archive

The Lunenburg, Massachusetts town and school budget, every figure traced to its source document.

사용해야 할까요

품질 및 안전성

A
설명 품질
100%
스키마 완전성
73%
이름 품질
90%
오염 위험
100%
권한 일치
100%
프로토콜 준수
100%

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~1,216토큰 (도구 정의)
~594 B일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 0.95%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "archive": {
      "url": "https://lunenburgbudgetproject.org/mcp"
    }
  }
}

원격 엔드포인트

https://lunenburgbudgetproject.org/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (8)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
🟢list_datasets

Every dataset in the archive, with THE YEARS EACH COVERS, its row count and its size. Call this before answering from prose: it is how you find out whether the archive holds data for the year and subject you are being asked about. 49 datasets covering the town and school budgets, the town ledger, staff rosters, out-of-district placements, elections, and fifteen years of annual town reports.

입력 스키마

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢read_first

The grain of every table and the specific ways to get a confident wrong answer out of this data. Read this before computing anything. It states, among others, that a budget and an actual must never be combined in one calculation; that a budget line is NET of grants and fees and is not what a thing costs; and that no budget line is mapped to a ledger account, so budget-to-actual at line level cannot be answered from this data at all.

입력 스키마

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟡worked_examples

107 questions this archive can answer, each with the SQL that answers it. Every one is executed against the database on every build, so none of them is a claim. Start from the nearest one and edit it rather than writing a query from scratch.

입력 스키마

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢search_meetings(word, board)

Which of the 1,422 published town meeting documents contain a word — every board, 2025 onward. Returns the board, the date and a citable URL for each. AN EMPTY RESULT MEANS THE WORD IS NOT IN THE INDEXED DOCUMENTS, which is not the same as nobody having said it: the archive starts in January 2025. It matches words exactly, so plurals are separate terms — search "jersey" and "jerseys" both.

입력 스키마

{
  "type": "object",
  "properties": {
    "word": {
      "type": "string",
      "description": "A single word, lowercase. Not a phrase."
    },
    "board": {
      "description": "Optional board slug, e.g. school-committee",
      "type": "string"
    }
  },
  "required": [
    "word"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟡budget_history(label, stage)

What a school budget line was in each year, AT ONE STAGE. The stage argument is required and singular on purpose: `proposed`, `settled` and `actual` are three different documents about the same year, and a growth rate measured from an actual to a budget is partly growth and partly the step between them. That mistake put a special education escalator 1.5 points too high here and was invisible until somebody asked how the number was derived. Note also that a budget line is NET — what the town must raise after grants, fees and state aid — so a line can rise because a grant ended rather than because anything cost more.

입력 스키마

{
  "type": "object",
  "properties": {
    "label": {
      "type": "string",
      "description": "Part of the line name, e.g. \"paraprofessional\""
    },
    "stage": {
      "type": "string",
      "enum": [
        "proposed",
        "settled",
        "actual"
      ],
      "description": "One stage. Never compare across stages."
    }
  },
  "required": [
    "label",
    "stage"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢staff(fy, category)

How many people the town PRINTED on a school staff roster, by year, school and kind of job. Uses our classification of the printed title, never the title itself, because the town has called the same job Tutor, Aide, Paraprofessional, Para, (para) and Sped Para across fifteen years. THIS IS A COUNT OF NAMES, NOT A STAFFING LEVEL: a roster carries no FTE, so a 0.4 music teacher and a full-timer are one row each, and it names no funding source, which is the question that usually matters. Grade appears only where the page happened to print it.

입력 스키마

{
  "type": "object",
  "properties": {
    "fy": {
      "description": "Fiscal year as four digits, e.g. 2022",
      "type": "string"
    },
    "category": {
      "description": "paraprofessional, teacher, administrator, counselor, nurse, psychologist, social_worker, speech_therapist, therapist, librarian, custodian, cafeteria, secretary, technology, specialist, coach",
      "type": "string"
    }
  },
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢document(name)

Where a document came from: the publisher's URL, our copy, and its sha256 so a reader can check they have the same bytes. Use it to cite anything.

입력 스키마

{
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Part of a filename or path"
    }
  },
  "required": [
    "name"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢query(sql)

Any question the other tools do not cover, as one read-only SQL statement over the archive database. SELECT or WITH only; a LIMIT is imposed if you omit one. Call `read_first` before computing anything and `list_datasets` to find table names. A query estimated to read more than 250,000 rows is refused — narrow it with a WHERE, or ask for one table at a time.

입력 스키마

{
  "type": "object",
  "properties": {
    "sql": {
      "type": "string",
      "description": "One SELECT statement."
    }
  },
  "required": [
    "sql"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

권장 프롬프트

search_research
Search for information about [topic] using archive
예상 도구: search_meetings
find_specific
Find [specific item] using archive
예상 도구: search_meetings
retrieve_data
Get details about [item] from archive
예상 도구: read_first
fetch_info
Fetch [information type] using archive
예상 도구: read_first
list_items
List all [items] available in archive
예상 도구: list_datasets

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