Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@F6 XDR MCP Server Show me critical unresolved alerts from the last 24 hours"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP-сервер к платформе F6 XDR. Даёт ИИ-ассистенту доступ к алертам, инцидентам, письмам, файлам, хостам и журналу — поиском на языке запросов платформы.
Тул | Назначение |
| Поиск по секции; пустой запрос возвращает секцию целиком |
| Количество записей |
| Доступные поля поиска |
| Доступные значения фильтров |
| Пометить объект: закрыть алерт, отметить ложное срабатывание |
Секции: alerts, incidents, emails, files, events, connections, assets,
modules, audit, applications.
Первые четыре тула только читают. xdr_mark_event изменяет данные и по умолчанию
выключен, см. раздел «Безопасность».
Related MCP server: F5 MCP Server
Нужен Python 3.10+ и персональный API-токен пользователя XDR.
{
"mcpServers": {
"xdr": {
"command": "uvx",
"args": ["xdr-mcp"],
"env": {
"XDR_BASE_URL": "https://<your-xdr-host>",
"XDR_API_KEY": "<token>"
}
}
}
}Этот блок подходит для Claude Desktop, Cursor и VS Code. Для Claude Code:
claude mcp add xdr \
-e XDR_BASE_URL=https://<your-xdr-host> -e XDR_API_KEY=<token> \
-- uvx xdr-mcpВместо uvx можно использовать pip install xdr-mcp и команду xdr-mcp, либо
контейнер — docker run -i --rm -e XDR_BASE_URL -e XDR_API_KEY ghcr.io/f6-security/xdr-mcp
(образ собран под linux/amd64 и linux/arm64).
Переменная | Назначение |
| Адрес вашей инсталляции XDR |
| Персональный API-токен пользователя |
|
|
| PEM-бандл для инсталляции за внутренним УЦ |
alerts severity : "critical" AND resolved : "false"
alerts timestamp >= now-1d AND NOT false_positive : "true"
incidents closed : "false" AND timestamp >= now-30d
emails is_blocked : "true" AND timestamp >= now-1d
assets edr_active : "true"
applications vendor : "Microsoft Corporation"
audit success : "false" AND timestamp >= now-7dУсловия объединяются через AND, OR, NOT и группируются скобками. Время —
поле timestamp, относительное (now-1d, now-6h) или абсолютное. Какие поля
доступны в секции, покажет xdr_get_mapping.
Всё, что возвращают читающие тулы, попадает в контекст языковой модели: адреса и темы писем, имена файлов, хосты, журнал действий. Выдавайте серверу токен с минимально необходимыми правами.
xdr_mark_event создаёт матчер — правило, которое применяется ко всем объектам,
подходящим под выражение, и этим тулом не отменяется. Поэтому тул выключен по
умолчанию; включайте XDR_ALLOW_WRITE осознанно и не добавляйте его в
авто-подтверждение MCP-клиента.
Модель угроз и порядок сообщения об уязвимостях — SECURITY.md.
См. CONTRIBUTING.md.
Get the total count of records in an XDR section. Optionally filter by query to count matching records only.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Search query string using field:value syntax, e.g. 'resolved : "false" AND severity : "critical"'. Pass an empty string to list the whole section unfiltered — deliberately, since that can be a very large number of records. Fields can be combined with AND, OR, AND NOT, OR NOT and grouped with parentheses. Call xdr_get_mapping for the fields a section accepts, and xdr_get_filters for the values an enumerated field takes — a value outside that set returns zero results rather than an error. Time filtering uses the timestamp field, relative (timestamp >= now-1d, now-6h) or absolute (timestamp >= "2024-01-01T00:00:00.000+03:00"). The calendar-rounding forms now/d, now/w and now/M return nothing, so use now-1d and the like. Note that timestamp is a search field: the ordering parameter takes an entirely different set of names. For the "applications" section, filter by asset, e.g. (asset: "<machine_id>"). A query the API cannot parse is not rejected — it is treated as free text, so verify that the results match what you asked for. (optional) | |
| section | Yes | Section name, one of: alerts, incidents, emails, files, events, connections, assets, modules, audit, applications |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description (and its embedded schema text) discloses many behaviors beyond the readOnlyHint and openWorldHint annotations: empty query returns the whole unfiltered section, unparseable queries are treated as free text rather than rejected, calendar-rounding time forms return nothing, and enumerated-field mismatches return zero results. This is rich, non-obvious behavioral context that helps the agent avoid pitfalls.
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 that front-load the core purpose ('Get the total count of records in an XDR section') and add the key optional-filter nuance. No filler or redundancy; every word earns its place.
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?
The description is complete for the main purpose, and the schema covers the query intricacies. It does not explicitly state the return value (e.g., an integer count), but given the tool's simplicity and the absence of an output schema, that is a minor omission. The description adequately equips an agent to call the tool correctly.
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 description coverage is 100%, and the schema's parameter descriptions are already extremely detailed, covering syntax, examples, and edge cases. The tool description adds no additional parameter meaning beyond what the schema provides, so the baseline of 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?
The description states a specific verb ('Get') and resource ('total count of records in an XDR section'), and distinguishes it from siblings like xdr_search (which would return records rather than a count). The purpose is immediately unambiguous and not a tautology.
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 counting rather than retrieving records, and the query parameter documentation (in the schema) provides extensive guidance on filtering syntax and edge cases. However, it does not explicitly name alternatives like xdr_search or state when to prefer this tool over them, so the routing is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get available filter options for an XDR section.
| Name | Required | Description | Default |
|---|---|---|---|
| section | Yes | Section name, one of: alerts, incidents, emails, files, events, connections, assets, modules, audit, applications |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, covering safety and data scope. However, the description adds no additional behavioral context—such as response format, pagination, or whether the result set is dynamic. With annotations present, the bar is lower, but the description still contributes nothing beyond the bare function name.
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, efficient sentence with no filler. It is front-loaded with the core action. While terse, it avoids unnecessary words and is appropriately concise for a simple getter.
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?
The description lacks context about what 'filter options' entail, how the output is structured, or when to use this tool in a workflow. With no output schema and only a single parameter, the description should explain more about the tool's role, but it stays minimal. This is insufficient for an agent to fully understand the tool's purpose in a broader context.
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 description coverage is 100%, so the parameter `section` is fully documented in the schema with an enum list. The description itself adds no extra meaning about the parameter or how to select a value. Baseline 3 applies because the schema does the heavy lifting.
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 a specific action (get) and resource (available filter options) tied to an XDR section. While it doesn't explicitly differentiate from siblings like xdr_search or xdr_count, the purpose is unambiguous and distinct enough for an agent to infer the intended function.
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 guidance is provided on when to use this tool versus alternatives. There is no mention of when filter options are needed, how they relate to search or count operations, or any exclusions. The description leaves usage entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all available search fields for an XDR section. Returns field names, labels, categories, and supported operators. Call this first to understand what you can search on.
| Name | Required | Description | Default |
|---|---|---|---|
| section | Yes | Section name, one of: alerts, incidents, emails, files, events, connections, assets, modules, audit, applications |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is known. The description adds useful context by specifying what is returned (field names, labels, categories, supported operators), which is beyond the annotations but does not go into depth about edge cases or response structure.
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?
Two sentences with no fluff: the first states the action and result, the second gives direct usage guidance. Information is front-loaded and every word earns its place.
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 read-only discovery tool with a single enum parameter and no output schema, the description is sufficient. It tells the agent what to expect and when to use it. While it could mention output format details, the listed return items are adequate for the tool's purpose.
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 a full description and enum for the 'section' parameter. The description does not add extra meaning about the parameter beyond what the schema already provides, so the baseline of 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?
The description clearly states a specific verb ('Get') and resource ('all available search fields for an XDR section'), and lists the returned data types. It does not explicitly name sibling tools to differentiate, but the purpose is distinct and understandable.
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 provides explicit guidance to 'Call this first to understand what you can search on', indicating when to use it relative to other tools. It does not mention exclusions or alternative conditions, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for events/objects in an XDR section. Returns matching records; an empty query returns the section unfiltered. Use xdr_get_mapping first to discover available fields.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query string using field:value syntax, e.g. 'resolved : "false" AND severity : "critical"'. Pass an empty string to list the whole section unfiltered — deliberately, since that can be a very large number of records. Fields can be combined with AND, OR, AND NOT, OR NOT and grouped with parentheses. Call xdr_get_mapping for the fields a section accepts, and xdr_get_filters for the values an enumerated field takes — a value outside that set returns zero results rather than an error. Time filtering uses the timestamp field, relative (timestamp >= now-1d, now-6h) or absolute (timestamp >= "2024-01-01T00:00:00.000+03:00"). The calendar-rounding forms now/d, now/w and now/M return nothing, so use now-1d and the like. Note that timestamp is a search field: the ordering parameter takes an entirely different set of names. For the "applications" section, filter by asset, e.g. (asset: "<machine_id>"). A query the API cannot parse is not rejected — it is treated as free text, so verify that the results match what you asked for. | |
| offset | No | Number of results to skip. For the "modules" section the API pages in fixed steps, so offset must be a multiple of page_size there. | |
| section | Yes | Section name, one of: alerts, incidents, emails, files, events, connections, assets, modules, audit, applications | |
| ordering | No | Sort field with optional - prefix for descending, e.g. -created_at (newest first). An unknown field is silently ignored: the API reports no error and the results come back in some other order, so use only the fields listed here. Per section — alerts: created_at, last_event, last_updated, severity; incidents: created_at, updated_at; emails: ts_created, delay; files: ts_created, delay; events: Header.Timestamp; connections: ts_parsed, ts_last_usage_parsed, service_count, duration; assets: last_activity, first_activity; modules: ts_created, name; audit: timestamp; applications: name, ts_created, ts_updated. | |
| page_size | No | Number of results per page (1-200, default 10) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint, so the bar is lower. The description adds a meaningful behavioral trait: an empty query returns the section unfiltered. It does not repeat edge cases like silent query/ordering failures, but those live in the schema and the description still adds useful behavior beyond annotations.
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?
Two sentences, front-loaded with the core purpose, and every sentence earns its place. It avoids restating schema content and is appropriately sized for a tool with a rich schema.
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?
The tool is complex and the schema is exceptionally detailed, covering query syntax, field lists, ordering, pagination, and edge cases. The description supplies the essential selection-level context and output behavior. It could mention the count alternative, but the sibling list already exposes that option.
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 description coverage is 100%, so the baseline is 3. The description itself adds no parameter-specific semantics beyond what the schema already provides, which is acceptable given the schema's richness.
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 states a specific verb and resource: search for events/objects in an XDR section and return matching records. It also adds the defining empty-query behavior, which clearly distinguishes it from siblings like xdr_count, xdr_get_mapping, and xdr_get_filters.
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?
Explicitly directs the agent to call xdr_get_mapping first to discover available fields, which is strong sequencing guidance. It does not explicitly say when to prefer xdr_count instead, but the usage context is otherwise clear.
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.
v0.1.0xdr_countxdr_get_filtersxdr_get_mappingxdr_searchEach tool has a clearly distinct purpose: search returns records, count returns aggregates, get_mapping lists searchable fields, and get_filters lists filter options. There is no functional overlap that would cause an agent to select the wrong tool.
All tool names follow a consistent pattern: the xdr_ prefix plus an action verb (search, count, get_mapping, get_filters). The convention is uniform and predictable.
With only 4 tools, the server is tightly scoped to a focused XDR query/exploration workflow. Each tool earns its place, and the count is well within the ideal range.
The server covers the core XDR search workflow well: discover fields, inspect filters, run searches, and get counts. A minor gap is the lack of an explicit tool for listing available XDR sections, but this may be outside the intended scope.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
SOAR security playbooks for AI agents: fetch, full-text search, and count. Metered via Stripe.
Enrich, search, assess, and manage threat intelligence through 80+ typed MCP tools.
Query your team's drift, vulnerability, and upgrade data from any AI assistant. OAuth 2.1, 51 tools.
Query and retrieve information about various adversarial tactics and techniques used in cyber atta…
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/F6-Security/xdr-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
