hny-mcp
Related Servers
Alternatives to hny-mcp
No user-submitted related servers found.
Related Servers
- AlicenseBqualityDmaintenanceServer for interacting with Meilisearch through LLM interfaces like Claude.22196MIT
- AlicenseBqualityDmaintenanceModel Context Protocol server for Langfuse observability. Query traces, analyze accuracy, detect failures, track costs, debug latency, manage prompts and datasets.582MIT
- AlicenseNot gradedqualityDmaintenanceEnables LLMs to interact with Google BigQuery by inspecting database schemas, listing tables, and executing SQL queries. This server facilitates seamless data analysis and management through natural language via the Model Context Protocol.MIT
- AlicenseNot gradedqualityDmaintenanceThis server enables Claude to directly interact with SQLite, SQL Server, PostgreSQL, and MySQL databases through the Model Context Protocol, allowing for query execution, table management, and data export capabilities.MIT
- AlicenseNot gradedqualityDmaintenanceA simple server that integrates with Claude to allow querying and manipulating Notion pages and databases through natural language prompts.2,184 npmMIT
- FlicenseNot gradedqualityDmaintenanceA Python server implementing the Model Context Protocol that exposes tools for querying external APIs, compatible with Claude Desktop and ChatGPT Desktop.-
TDQS
Scored across 14 tools
Most tools have distinct purposes targeting specific Honeycomb resources (e.g., get_board vs. get_slo vs. get_trigger), but analyze_columns overlaps somewhat with run_query as both involve data analysis. The descriptions help clarify that analyze_columns is for statistical summaries of columns while run_query is for general query execution, but an agent might still confuse them when seeking data insights.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., list_boards, get_slo, run_query). The verbs 'list', 'get', 'analyze', and 'run' are used predictably across the set, making it easy to understand the action each tool performs.
With 14 tools, this server is well-scoped for Honeycomb's observability domain, covering dashboards, SLOs, triggers, datasets, queries, and instrumentation guidance. Each tool has a clear role, and the count is typical for a comprehensive API surface without being overwhelming.
The tool set provides strong coverage for retrieving and listing Honeycomb resources (e.g., boards, SLOs, triggers, datasets) and includes key operations like run_query and analyze_columns. Minor gaps exist, such as no tools for creating or updating resources (e.g., create_board, update_slo), but agents can still perform many workflows with the available read-oriented tools.