AI Integration Server for Honeycomb Observability

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Core Concept

This server component establishes a robust conduit leveraging the Model Context Protocol (MCP) specification. Its primary function is to grant AI agents authorized, programmatic access to the functionalities exposed by the Honeycomb API for deep system monitoring and performance analysis.

Functionality enabled via this interface encompasses the full lifecycle management and retrieval operations for Honeycomb constructs: datasets, complex analytical queries, streamed events, executive dashboards, service health markers, Service Level Objectives (SLOs), and automated response triggers.

Repository Summary

This package delivers a self-contained, deployable implementation of the Honeycomb MCP intermediary. It is designed to seamlessly embed Honeycomb's advanced observability capabilities directly into AI-driven workflows, significantly accelerating incident response and performance tuning.

Deployment Prerequisites & Setup

System Requirements

• A contemporary Node.js runtime (version 18 or newer).
• Valid, appropriately scoped Honeycomb API access credentials.

Installation Procedures

To install the utility globally for command-line use:

bash

Global Installation

npm install -g @kajirita2002/honeycomb-mcp-server

Immediate execution via npx

npx @kajirita2002/honeycomb-mcp-server

Environment Configuration

API authentication relies on a specific environment variable:

bash

Configure API Key

export HONEYCOMB_API_KEY="your_super_secret_honeycomb_key_here"

MCP Configuration Manifest Example

When integrating this server into an overarching MCP environment configuration file (mcp_config.json), use the following structure:

"honeycomb": { "command": "npx", "args": ["-y", "@kajirita2002/honeycomb-mcp-server"], "env": { "HONEYCOMB_API_KEY": "your_honeycomb_api_key_from_env" } }

Launching the Service

Start the server process:

bash

Initiate server runtime

npm start

Exposed Toolset Reference

This server exposes the following specialized functions for AI interaction:

Security & Handshake

1. honeycomb_auth
2. Verifies the connectivity and legitimacy of the configured Honeycomb API key.
3. Parameters: None (credentials inferred from environment).

Data Structure Management

1. honeycomb_datasets_list
2. Retrieves a catalog of all accessible data containers within the linked Honeycomb account.

3. Parameters: None.

4. honeycomb_dataset_get

5. Fetches granular metadata pertaining to a specific data repository.
6. Input:
   • datasetSlug (string, MANDATORY): The unique identifier (slug) for the target dataset.

Field Schema Inventory

1. honeycomb_columns_list
2. Enumerates the metadata for all observed fields within a dataset, supporting optional search criteria.
3. Input:
   • datasetSlug (string, MANDATORY): Identifier for the dataset.
   • key_name (string, OPTIONAL): A partial or exact match filter for field names.

Query Definition and Execution

1. honeycomb_query_create
2. Constructs and persists a new analytical query definition against a dataset.

3. Input:

   • datasetSlug (string, MANDATORY): Target dataset identifier.
   • query (object, MANDATORY): Comprehensive definition object detailing aggregations, temporal scope, and filtering predicates.

4. honeycomb_query_get

5. Retrieves the saved configuration details for a previously defined analytical query.

6. Input:

   • datasetSlug (string, MANDATORY): Target dataset identifier.
   • queryId (string, MANDATORY): The unique identifier assigned to the saved query.

7. honeycomb_query_result_create

8. Executes a specified query against Honeycomb and initiates the result calculation process (runs the saved query).

9. Input:

   • datasetSlug (string, MANDATORY): Target dataset identifier.
   • queryId (string, MANDATORY): Identifier of the query to execute.
   • disable_series (boolean, OPTIONAL): Flag to suppress time-series component output.
   • disable_total_by_aggregate (boolean, OPTIONAL): Flag to suppress overall aggregate values.
   • disable_other_by_aggregate (boolean, OPTIONAL): Flag to suppress 'other' category aggregates.
   • limit (integer, OPTIONAL): Constraint on the maximum number of resulting data points.

10. honeycomb_query_result_get

11. Fetches the finalized computational output for a query execution that has already completed.
12. Input:
    • datasetSlug (string, MANDATORY): Target dataset identifier.
    • queryResultId (string, MANDATORY): The ID associated with the completed computation run.

Dataset Definition Retrieval

1. honeycomb_dataset_definitions_list
2. Provides a paginated listing of dataset metadata definitions.
3. Input:
   • page (number, OPTIONAL): The requested page number (1-indexed).
   • limit (number, OPTIONAL): Count of records per page (default: 100; cap: 1000).
   • sort_by (string, OPTIONAL): Attribute name used for ordering results (e.g., 'creation_date', 'name').
   • sort_order (string, OPTIONAL): Direction of sorting ('asc' or 'desc').

Visualization & Dashboard Management

1. honeycomb_boards_list
2. Fetches a complete inventory of existing operational dashboards.

3. Parameters: None required.

4. honeycomb_board_get

5. Retrieves the detailed configuration and content of a specific visualization dashboard.
6. Input:
   • boardId (string, MANDATORY): The unique identifier for the dashboard artifact.

Operational Dialogue Examples

Illustrating how Claude utilizes this server to survey data sources and execute an analytical computation:

[AI Assistant] Querying the Honeycomb environment status: What data structures are currently available?

[Operator Input] I need an inventory of all logged data repositories.

[AI Assistant] Executing resource enumeration via honeycomb_datasets_list...

Observed Data Structures: - High-Volume Production Telemetry (slug: prod-telemetry) - Sandbox Environment Traces (slug: sandbox-traces) - Critical Application Streams (slug: app-streams)

Which data repository warrants deeper inspection?

[Operator Input] Focus on the production telemetry structure.

[AI Assistant] Retrieving comprehensive metadata for the 'prod-telemetry' dataset using honeycomb_dataset_get (slug: prod-telemetry)...

Proceeding to formulate and dispatch a time-series analysis. I will define a calculation to measure the 95th percentile latency across the last 4 hours.

This step involves invoking honeycomb_query_create followed by honeycomb_query_result_create...

Analysis Output Summary (Simulated): [Visualization description of P95 latency distribution over time]

Demonstrating Data Ingestion (Event Creation):

[AI Assistant] I can also inject a test event into the application-streams dataset.

[AI Assistant] Invoking honeycomb_event_create with payload:

Input: { "datasetSlug": "application-streams", "data": { "system_component": "auth-service", "latency_microsec": 12500, "http_response_code": 201, "api_path": "/v1/user/register", "verb": "POST" } }

Telemetry record successfully committed to the 'application-streams' repository!

[AI Assistant] To analyze authentication throughput, I will now define a metric aggregation query.

[AI Assistant] Utilizing honeycomb_query_create for metric calculation:

Input: { "datasetSlug": "application-streams", "query": { "calculations": [ { "op": "COUNT_DISTINCT", "column": "user_id" } ], "breakdowns": ["api_path"], "time_range": 7200, / Two hours / "filters": [ { "column": "http_response_code", "operator": "=", "value": 201 } ] } }

Standardized Workflow Scenarios

1. Establishing Service Level Compliance (SLO Automation)

[Operator Input] Configure a new Service Level Objective focusing on API uptime.

[AI Assistant] Acknowledged. Deploying an objective to guarantee 99.9% availability for the core authentication endpoint over a 90-day cycle.

I will utilize honeycomb_slo_create with the following parameters: { "datasetSlug": "production-api", "name": "Auth Endpoint Uptime Goal", "description": "Maintain 99.9% successful request rate.", "sli": { "alias": "Success Ratio > 0.999", "conditions": { "column": "status_code", "operator": "=", "value": 200 } }, "time_period_days": 90, "target_per_million": 999000 }

1. Deep Dive Incident Analysis

[Operator Input] Production latency spiked 15 minutes ago. Investigate by querying recent service failures.

[AI Assistant] Initiating rapid forensic analysis on the recent latency increase. I will aggregate error occurrences.

Executing initial data scan using honeycomb_query_result_create: { "datasetSlug": "production", "query": { "calculations": [ { "op": "SUM", "column": "request_size_bytes" } ], "filters": [ { "column": "error_flag", "operator": "IS", "value": true } ], "breakdowns": ["service_name", "region"], "time_range": 900, / Last 15 minutes / "granularity": 30 / 30-second buckets / } }

Error Reporting Structure

In the event of an operational failure, the server returns diagnostic information conforming to this standardized JSON format:

{ "error": { "message": "A detailed explanation of the failure scenario.", "code": "CONCISE_ERROR_IDENTIFIER" } }

Canonical Error Codes

• AUTH_FAILURE: Credentials rejected or insufficient permissions.
• RESOURCE_MISSING: The referenced entity (dataset, query ID) could not be located.
• INPUT_VALIDATION_FAIL: A provided argument violates schema requirements.
• THROTTLING_EXCEEDED: Honeycomb API rate limitation has been triggered.
• SERVICE_INTERNAL_FAULT: Unhandled exception within the MCP server process.

Remediation Guidelines

1. Authentication Failures
2. Verify the environment variable HONEYCOMB_API_KEY integrity.

3. Confirm the key's scope permits all intended API interactions.

4. Resource Not Found

5. Double-check the spelling and casing of the datasetSlug.

6. Confirm the target resource is active within the Honeycomb tenant.

7. Query Execution Failures

8. Scrutinize the structure of provided calculation and filter objects.
9. Validate that all referenced field names exist in the specified dataset schema.

Collaboration Guidelines

We welcome external contributions to enhance this MCP intermediary. Please follow these steps for involvement:

Local Development Setup

1. Branch the primary repository.

2. Clone your personal fork locally. bash git clone https://github.com/your-username/honeycomb-mcp-server.git

3. Install project dependencies. bash npm install

4. Implement required modifications.

5. Compile the source code. bash npm run build

6. Perform functional validation.

Contribution Submission Flow

1. Isolate new work within a dedicated feature branch. bash git checkout -b feature/new-functionality-name

2. Commit changes adhering strictly to the Conventional Commits specification. bash git commit -m "feat: introduce X functionality"

3. Push the branch to your remote fork. bash git push origin feature/new-functionality-name

4. Submit a formal Pull Request against the main repository.

Coding Standards Adherence

• Primary implementation language must be TypeScript.
• Maintain consistency with existing stylistic patterns.
• Document all externally facing API methods with clear descriptions.
• Ensure comprehensive unit and integration tests accompany new feature additions.

Licensing

This software is distributed under the terms of the MIT License. See the accompanying LICENSE file for full legal details.

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