Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday).
| Name | Required | Description | Default |
|---|---|---|---|
| expression | Yes | Cron expression (e.g., "0 9 * * 1-5", "*/15 * * * *") |
| Name | Required | Description | Default |
|---|---|---|---|
| fields | No | ||
| expression | No | ||
| human_readable | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds behavioral context by specifying support for standard 5-field cron (minute hour day month weekday), which is useful beyond the annotations. It does not cover error behavior, but the output schema likely covers that.
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 primary purpose, and no wasted words. Every part of the description contributes useful information.
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 simple with one parameter and an output schema present. The description is sufficiently complete for an agent to understand when and how to use it, and the format clarification covers the key edge case.
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 parameter description and examples. The description adds semantic value by explaining the exact format and field order, which goes beyond the schema's simple 'Cron expression' label.
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 ('Parse a cron expression') and a clear outcome ('into a human-readable schedule description'). It distinguishes itself from the sibling cron_validator by focusing on producing a description rather than validating.
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 the tool is for getting human-readable schedules from cron expressions, which gives clear context. However, it does not explicitly mention alternatives or when not to use it (e.g., if validation is needed, use cron_validator), so it misses the explicit exclusion criterion for a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.
The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.
With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.
The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.