Skip to main content
Glama

acknowledge_alert

Acknowledge clinical alerts in athenahealth workflows to confirm review and maintain accurate patient care documentation.

Instructions

Acknowledge a clinical alert

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
alert_idYesAlert ID
acknowledged_byYesUser acknowledging the alert

Implementation Reference

  • The primary MCP tool handler that executes acknowledge_alert: destructures arguments, calls the AthenaHealthClient, logs audit trail, and returns standardized MCP response.
    async handleAcknowledgeAlert(args: any) {
      const { alert_id, acknowledged_by } = args;
      await this.client.acknowledgeAlert(alert_id, acknowledged_by);
    
      auditLog('ALERT_ACKNOWLEDGE', {
        resourceType: 'CLINICAL_ALERT',
        resourceId: alert_id,
        userId: acknowledged_by,
        result: 'success',
      });
    
      return {
        content: [
          {
            type: 'text' as const,
            text: JSON.stringify({ success: true, message: 'Alert acknowledged' }, null, 2),
          },
        ],
      };
    }
  • MCP tool schema definition specifying input validation for alert_id and acknowledged_by parameters.
    {
      name: 'acknowledge_alert',
      description: 'Acknowledge a clinical alert',
      inputSchema: {
        type: 'object',
        properties: {
          alert_id: { type: 'string', description: 'Alert ID' },
          acknowledged_by: { type: 'string', description: 'User acknowledging the alert' },
        },
        required: ['alert_id', 'acknowledged_by'],
      },
    },
  • Tool registration in MCP server: switch case in CallToolRequestSchema handler dispatches to the specific tool handler method.
    case 'acknowledge_alert':
      return await this.toolHandlers.handleAcknowledgeAlert(args);
  • Core service helper: makes the actual HTTP POST request to Athenahealth API endpoint to acknowledge the clinical alert.
    async acknowledgeAlert(alertId: string, acknowledgedBy: string): Promise<void> {
      const formData = new URLSearchParams();
      formData.append('acknowledgedby', acknowledgedBy);
    
      await this.makeRequest(
        `${this.config.practice_id}/clinicalalerts/${alertId}/acknowledge`,
        {
          method: 'POST',
          data: formData.toString(),
          headers: {
            'Content-Type': 'application/x-www-form-urlencoded',
          },
        }
      );
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Acknowledge' implies a state change (likely marking an alert as acknowledged), but the description doesn't specify if this is a mutation, what permissions are needed, whether it's reversible, or what the response looks like. For a clinical tool with zero annotation coverage, this is a significant gap in safety and operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste—it directly states the tool's purpose without fluff. It's appropriately sized for a simple tool and front-loaded, making it easy for an AI agent to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the clinical context, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., mutation effects, error handling), return values, or clinical workflow integration. For a tool that likely modifies alert states in a healthcare setting, more context is needed to ensure safe and correct usage by an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both parameters ('alert_id' and 'acknowledged_by') documented in the schema. The description adds no additional meaning beyond the schema, such as format examples or clinical context (e.g., alert IDs from prior alerts, user identifiers). Baseline 3 is appropriate since the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('acknowledge') and resource ('clinical alert'), making the purpose immediately understandable. It doesn't differentiate from sibling tools, but all siblings are distinct operations (create, get, list, update) rather than alternative alert management tools, so differentiation isn't needed. The purpose is specific but could be more detailed about what 'acknowledge' entails.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. There are no explicit sibling tools for alert management, but it doesn't mention prerequisites (e.g., after receiving an alert), exclusions, or context for acknowledgment. Usage is implied by the name but not elaborated, leaving gaps for an AI agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

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/ophydami/Athenahealth-MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server