
        "minimum": 1,
        "description": "How many results, 1 to 15. Default 6."
      },
      "query": {
        "type": "string",
        "description": "What to look for, for example \"automate sales order entry\"."
      }
    }
  }

  arguments 18 lines copy
• automation_roi unknown 1h ago

  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.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "hours_per_week",
      "hourly_cost",
      "share_percent",
      "build_cost",
      "monthly_running_cost"
    ],
    "properties": {
      "build_cost": {
        "type": "number",
        "minimum": 0,
        "description": "One-off cost to build."
      },
      "hourly_cost": {
        "type": "number",
        "minimum": 0,
        "description": "Cost of one hour of that work, salary plus employer costs."
      },
      "share_percent": {
        "type": "number",
        "maximum": 100,
        "minimum": 0,
        "description": "Share of the work an automation can take on, 0 to 100. Real systems range from about 60 to 90."
      },
      "working_weeks": {
        "type": "number",
        "maximum": 52,
        "minimum": 1,
        "description": "Working weeks a year. Default 46."
      },
      "hours_per_week": {
        "type": "number",
        "minimum": 0,
        "description": "Hours a week the team spends on the task, everyone added together."
      },
      "monthly_running_cost": {
        "type": "number",
        "minimum": 0,
        "description": "Running cost a month, including model usage, hosting, monitoring and upkeep."
      }
    }
  }

  arguments 44 lines copy

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.