thinkhard.si prompt tools

Reliability

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How to connect

Add it to any MCP client that supports remote servers.

{
  "mcpServers": {
    "thinkhard-si-prompt-tools": {
      "type": "http",
      "url": "https://thinkhard.si/mcp"
    }
  }
}

14 tools

• search_prompts

  Find ready-made, expert prompt templates for a work task in the thinkhard.si repository (600+ prompts for email, meetings, presentations, job search, marketing, sales, support, HR, finance, legal, spreadsheets, research and more). Use it whenever the user wants to write, plan…

• get_prompt

  Get the full text of one prompt from the thinkhard.si repository by its slug, registry number (such as CAR-001) or exact title, with its {{blanks}}, what goes in each, and why it works. If the name does not match exactly, it answers with the closest matches instead. Pass values…

• list_collections

  List the collections in the thinkhard.si prompt repository, with how many prompts each has.

• grade_prompt

  Score a prompt out of 10 on the ten checks thinkhard.si uses (what to make, who it is for, background facts, length, format, what to avoid, what good looks like, an example, what to do if unsure, layout for the model). Returns which checks pass and a plain question for each one…

• estimate_ai_cost

  Estimate the monthly cost of running a prompt on current AI models (Claude, GPT, Gemini, DeepSeek, Qwen and others), from thinkhard.si's price catalog, which is checked against each provider's pricing page. Give token counts, or paste the prompt text to estimate them.

• fix_bad_answer

  When an AI answer goes wrong (it made up a source or a number, ignored an instruction, came back too long or in the wrong shape, sounded robotic or generic, changed every time, refused, or lost track of a long chat), find the likely cause and the exact line to add to the prompt…

• get_prompt_pattern

  The 24 prompt patterns from thinkhard.si: small moves that change the answer, such as giving the facts, naming the reader, showing one example, setting a length, fencing off pasted text or asking for reasoning first. Each comes with when it helps, when it does not, a…

• define_ai_term

  Plain-English definitions of 102 AI words from the thinkhard.si glossary (token, context window, hallucination, temperature, system prompt, RAG, fine-tuning, agent, MCP and more), written for people who use AI at work rather than build it.

• check_answer

  Did the AI do what the prompt asked? Give the prompt and the answer it got. Finds every rule in the prompt that can be checked by reading (length in words, characters, sentences or lines, how many items, a list, a table and its columns, JSON and its keys, sections, plain text…

• build_assistant

  Write the instructions for an assistant that keeps its rules: a ChatGPT custom GPT, a Claude Project, a Gemini Gem, a Microsoft Copilot agent or an app's system prompt. From what it is for and who will use it, it finds the tasks it will do in the repository and what each needs…

• plan_workflow

  Turn a job such as hiring for a role, launching a feature, closing the month or applying for a job into steps in the order the work happens, each with the expert prompt from the repository that does it, which earlier answer it takes and into which blank, and the details to…

• lint_prompts

  Find the prompts in source files (Python, JavaScript, TypeScript, YAML, JSON and prompt files) and review each for what breaks AI features in production: user input in a system prompt or with nothing around it, no rule that user text is data, code that parses the reply as JSON…

• audit_library

  Read many prompts at once, such as a team's shared prompt doc: each prompt's score on the ten checks, the task it does and how ready it is for it, near-copies with the one to keep, private details that should not be in a shared prompt (emails, phone numbers, card numbers, keys)…

• check_agent_brief

  Before a task goes to a coding agent (Claude Code, Codex, Cursor, GitHub Copilot or another), check the brief: what kind of task it is, which of the parts that kind needs are there (the outcome, where in the code, how to reproduce a bug, the command that proves it works…

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