Shakespeare's Monologues

Search Shakespeare monologues by free text (matches character, play, or first line) plus optional filters. Returns matches, each with a permalink `url`.

{
  "type": "object",
  "properties": {
    "query": {
      "type": [
        "string",
        "null"
      ],
      "description": "Free text — matched against character name, play title, and first line."
    },
    "gender": {
      "type": [
        "string",
        "null"
      ],
      "description": "Role gender: Men, Women, or Both. Natural words like 'male'/'female' are accepted. 'Men'/'Women' also include gender-neutral ('Both') roles."
    },
    "play": {
      "type": [
        "string",
        "null"
      ],
      "description": "Exact or partial play title."
    },
    "style": {
      "type": [
        "string",
        "null"
      ],
      "description": "Verse or Prose."
    },
    "act": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "Filter to a specific act number."
    },
    "limit": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "Max results to return (default 25, max 100)."
    },
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Fetch one monologue's catalogue entry by its numeric id. Follow the returned `url` for the full text.

{
  "type": "object",
  "properties": {
    "id": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "The monologue's numeric id (from search_monologues)."
    },
    "monologue_id": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "Deprecated alias for `id`."
    },
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Return one random monologue, with optional gender/play filters. Useful for a suggestion when the user is undecided.

{
  "type": "object",
  "properties": {
    "gender": {
      "type": [
        "string",
        "null"
      ],
      "description": "Role gender: Men, Women, or Both. Natural words like 'male'/'female' are accepted. 'Men'/'Women' also include gender-neutral ('Both') roles."
    },
    "play": {
      "type": [
        "string",
        "null"
      ],
      "description": "Exact or partial play title."
    },
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

List Shakespeare's plays with their classification (Comedy/History/Tragedy) and monologue counts.

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

List every monologue spoken by a given character (e.g. 'Hamlet', 'Rosalind'). Matches the character name exactly first, then as a substring.

{
  "type": "object",
  "properties": {
    "character": {
      "type": [
        "string",
        "null"
      ],
      "description": "Character name."
    },
    "limit": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "Max results (default 50, max 200)."
    },
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

The current 'Monologue of the Day' — the piece most recently posted to the site's social feeds.

{
  "type": "object",
  "properties": {
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Fetch a monologue's full text alongside its modern-English, line-by-line paraphrase. The paraphrase is AI-generated (Claude) and may be null if it hasn't been generated yet — the `url` always has the monologue itself.

{
  "type": "object",
  "properties": {
    "id": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "The monologue's numeric id (from search_monologues)."
    },
    "monologue_id": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "Deprecated alias for `id`."
    },
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Fetch an AI-generated summary of the scene a monologue appears in (context for the speech). May be null if not generated yet.

{
  "type": "object",
  "properties": {
    "id": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "The monologue's numeric id (from search_monologues)."
    },
    "monologue_id": {
      "type": [
        "number",
        "string",
        "null"
      ],
      "description": "Deprecated alias for `id`."
    },
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Fetch an AI-generated summary of a play (e.g. 'Hamlet', 'The Tempest'). May be null if not generated yet.

{
  "type": "object",
  "properties": {
    "play": {
      "type": [
        "string",
        "null"
      ],
      "description": "Play title (exact or partial)."
    },
    "context": {
      "type": "string",
      "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "context",
    "llm_model"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

Send feedback about this Shakespeare monologues server: a monologue, play or capability you needed that its tools could not provide, a problem with a result, or praise. Only for feedback about these tools, not about other tasks. Do not include personal information about the user.

{
  "type": "object",
  "properties": {
    "feedback_type": {
      "type": "string",
      "enum": [
        "missing_capability",
        "issue",
        "praise",
        "other"
      ],
      "description": "What kind of feedback this is. Use 'missing_capability' when the tool you needed does not exist in the tool list - nothing failed, the capability is absent (this is the most valuable report; send it even if you found a workaround). Use 'issue' when an existing tool behaved badly: it failed, returned a confusing error, or its description or schema misled you. Use 'praise' when something worked notably well. Use 'other' for anything else."
    },
    "summary": {
      "type": "string",
      "description": "One self-contained sentence. For 'missing_capability': the capability you needed, e.g. 'No tool to delete multiple cohorts in one call.' For 'issue': the tool and the problem, e.g. 'query-trends rejects relative date ranges with an unclear error.'"
    },
    "details": {
      "type": "string",
      "description": "Optional longer context: what you tried, exact parameter values, the error text you saw, and any workaround you used. Omit when the summary says it all."
    },
    "friction_points": {
      "type": "string",
      "description": "Optional: the specific moments that slowed you down, as short bullet-like sentences, quoting exact tool names, parameters, or error text."
    },
    "suggested_improvement": {
      "type": "string",
      "description": "Optional: the concrete change that would have helped, e.g. the tool to add, the description to reword, or the error message to improve."
    },
    "tool_name": {
      "type": "string",
      "description": "Optional: the existing tool this feedback is about (for 'issue' or 'praise'). Leave empty for 'missing_capability' - the point is that no tool fits."
    },
    "sentiment": {
      "type": "string",
      "enum": [
        "positive",
        "neutral",
        "negative",
        "mixed"
      ],
      "description": "Optional: how the experience felt overall."
    },
    "task_completed": {
      "type": "boolean",
      "description": "Optional: whether you still completed the user's task despite the problem."
    },
    "llm_model": {
      "type": "string",
      "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess."
    },
    "conversation_id": {
      "type": "string",
      "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request."
    }
  },
  "required": [
    "feedback_type",
    "summary",
    "llm_model"
  ]
}