MCP-сервер (Model Context Protocol — открытый протокол подключения AI-ассистентов к внешним инструментам) для работы с ЕГРЮЛ (Единый Государственный Реестр Юридических Лиц РФ) и ЕГРИП (Единый Государственный Реестр Индивидуальных Предпринимателей). Источник — официальные open-data дампы ФНС (Федеральной налоговой службы).
Статус: v0.1.2 — open-версия (self-host через SQLite) полностью готова + клиентская часть hosted Pro (HTTP-клиент HostedClient для api.atomno-mcp.ru). Опубликована на PyPI, индексирована в Glama и Smithery. Сама hosted Pro-инфра — в активной разработке. Coverage 100.00% (345 тестов, ruff clean, fastmcp 3.2.4, enforced через --cov-fail-under=100).
Парный проект: mcp-fns-check (risk-чек-слой поверх ЕГРЮЛ).
Семь MCP-тулзов, видимых AI-ассистенту (Cursor, Claude Desktop, Cline, любой MCP-клиент):
Tool | Описание | Аргументы |
| Поиск по ИНН (10 цифр — юр.лицо, 12 — ИП) |
|
| Поиск по ОГРН (13) или ОГРНИП (15) |
|
| Fuzzy-поиск по названию (FTS5) |
|
| Полная карточка со всеми секциями |
|
| Только учредители с долями |
|
| Только текущий руководитель |
|
| Массовая проверка (до 100 ИНН) |
|
Плюс диагностический ping для проверки что сервер жив.
Полная спецификация payload'ов — в src/mcp_egrul/schemas.py (Pydantic-модели CompanyCard, IECard, SearchResult, BulkResult).
Related MCP server: onec-meta-mcp
# Без локального clone — работает «из коробки»
uvx atomno-mcp-egrul
# Или установка глобально
pipx install atomno-mcp-egrul
atomno-mcp-egrul
# Или классический pip в venv
pip install atomno-mcp-egrul
atomno-mcp-egrulТребуется Python 3.11+ и uv (быстрая замена pip, опционально).
git clone https://github.com/atomno-mcp/mcp-egrul
cd mcp-egrul
uv venv
uv pip install -e ".[dev]"Альтернативно через pip:
python -m venv .venv
.venv/Scripts/activate # Windows
# source .venv/bin/activate # Linux/macOS
pip install -e ".[dev]"atomno-mcp-egrulТранспорт по умолчанию — stdio (стандартный ввод/вывод JSON-RPC). Подходит для подключения к Cursor / Claude Desktop / Claude Code.
claude_desktop_config.json){
"mcpServers": {
"egrul": {
"command": "uvx",
"args": ["atomno-mcp-egrul"]
}
}
}.cursor/mcp.json в проекте или ~/.cursor/mcp.json глобально){
"mcpServers": {
"egrul": {
"command": "uvx",
"args": ["atomno-mcp-egrul"]
}
}
}Если не используете
uv, замените"command": "uvx", "args": ["atomno-mcp-egrul"]на"command": "atomno-mcp-egrul"(требуетpip install atomno-mcp-egrulилиpipx install atomno-mcp-egrul).
# 1. Скачайте дампы ФНС (acceptance на сайте ФНС — раз в жизни).
# Источники:
# ЕГРЮЛ — https://www.nalog.gov.ru/opendata/7707329152-egrul/
# ЕГРИП — https://www.nalog.gov.ru/opendata/7707329152-egrip/
# Положите их в структуру:
mkdir -p dumps/egrul/2026-04-24 dumps/egrip/2026-04-24
cp ~/Downloads/EGRUL_*.zip dumps/egrul/2026-04-24/
cp ~/Downloads/EGRIP_*.zip dumps/egrip/2026-04-24/
# 2. Первоначальный полный импорт (однократно, ~30-60 минут):
docker compose --profile import run --rm \
mcp-egrul-import atomno-mcp-egrul-import --registry egrul --full
docker compose --profile import run --rm \
mcp-egrul-import atomno-mcp-egrul-import --registry egrip --full
# 3. Запустите сервер + фоновый cron-демон:
docker compose up -d
docker compose logs -f mcp-egrul-schedulerЧерез ~10 минут после импорта все тулзы (search_by_inn, search_by_name и пр.) уже отвечают данными из локального слепка ФНС.
Схема тома /data внутри контейнера:
/data/
├── mcp_egrul_data.sqlite # SQLite + FTS5
└── dumps/ # read-only монтируется из ./dumps
├── egrul/
│ └── YYYY-MM-DD/*.zip
└── egrip/
└── YYYY-MM-DD/*.zipCron-демон (atomno-mcp-egrul-scheduler) сам забирает самую свежую выгрузку после
того как вы положите её в dumps/<registry>/<YYYY-MM-DD>/ — ночью в 03:00
Europe/Moscow. Если ничего нового нет — job завершится с nothing_to_import
и никаких лишних записей в import_log не сделает.
Источники:
ЕГРЮЛ open-data: https://www.nalog.gov.ru/opendata/7707329152-egrul/
ЕГРИП open-data: https://www.nalog.gov.ru/opendata/7707329152-egrip/
Формат: суточные архивы XML в ZIP, ~15 ГБ на полный слепок. Юридически их нужно скачать с сайта ФНС после acceptance лицензии — сервер не качает архивы сам (строго).
CLI:
# Полный первоначальный импорт (однократно):
atomno-mcp-egrul-import --registry egrul --full
atomno-mcp-egrul-import --registry egrip --full
# Инкремент (cron / ручной): загружается только если появилась более
# свежая YYYY-MM-DD-папка, чем последний успешный `import_log.source_dump_date`.
# Если новее нет — exit-code 5 и сообщение `nothing_to_import`.
atomno-mcp-egrul-import --registry egrul --incremental
# Фоновой cron-демон с ежедневным 03:00 MSK (вызывать вручную редко;
# обычно запускается сервисом mcp-egrul-scheduler в docker-compose).
atomno-mcp-egrul-scheduler --run-nowExit-коды atomno-mcp-egrul-import:
Код | Значение |
0 | Импорт прошёл успешно |
2 | Невалидный конфиг / аргумент CLI |
4 | Ошибка ингеста (битый XML, нет каталога дампов, DB error) |
5 |
|
api.atomno-mcp.ru)Когда пользователь задаёт ATOMNO_API_KEY, все семь тулзов автоматически
проксируются на hosted Pro API (SPEC §5.4, §5.4.1). Локальный SQLite в этом
режиме не используется — hosted Pro даёт:
Актуальные данные на сегодня (без суточной задержки open-data дампа): прямой scrape egrul.nalog.ru + Dadata fallback на стороне сервера.
Bulk-эндпойнт без rate-limit (POST /companies/bulk) — один запрос вместо N локальных gather'ов.
AI-summary карточки, история изменений, поиск по ФИО директора (Pro-only тулзы — приезжают вместе с hosted-сервером в Phase 2, см. §5.4.1).
Цена: Pro — $10/мес отдельно или $15/мес в паре с mcp-fns-check (bundle-ключ). Free tier: 30 запросов/день/IP без регистрации (SPEC §1).
Настройка в Cursor (.cursor/mcp.json):
{
"mcpServers": {
"egrul": {
"command": "uvx",
"args": ["atomno-mcp-egrul"],
"env": {
"ATOMNO_API_KEY": "your-pro-key-here"
}
}
}
}Поведение и ошибки — никакого silent fallback: если hosted API недоступен, клиент поднимает типизированное исключение, а не молча отдаёт данные из устаревшего локального дампа. Сопоставление HTTP ↔ MCP-код ошибки — в SPEC §5.4.1:
HTTP-ответ hosted API | Исключение клиента |
|
200 | — | — |
400 |
|
|
401 |
|
|
403 |
|
|
404 (code=not_found) |
|
|
404 (wrong route) |
|
|
413 |
|
|
429 |
|
|
5xx |
|
|
timeout / DNS fail |
|
|
Валидация ИНН/ОГРН остаётся клиент-саид (контрольные цифры проверяются до HTTP-запроса — экономия round-trip на битых идентификаторах).
Переменная | Описание | По умолчанию |
| Путь к SQLite-файлу со слепком ЕГРЮЛ/ЕГРИП |
|
| User-Agent HTTP-клиента |
|
| Таймаут HTTP в секундах |
|
| Каталог с дампами ФНС, структура |
|
| Уровень логирования |
|
| Таймзона для scheduler (cron 03:00) |
|
| (Pro) ключ hosted-подписки — включает проксирование на | не задан |
| (Pro) базовый URL hosted-API |
|
Пример — см. .env.example.
apps/mcp-egrul/
├── pyproject.toml
├── LICENSE # MIT
├── README.md # ЭТОТ ФАЙЛ
├── Dockerfile
├── docker-compose.yml
├── .env.example
├── .gitignore
├── src/mcp_egrul/
│ ├── __init__.py
│ ├── server.py # FastMCP entrypoint, регистрация 7 тулзов + ping
│ ├── context.py # ServiceContext (DI: SQLiteStore + HTTP-клиент)
│ ├── config.py # Чтение env-vars в типизированные поля
│ ├── constants.py # Все магические числа и enum'ы
│ ├── validators.py # Контрольные цифры ИНН (10/12) и ОГРН (13/15)
│ ├── schemas.py # Pydantic-модели CompanyCard/IECard/SearchResult/...
│ ├── errors.py # McpEgrulError и подклассы
│ ├── db/
│ │ ├── __init__.py
│ │ └── sqlite.py # Async-клиент (aiosqlite), init/query/upsert/search + import_log
│ ├── sources/
│ │ ├── __init__.py
│ │ ├── base.py # Абстрактный интерфейс Source
│ │ ├── opendata.py # ФНС open-data адаптер (read-local → SQLite upsert)
│ │ ├── opendata_parser.py # Потоковый lxml.iterparse парсер ЕГРЮЛ/ЕГРИП XML
│ │ └── hosted_adapter.py # HTTP-клиент hosted Pro API (SPEC §5.4.1)
│ ├── tools/
│ │ ├── __init__.py
│ │ ├── search_by_inn.py
│ │ ├── search_by_ogrn.py
│ │ ├── search_by_name.py
│ │ ├── get_full_card.py
│ │ ├── get_founders.py
│ │ ├── get_director.py
│ │ └── bulk_cards.py
│ └── scripts/
│ ├── __init__.py
│ ├── import_opendata.py # CLI `atomno-mcp-egrul-import` (ручной / одноразовый)
│ └── scheduler.py # CLI `atomno-mcp-egrul-scheduler` (apscheduler cron 03:00 MSK)
└── tests/
├── __init__.py
├── conftest.py
├── fixtures/
│ ├── egrul_sample.xml # Мини-ЕГРЮЛ (2 валидных + 1 skip на неизвестный статус)
│ └── egrip_sample.xml # Мини-ЕГРИП (active + closed)
├── test_validators.py
├── test_schemas.py
├── test_config.py # Config.from_env + _parse_float_env (валидация env)
├── test_sqlite_store.py
├── test_cards.py # _cards.py: parse_iso_date/datetime + build_*card
├── test_server_ping.py # FastMCP tool-layer + server.main()
├── test_tools.py # 7 тулзов: happy-path + validation + not_found
├── test_opendata_parser.py # XML-парсер (zip, xml, skip-на-неизвестный-статус)
├── test_opendata_source.py # OpenDataSource.run_ingest (full/incremental)
├── test_integration_import.py # Полный цикл import → search → get_card
├── test_import_cli.py # CLI `atomno-mcp-egrul-import`
├── test_scheduler_cli.py # CLI `atomno-mcp-egrul-scheduler` + _run_scheduler
└── test_hosted_adapter.py # HostedClient + маршрутизация тулзов (respx-моки)pytest -v --cov=src/mcp_egrulТекущий coverage: 100.00% (345 tests passed, ruff clean, 1529 statements + 382 branches,
0 misses). Enforced политикой --cov-fail-under=100 — любая регрессия сломает CI. Тесты покрывают:
валидаторы ИНН/ОГРН/ОГРНИП (контрольные цифры);
Config.from_env + парсер float-env-переменных (валидация, а не silent fallback);
все 7 MCP-тулзов (happy-path + validation + not_found + bulk partial);
SQLite store + FTS5 + import_log;
XML-парсер ЕГРЮЛ/ЕГРИП (zip, xml, skip-запись с неизвестным статусом);
OpenDataSource.run_ingest (full/incremental/nothing_to_import);
полный интеграционный цикл import fixture → search → get_card → bulk;
обе CLI (atomno-mcp-egrul-import, atomno-mcp-egrul-scheduler) — регистрация cron-job'ов, парсинг
аргументов, _run_daily_ingest на all-happy/nothing_to_import/McpEgrulError, полный цикл
_run_scheduler с mock-ed asyncio.Event;
FastMCP tool-layer через mcp.call_tool() — сериализация ошибок в структурированные dict'ы,
server.main() с валидным и невалидным env;
HostedClient (hosted Pro API proxy) — happy-path всех 7 методов, все HTTP-ошибки из
SPEC §5.4.1 (401/403/404/413/429/5xx), timeout/ConnectError, невалидный JSON/payload от
сервера, клиентская валидация bulk, async with-контекст; плюс маршрутизация из тулзов
в hosted-режиме (при задан ATOMNO_API_KEY — запрос идёт в api.atomno-mcp.ru, не в SQLite,
валидация ИНН до HTTP);
edge-case'ы XML-парсера (75 отдельных unit-тестов на _parse_company/_parse_ie/
_parse_share/_parse_director/_parse_founders/address fallback'ы/legacy-атрибуты/
невалидные длины ИНН/ОГРН/КПП);
приватные helper'ы SQLite-стора (_wrap, _prepare_row, _row_to_dict, _normalize_bm25,
auto-init через _ensure, rejecting invalid finish_import статусов);
ServiceContext reentry-идемпотентность, atexit-cleanup, Config.from_env ValidationError
→ exit-code 2 из atomno-mcp-egrul-import CLI.
Внешние API никогда не вызываются напрямую из тестов — только через respx (HTTP-мокинг) и
локальные XML-фикстуры (tests/fixtures/).
Все источники — публично открытые данные ФНС (ЕГРЮЛ / ЕГРИП open-datasets), распространение которых разрешено ФЗ «Об информации…» и ЕГРЮЛ-специфичными нормами (см. SPEC §8).
Юридические лица не подпадают под 152-ФЗ (О персональных данных).
ФИО физлиц-руководителей и учредителей публикуются самой ФНС в открытом реестре — пересылка этих данных легальна.
Никаких write-операций ни в один внешний API.
Секреты — только через переменные окружения, в репозитории — .env.example без значений.
Сервис — агрегатор и удобный интерфейс над публичными данными ФНС. Не аффилирован с ФНС. Используется на ваш риск. Информация в ответах сервиса не является заменой полноценной юридической или финансовой оценки.
MIT. Файл LICENSE в корне папки.
Массовая выгрузка до 100 карточек за один вызов.
Вернёт объект с полями cards (успешные) и errors (точечные ошибки
по отдельным ИНН) — один плохой ИНН не ломает весь bulk.
| Name | Required | Description | Default |
|---|---|---|---|
| inns | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses important behavioral aspects like returning both successful cards and per-TIN errors, and that one bad TIN doesn't break the whole call. However, it omits whether the operation is read-only or has side effects, and no mention of authorization or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences with no fluff. The first sentence front-loads the core purpose and capacity, and the second explains the return structure and error handling. Every word contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema (one parameter) and the existence of an output schema (not provided but implied), the description sufficiently covers maximum batch size, return format (cards/errors), and partial failure behavior. It is complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the schema has no description for the 'inns' parameter, the description mentions 'отдельным ИНН' (individual TINs), clarifying that the array contains Russian tax identifiers. With 0% schema coverage, the description adds meaning beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a massive upload of up to 100 cards per call, specifying the action (выгрузка) and resource (карточек). It distinguishes itself from siblings like search_by_inn by handling multiple INNs and returning partial errors.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates usage for batch card retrieval with a capacity limit of 100, but does not explicitly state when not to use it or list alternatives. However, the context of sibling tools and the capacity hint provide implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Текущий руководитель юр.лица по ИНН (только 10-значный ИНН).
| Name | Required | Description | Default |
|---|---|---|---|
| inn | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only states it returns the current director, but fails to mention aspects like error handling, data freshness, auth requirements, or side effects. This is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, very concise and front-loaded with the key action and constraint. Every word earns its place, though it could be slightly expanded for additional clarity without losing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple single-parameter tool and the existence of an output schema, the description is minimally adequate but lacks details on edge cases or error responses. It covers the basic purpose but not the full context of usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage. The tool description adds value by specifying that the INN must be exactly 10 digits. However, it doesn't fully compensate for missing schema descriptions, e.g., no mention of format beyond digit count.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the current director of a legal entity by INN. It specifies the exact resource (director), action (get), and constraint (10-digit INN for legal entities). This distinguishes it from siblings like get_founders or search_by_inn.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide any guidance on when to use this tool versus alternatives such as get_founders or search_by_inn. It only describes what it does without contextual or comparative usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Учредители юр.лица по ИНН (только 10-значный ИНН).
| Name | Required | Description | Default |
|---|---|---|---|
| inn | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions the TIN length constraint but does not disclose whether this is a read-only operation, error handling, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that conveys the core purpose and a key constraint without any unnecessary words. It is efficiently structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown), the description does not need to detail return values. However, it lacks information on error handling, input validation details, or the structure of the founders data, which could be incomplete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, leaving the 'inn' property undocumented. The description adds the crucial constraint that only a 10-digit TIN is accepted, which is valuable beyond the schema definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves founders of a legal entity by TIN and specifies the requirement of a 10-digit TIN. This distinguishes it from siblings like get_director which targets a different role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool over alternatives like search_by_inn or get_director. It does not state prerequisites or exclusions, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Полная карточка (все секции: реквизиты, ОКВЭД, учредители, директор).
Хотя бы один из inn / ogrn обязателен. Если переданы оба — используется inn.
| Name | Required | Description | Default |
|---|---|---|---|
| inn | No | ||
| ogrn | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that at least one of inn/ogrn is required and inn takes precedence, but does not mention read-only nature, authentication needs, or error handling for missing parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and front-loads the purpose. It is concise with no unnecessary words, though structure could be improved with bullet points for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema and only two parameters, the description covers the primary requirements: purpose and parameter constraints. It lacks mention of error cases or usage limitations, but is largely complete for a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the description adds meaning by clarifying that inn and ogrn are alternative identifiers, at least one is mandatory, and inn is used if both are provided. This significantly compensates for the schema's lack of parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Full card (all sections: details, OKVED, founders, director)', clearly indicating it retrieves a complete company card. This distinguishes it from sibling tools like get_director and get_founders, which target specific sections.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving full card data, but does not explicitly contrast with siblings or state when to use this tool over alternatives like search_by_inn or get_director. No exclusion criteria or context is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Диагностика: сервер жив, сообщает версию и размер локального слепка.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description takes full burden. It discloses that the tool reports version and snapshot size, which is useful. However, it does not mention read-only nature, safety, or potential side effects, though for a ping tool these are minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence that front-loads the main purpose ('Диагностика') and immediately specifies what the tool reports. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters, an output schema exists (not shown but referenced), and a set of sibling tools, the description is complete for this simple tool: it tells the agent exactly what the tool returns and its role as a health check.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the description need not add parameter info. Baseline for zero parameters is 4, and the description appropriately focuses on the tool's output rather than parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is for diagnostics: checking server liveness, version, and snapshot size. It uses a specific verb ('диагностика') and resource ('сервер'), and is distinct from sibling tools that handle cards, directors, or searches.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for health checks but does not explicitly state when to use this tool vs alternatives, nor does it mention exclusions or prerequisites. Given the clear purpose, usage is implied but not guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Карточка юр.лица или ИП по ИНН (10 цифр — ООО/АО, 12 — ИП/физлицо).
| Name | Required | Description | Default |
|---|---|---|---|
| inn | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must cover behavioral traits. It only states the searchby TIN and format, omitting any details on error handling, rate limits, or return behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (one short phrase) and front-loaded with the core purpose, containing no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple lookup tool with an output schema, the description is minimally complete but lacks details on error cases and what the card contains, leaving some gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameter descriptions (0% coverage), but the description adds critical semantic meaning by explaining the TIN length and entity mapping, significantly aiding correct usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a card of a legal entity or individual entrepreneur by TIN, and distinguishes it from siblings by specifying TIN format (10/12 digits) and entity types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for TIN-based lookup, distinct from siblings like search_by_name or search_by_ogrn, but provides no explicit when-to-use or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fuzzy-поиск юр.лиц по названию через FTS5.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | максимум результатов (1..50). | |
| query | Yes | строка запроса (минимум 2 символа). | |
| only_active | No | фильтровать только записи со статусом 'active'. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions fuzzy search via FTS5 but omits details like matching behavior, result ordering, or handling of misspellings.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single sentence, concise but lacking structure. It could benefit from additional context without being lengthy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and only input schema provided, the description omits important behavioral traits and result format expectations. Even with an output schema, more context would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for all parameters. The description adds no new meaning beyond 'fuzzy', so baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it is a fuzzy search of legal entities by name using FTS5, which is specific and distinguishes from siblings like search_by_inn and search_by_ogrn.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. It implicitly targets name-based searches, but without stating exclusions or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Карточка по ОГРН (13 цифр) или ОГРНИП (15 цифр).
| Name | Required | Description | Default |
|---|---|---|---|
| ogrn | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It only states what the tool does (retrieve a card) but omits details about error handling, side effects, or what happens for invalid inputs. This is insufficient for an agent to fully anticipate behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is highly concise and front-loaded. However, it could be structured with separate sentences for clarity, but for a simple tool it is appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema (not shown), the description may not need to detail return values. It provides the parameter format but lacks any mention of error handling or usage context. For a simple lookup tool, it is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It adds meaningful format constraints: 13 digits for OGRN and 15 digits for OGRNIP, which is valuable beyond the plain string type in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves a card by OGRN (13 digits) or OGRNIP (15 digits), making the purpose and resource specific. It distinguishes itself from sibling tools like search_by_inn and search_by_name by indicating the identifier type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no explicit guidance on when to use this tool versus alternatives or when not to use it. The context is only implied by the tool name and description, with no mention of prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.2bulk_cardsget_directorget_foundersget_full_cardpingsearch_by_innsearch_by_namesearch_by_ogrnEach tool targets a distinct query type: bulk export, specific fields (director, founders), full card, health check, and three search methods (INN, name, OGRN). No overlapping purposes.
Most tools use a verb_noun pattern (get_director, get_founders, search_by_inn, etc.). Ping and bulk_cards are minor deviations but still clear.
Eight tools cover a complete set of operations for a business registry: multiple search methods, specific field lookups, bulk export, and health check. No extraneous tools.
Covers essential read operations for a registry: search by various identifiers, retrieval of full cards and specific fields, bulk export. Missing filtering or advanced search (e.g., by region) but acceptable for a focused server.
Hosted MCP server for real-world data: business registries, sanctions, companies, domains, crypto.
MCP server for US nursing facility search and ownership lookup (NursingHomeDatabase).
MCP server for Russian books search, details, and recommendation candidates.
MCP server for nonprofit financials via ProPublica — IRS Form 990 data for 1.8M+ nonprofits.
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