@crontinel/mcp-server
OfficialAllows querying the status of Laravel cron jobs and queues via Crontinel monitoring.
Allows retrieving health snapshots of Laravel Horizon supervisors, including status and failed jobs per minute.
Allows creating Slack alert channels for Crontinel notifications.
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., "@@crontinel/mcp-serverwhat's the queue depth for app my-app?"
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.
@crontinel/mcp-server
An MCP (Model Context Protocol) server that connects AI assistants to Crontinel, the background job monitoring platform for Laravel. It runs as a local stdio process, proxying tool calls from your AI assistant to the Crontinel REST API.
Ask your AI assistant questions like "Did my cron jobs run last night?" or "What's the queue depth right now?" or "Trigger a redeploy" and get answers inline, without opening a browser.
Requirements
Node.js 18+
A Crontinel account with an API key — get one at app.crontinel.com/settings
Related MCP server: Lanten MCP Server
Installation
npx -y @crontinel/mcp-serverOr install globally:
npm install -g @crontinel/mcp-serverConfiguration
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"crontinel": {
"command": "npx",
"args": ["-y", "@crontinel/mcp-server"],
"env": {
"CRONTINEL_API_KEY": "your-api-key-here"
}
}
}
}Claude Code
Add to ~/.claude/settings.json (or use the Claude Code settings UI):
{
"mcpServers": {
"crontinel": {
"command": "npx",
"args": ["-y", "@crontinel/mcp-server"],
"env": {
"CRONTINEL_API_KEY": "your-api-key-here"
}
}
}
}Cursor
Add to ~/.cursor/mcp.json or the project-level .cursor/mcp.json:
{
"mcpServers": {
"crontinel": {
"command": "npx",
"args": ["-y", "@crontinel/mcp-server"],
"env": {
"CRONTINEL_API_KEY": "your-api-key-here"
}
}
}
}Windsurf
Add to ~/.windsurf/settings.json:
{
"mcpServers": {
"crontinel": {
"command": "npx",
"args": ["-y", "@crontinel/mcp-server"],
"env": {
"CRONTINEL_API_KEY": "your-api-key-here"
}
}
}
}Continue.dev
Add to ~/.continue/config.json:
{
"experimental": {
"mcpServers": {
"crontinel": {
"command": "npx",
"args": ["-y", "@crontinel/mcp-server"],
"env": {
"CRONTINEL_API_KEY": "your-api-key-here"
}
}
}
}
}Environment Variables
Variable | Required | Default | Description |
| Yes | n/a | Your Crontinel API key |
| No |
| Override the API base URL (self-hosted or local dev) |
Available Tools
Tool | Description |
| List all monitored cron commands with last run status |
| Last run details for a specific command (exit code, duration, output) |
| Depth, failed count, and wait time for queues |
| Horizon supervisor health snapshot (status, failed/min) |
| Alerts fired in the last N hours |
| Dismiss an active alert by its key |
| Create a new alert channel (Slack, email, or webhook) |
list_scheduled_jobs
List all monitored cron jobs, with their last run status and timing.
Returns: Array of job objects with command, last_run_at, last_status, run_count_today.
get_cron_status
Get the last run result for a specific cron command.
Parameters:
Name | Type | Required | Description |
| string | Yes | The cron command string or partial match (e.g. |
Returns: command, status, exit_code, duration_ms, started_at, finished_at, output.
get_queue_status
Get queue depth, failed count, and oldest pending job age.
Parameters:
Name | Type | Required | Description |
| string | No | Specific queue name; omit for all queues |
Returns: Array of queue objects with name, depth, failed, oldest_job_age_seconds.
get_horizon_status
Get a health snapshot of Laravel Horizon: supervisor states, paused/running, failed jobs per minute.
Returns: status (running / paused / inactive), failed_jobs_per_minute, supervisors array.
list_recent_alerts
List alerts that have fired within the last N hours.
Parameters:
Name | Type | Required | Description |
| number | No | Look-back window in hours (default: 24) |
Returns: Array of alert objects with alert_key, state (firing / resolved), fired_at, resolved_at.
acknowledge_alert
Dismiss an active alert so it stops notifying.
Parameters:
Name | Type | Required | Description |
| string | Yes | Alert key (from |
Returns: { acknowledged: true, alert_key: "..." } on success.
create_alert
Create a new alert channel for an app. Requires a Starter, Pro, or Ultra plan.
Parameters:
Name | Type | Required | Description |
| string | Yes |
|
| string | No | Slack incoming webhook URL (required for |
| string | No | Recipient email address (required for |
| string | No | Webhook endpoint URL (required for |
Returns: { created: true, channel_id: "...", type: "..." } on success.
How It Works
Your AI assistant spawns the MCP server as a local stdio process
The server receives JSON-RPC tool calls over stdin
It forwards each call as an HTTP request to
app.crontinel.com/api/mcpwith your API key in theAuthorizationheaderThe JSON-RPC response is returned over stdout
All tool definitions are declared locally so your AI can inspect them without a network round-trip.
Troubleshooting
401 Unauthorized: Your CRONTINEL_API_KEY is missing or invalid. Check that the env var is set in your MCP config, not your shell profile (MCP servers don't inherit your shell environment).
404 Not Found on a tool call: The app_slug doesn't match any app in your account. Copy the slug from the app URL in the Crontinel dashboard (app.crontinel.com/apps/{slug}).
Tools not showing up in Claude/Cursor: Restart the AI client after updating the MCP config. Most clients only load MCP servers at startup.
npx slow on first run: npx -y downloads the package on first use. Run npm install -g @crontinel/mcp-server once to cache it locally, then change command to crontinel-mcp and remove the args.
Documentation
For the full integration guide, tool reference, and setup walkthroughs:
Ecosystem
Package | Description |
MCP server for AI assistants (this repo) | |
Laravel package that reports the data this server reads | |
Full documentation |
License
Available Tools
7 toolsacknowledge_alertA
Dismiss an active alert by its alert key
| Name | Required | Description | Default |
|---|---|---|---|
| alert_key | Yes | Alert key to dismiss (e.g. "horizon:paused", "queue:emails:depth") |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It merely states the action without disclosing what 'dismiss' entails—whether it permanently deletes the alert, marks it as acknowledged, requires specific permissions, or is reversible. This is a significant transparency gap for a mutation tool.
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 with direct, front-loaded wording. Every word earns its place, making it highly concise and structured appropriately.
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 tool (one parameter, full schema coverage, no output schema), the description is minimally adequate. However, it lacks details about return behavior or side effects, which an output schema or more descriptive text could provide. Complete enough for a basic dismissal but not informative.
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?
Input schema covers 100% of the parameter (alert_key) with a description and example, so baseline is 3. The tool description adds no additional meaning beyond restating 'by its alert key', not improving semantic understanding beyond 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 the action 'Dismiss' and the resource 'active alert' by 'alert key', which is specific and unambiguous. It distinguishes from siblings like create_alert and list_recent_alerts by focusing on dismissal.
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 (dismissing active alerts when they need to be acknowledged) but does not explicitly state when to use this instead of other tools like list_recent_alerts or create_alert. No exclusions or alternatives are mentioned, making usage guidance implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_alertB
Create a new alert channel (slack, email, or webhook) for the app
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | Recipient email address (required when type=email) | |
| url | No | Webhook endpoint URL (required when type=webhook) | |
| type | Yes | Alert channel type | |
| webhook_url | No | Slack incoming webhook URL (required when type=slack) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It only states 'create' without mentioning side effects, required permissions, behavior on duplicate channels, or what happens on success/failure.
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 concise sentence that front-loads the action and resource, with zero 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?
Despite comprehensive parameter schema, the tool has no annotations or output schema, and the description omits return values, permissions, and edge-case behaviors. The overall context is incomplete for a mutation 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?
Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific meaning beyond what the schema already provides (e.g., conditional fields for email/webhook/slack).
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 'Create a new alert channel' with explicit types (slack, email, or webhook), making the action and resource specific. It distinguishes this creation tool from sibling tools like list_recent_alerts and acknowledge_alert.
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 (when you need to set up an alert channel) but does not explicitly state when to use this tool versus alternatives or any exclusions. No reference to sibling tools or conditions is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cron_statusA
Get the last run status, exit code, and duration for a specific cron command
| Name | Required | Description | Default |
|---|---|---|---|
| command | Yes | Command name or partial match (e.g. "send-invoices") |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the burden of behavioral disclosure. The verb 'Get' implies a read-only operation, and specifying the return fields (status, exit code, duration) adds useful context. However, it does not explicitly state that the tool has no side effects, nor does it describe error conditions or behavior when the command is not found.
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 of fourteen words, front-loading the verb and result. Every word contributes meaning, with no redundancy or filler.
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 single-parameter read tool with no output schema, the description covers the essential purpose and return data. It could mention return formatting or edge cases, but the tool's simplicity and schema coverage make it adequately complete.
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 fully describes the single parameter, including an example and partial-match behavior. The description's phrase 'specific cron command' adds minimal semantic value beyond the schema, 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 uses the verb 'Get' and specifies the resource ('specific cron command') and the exact data returned ('last run status, exit code, and duration'). This clearly distinguishes it from sibling tools like get_queue_status or list_scheduled_jobs, which target different resources.
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 checking the status of a specific cron command, but it does not explicitly contrast with sibling tools or provide when-not-to-use guidance. For example, it doesn't mention that list_scheduled_jobs should be used to see all scheduled jobs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_horizon_statusA
Get a snapshot of Laravel Horizon health: supervisor states, paused status, failed jobs per minute
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds concrete behavioral context by enumerating the returned data (supervisor states, paused status, failed jobs per minute), making clear it is a read-only snapshot. With no annotations, it does not explicitly state non-destructiveness or auth requirements, but the 'Get' verb implies a safe read operation. It covers the key behavior beyond the tool 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?
A single, front-loaded sentence with no redundant words. It immediately names the action and resource, then lists the relevant data points. Optimal conciseness.
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 parameterless read-only tool with no output schema, the description sufficiently communicates what the tool does and what it returns. The listed items give a clear picture, though it lacks explicit return format or error behavior. This is adequate for a simple health-check snapshot.
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 tool has zero parameters and an empty schema, so the description correctly adds no parameter-specific information. Per the rubric, zero parameters baseline is 4.
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 ('Laravel Horizon health'), listing exact data points (supervisor states, paused status, failed jobs per minute). This distinguishes it from sibling tools like get_queue_status by focusing on Horizon specifically.
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. It does not mention sibling tools, exclusions, or specific scenarios. The word 'Horizon' implies a Laravel-specific context, but explicit usage instructions are absent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_queue_statusA
Get queue depth, failed job count, and oldest job age for all queues or a specific queue
| Name | Required | Description | Default |
|---|---|---|---|
| queue | No | Queue name (optional — returns all queues if omitted) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior itself. 'Get' implies a read-only operation, and the description lists the returned data, which is helpful. However, it says nothing about error behavior, authentication requirements, rate limits, or whether any side effects occur, so some transparency gap remains.
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, tightly scoped sentence that conveys the tool's purpose, data returned, and parameter scope without fluff or repetition. Every word earns its place, and it is easy to parse.
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 tool with one optional parameter, the description adequately covers the key aspects: what metrics are returned and how scope is controlled. The absence of an output schema is partially compensated by listing the metrics, though it does not describe the return format (e.g., object structure) or failure modes, which would improve completeness.
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 already provides a full description for the single 'queue' parameter (100% coverage), including the behavior when omitted. The tool description adds no new parameter-specific meaning beyond the schema, so it does not elevate the baseline.
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's function with a specific verb ('Get') and resource ('queue status'), listing concrete metrics (queue depth, failed job count, oldest job age) and scope ('all queues or a specific queue'). This distinguishes it from sibling tools like get_cron_status or get_horizon_status, which target different resources.
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 given on when to choose this tool over alternatives, nor are any exclusions or prerequisites mentioned. The only usage nuance is the optional 'queue' parameter, which the schema also communicates. The description does not provide context to help an agent decide between this and sibling status tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_recent_alertsA
List alerts that have fired in the last N hours
| Name | Required | Description | Default |
|---|---|---|---|
| hours | No | Look-back window in hours (default: 24) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. The verb 'list' suggests a read-only operation, and the time-window constraint is stated. However, it does not explicitly confirm that the tool has no side effects (e.g., does not acknowledge alerts) nor describe the return format, leaving some behavioral details opaque.
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, front-loaded sentence with no redundant information. Every word contributes meaning, and it is appropriately concise for a simple list operation.
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 optional-parameter list tool, the description adequately conveys the core functionality, but it lacks coverage of return structure and side-effect guarantees. Given the absence of an output schema and annotations, the description could be more complete (e.g., mention read-only nature explicitly), but it is not severely underspecified.
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 already provides full coverage of the single 'hours' parameter, including its meaning and default value. The tool description's reference to 'N hours' adds no additional semantic detail beyond the schema, 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 uses the specific verb 'list' with a clear resource ('alerts') and scope ('fired in the last N hours'), which unambiguously distinguishes it from sibling tools that target scheduled jobs or statuses. It clearly states what the tool does.
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 when to use this tool (when you need to see alerts that have fired in a recent time window) but does not explicitly state when not to use it or mention alternatives like acknowledge_alert or the status tools. There is no comparison to siblings, so usage guidance is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_scheduled_jobsA
List all monitored cron commands with their last run status and timing
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits. The verb 'List' signals a read-only operation, and the content of results (last run status and timing) is disclosed. However, it doesn't explicitly state that no changes are made or that it provides a snapshot view, though that is largely inferable. Given the low-risk nature, this is adequate but not rich.
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?
A single, grammatically clear sentence with no fluff; the verb and object are front-loaded, making the purpose immediately apparent.
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?
This is a simple listing tool with no parameters and no output schema. The description tells the user what data will be returned (status and timing), which is sufficient for this level of complexity. It could note that it only covers 'monitored' cron commands, which is a constraint, but overall it's adequately complete.
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 tool has zero parameters, and the schema is empty. Per the baseline rule, a score of 4 is given since the description doesn't need to compensate for parameter documentation when there are none.
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 uses the specific verb 'List' and identifies the resource as 'monitored cron commands', clearly distinguishing it from sibling tools like get_cron_status (which likely targets a single job) by emphasizing the list scope.
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 clearly implies this is for listing all monitored cron commands, but it does not explicitly contrast with alternatives like get_cron_status for per-job status, nor does it state when not to use it. This leaves usage guidance implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
7 tool updates
v0.2.0- First observed
acknowledge_alert - First observed
create_alert - First observed
get_cron_status - First observed
get_horizon_status - First observed
get_queue_status - First observed
list_recent_alerts - First observed
list_scheduled_jobs
TDQS
Each tool targets a distinct resource and action: listing vs. getting cron status, queue vs. Horizon status, and alert listing/acknowledgment/creation. Descriptions clearly differentiate overlapping monitoring concepts.
All tool names follow a consistent verb_noun pattern: list_, get_, acknowledge_, create_. No mixed conventions, making the set predictable and easy to navigate.
Seven tools is well-scoped for a monitoring server, covering the core areas (cron, queues, Horizon, alerts) without unnecessary redundancy.
The set covers monitoring and alert management comprehensively, but lacks an update/delete for alert channels and a detail view for individual alerts. Minor gaps that agents can work around.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Connects AI assistants to CloudQuell multi-cloud and AI cost, savings, anomaly, and budget data.
Connect AI assistants to Xitoring monitoring: servers, uptime, incidents, metrics, SSL, and alerts.
Connect any AI assistant to Syncro: manage tickets, invoices, customers, assets, and more.
- mcpOAuthcom.keboola
Connect your AI assistants to Keboola and expose your data, transformations, SQL queries, ...
Related MCP Servers
AlicenseBqualityDmaintenanceConnects AI assistants (Claude, Cursor, etc.) directly to the HiveFlow automation platform, allowing them to create, manage, and execute automation flows through natural language commands.9164MIT- FlicenseAqualityDmaintenanceConnects AI assistants to the Lanten property management API, allowing them to manage tenants, units, and work orders using natural language.1513-
- AlicenseNot gradedqualityCmaintenanceEnables secure execution of Laravel Artisan commands through AI assistants, allowing controlled management of Laravel projects via natural language.6MIT
- FlicenseNot gradedqualityDmaintenanceConnects AI assistants to live VuNet observability data, allowing querying metrics, browsing data models, and checking connection status.-
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/crontinel/mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server