hunch

endpoint

https://hunchsheet.app/mcp/v1

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 hunch live?

Yes — it answered the hub's last check (checked 43m ago). It answered 97% of checks over the last 30 days.

Is hunch free to use?

Not anonymously — it asks for a key or a login before it will serve. Whether that key costs anything is the operator's plan to say: the hub measured the door, not the pricing.

What tools does hunch have?

8 tools: hunch_quote, hunch_ask, hunch_pick, hunch_score, hunch_get_key, hunch_buy_credits, hunch_multi, hunch_balance.

Is hunch 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.

• hunch_balance reads auth-required 21h ago

  Check how many Hunch credits are left on this key and how many have been used so far. Read-only, costs nothing. Call it before a large batch, or when a judging tool reports texts were skipped for lack of credits. On the keyless /mcp/try connection it reports the free sample rows left today.

  mcp-tool

  12345

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

  arguments 5 lines copy
• hunch_quote reads unknown 21h ago

  Free, no key needed. Give the number of texts (and questions per text) and get the credits the job needs and what it would cost in dollars, with the free 100-row key, Starter ($29 for 5,000 rows) and Top-up ($19 for 25,000 rows) applied. With a key it also says whether the credits on that key already cover the job. Call it before a large batch so you can tell the person the price first.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "rows"
    ],
    "properties": {
      "rows": {
        "type": "integer",
        "maximum": 10000000,
        "minimum": 1,
        "description": "How many texts the job has."
      },
      "questions_per_text": {
        "type": "integer",
        "maximum": 10,
        "minimum": 1,
        "description": "Yes/no questions per text for hunch_multi (one credit each). Leave out for the other tools."
      }
    },
    "additionalProperties": false
  }

  arguments 21 lines copy
• hunch_ask reads unknown never probed

  Judge a batch of short texts against one yes/no question and get back a calibrated probability (0 to 1) per text, not generated prose. Use it to score, tag, filter or triage many leads, support tickets, reviews, survey answers or emails at once, for example "Is this lead a decision maker?" or "Is this email urgent?". Prefer it to judging the texts yourself once there are more than about 25: one call returns a number per text and keeps the texts out of your context. Costs 1 credit per answered text (blank texts and texts repeated elsewhere in the same call are free; the same text asked again in a later call is charged again). Limits: the model reads the text only, no math, counting or dates; English works best; put the full definition of what counts as yes inside the question, since the model sees nothing else.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "texts",
      "question"
    ],
    "properties": {
      "texts": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 500,
        "minItems": 1,
        "description": "Short texts to judge (leads, tickets, reviews, survey answers, emails, ...), one answer per text. Blank entries and texts repeated elsewhere in the same call cost nothing. Chunked internally into calls of 40."
      },
      "question": {
        "type": "string",
        "minLength": 1,
        "description": "A yes/no question, e.g. \"Is this lead a decision maker who can approve a purchase without asking someone else?\". Put the full definition of yes/no in the question text."
      }
    },
    "additionalProperties": false
  }

  arguments 24 lines copy
• hunch_pick reads unknown never probed

  Sort a batch of short texts into one of your own categories and get back the chosen option plus how confident the model is, not generated prose. Use it to route support tickets, classify feedback, or tag leads by type, for example options ["billing: invoices and charges", "refund", "bug", "other"]. Prefer it to judging the texts yourself once there are more than about 25: one call returns a number per text and keeps the texts out of your context. Costs 1 credit per answered text (blanks and duplicates in the same call are free). Limits: 2 to 255 options, each "label" or "label: description" to disambiguate a short label; the model reads the text only, no math, counting or dates, English works best.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "texts",
      "options"
    ],
    "properties": {
      "texts": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 500,
        "minItems": 1,
        "description": "Short texts to judge (leads, tickets, reviews, survey answers, emails, ...), one answer per text. Blank entries and texts repeated elsewhere in the same call cost nothing. Chunked internally into calls of 40."
      },
      "options": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 255,
        "minItems": 2,
        "description": "The options to choose from, 2 to 255 of them. Each is \"label\" or \"label: description\" when the label alone is ambiguous, e.g. \"billing: invoices and charges\"."
      },
      "question": {
        "type": "string",
        "description": "Optional. What is being decided, e.g. \"Which category does this ticket belong to?\". Defaults to \"Which option best describes this text?\"."
      }
    },
    "additionalProperties": false
  }

  arguments 32 lines copy
• hunch_score reads unknown never probed

  Place a batch of short texts on your own ordered scale (2 to 10 levels, low to high) and get back a probability-weighted position, the most likely level, and confidence, not generated prose. Use it for sentiment ("angry|disappointed|neutral|happy|delighted"), fit scoring ("no fit|weak|good|perfect"), or any low-to-high rating. Prefer it to judging the texts yourself once there are more than about 25: one call returns a number per text and keeps the texts out of your context. Costs 1 credit per answered text (blanks and duplicates in the same call are free). Limits: the model reads the text only, no math, counting or dates, English works best, and the question should say what is being scored.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "texts",
      "question",
      "levels"
    ],
    "properties": {
      "texts": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 500,
        "minItems": 1,
        "description": "Short texts to judge (leads, tickets, reviews, survey answers, emails, ...), one answer per text. Blank entries and texts repeated elsewhere in the same call cost nothing. Chunked internally into calls of 40."
      },
      "levels": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 10,
        "minItems": 2,
        "description": "The scale, low to high, 2 to 10 levels, e.g. [\"angry\", \"disappointed\", \"neutral\", \"happy\", \"delighted\"]. Each may be \"label: description\"."
      },
      "question": {
        "type": "string",
        "minLength": 1,
        "description": "What is being scored, e.g. \"How does the reviewer feel about the product overall?\"."
      }
    },
    "additionalProperties": false
  }

  arguments 34 lines copy
• hunch_get_key changes data unknown never probed

  Returns a Hunch key with 100 free rows, in this response, with no email and no card. For the keyless /mcp/try connection: pass the key as api_key to the other tools (or reconnect with it). Limited to 2 keys per address per day. Treat the key like a password.

  mcp-tool

  12345

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

  arguments 5 lines copy
• hunch_buy_credits changes data unknown never probed

  Returns a link the person can open to buy more credits with a card on Stripe. Nothing is charged until they finish paying on that page; you cannot pay for them. Starter is $29 for 5,000 rows (a key's first purchase), Top-up is $19 for 25,000 rows on a key that already paid. Credits never expire and there is no subscription. Call it when a judging tool says the key is out of credits, or when hunch_quote says the job needs more than the key holds. Show the link to the person and let them decide.

  mcp-tool

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  {
    "type": "object",
    "properties": {
      "plan": {
        "enum": [
          "starter",
          "topup"
        ],
        "type": "string",
        "description": "Optional. Defaults to Top-up when the key already paid, Starter otherwise."
      }
    },
    "additionalProperties": false
  }

  arguments 14 lines copy
• hunch_multi reads unknown never probed

  Ask up to 10 yes/no questions about the same batch of texts in one call, one probability per question per text, not generated prose. Use it when several judgments read the same text at once, for example "Can they buy?", "Are they angry?", "Is it urgent?" on the same support ticket, for a fraction of the tokens of separate calls. Prefer it to judging the texts yourself once there are more than about 25: one call returns a number per text and keeps the texts out of your context. Costs 1 credit per answered question per text (blanks and duplicate texts are free). Limits: the model reads the text only, no math, counting or dates, English works best, and each question needs its own definition of yes inside it.

  mcp-tool

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  {
    "type": "object",
    "required": [
      "texts",
      "questions"
    ],
    "properties": {
      "texts": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 500,
        "minItems": 1,
        "description": "Short texts to judge (leads, tickets, reviews, survey answers, emails, ...), one answer per text. Blank entries and texts repeated elsewhere in the same call cost nothing. Chunked internally into calls of 40."
      },
      "questions": {
        "type": "array",
        "items": {
          "type": "string"
        },
        "maxItems": 10,
        "minItems": 1,
        "description": "Up to 10 yes/no questions, each answered once per text, e.g. [\"Can they buy?\", \"Are they angry?\", \"Is it urgent?\"]."
      }
    },
    "additionalProperties": false
  }

  arguments 28 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.