Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — use this to avoid bounces and malformed data.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to validate |
| Name | Required | Description | Default |
|---|---|---|---|
| No | |||
| valid | No | ||
| reason | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value beyond annotations by revealing the return shape `{ valid, email }` and by scoping the check to RFC 5322 syntax (vs. deliverability or MX-lookup), which sets accurate expectations about the tool's boundary. No contradiction with annotations.
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?
Three tight sentences that each earn their place: the standard and use cases, the return shape as a code snippet, and the purpose. Front-loaded with the imperative verb 'Validate.' 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?
For a low-complexity tool with 1 trivially-named parameter, 100% schema coverage, rich annotations, and an output schema, the description fully covers purpose, timing, return contract, and value. Nothing material is left undisclosed.
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?
With 100% schema coverage on a single self-evident parameter, the baseline is 3. The description adds marginal but real value by clarifying that 'validate' means syntactic RFC 5322 conformance and by disclosing the tool's output contract, which tells the agent what the single parameter gets checked against.
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?
Uses a specific verb+resource+standard ('Validate an email address against RFC 5322 syntax') and grounds it in concrete use cases (storing, sending transactional email, mailing list). The RFC citation and email domain clearly distinguish it from siblings like validate_url, validate_agent_trajectory, and validate_mcp_response.
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?
Clearly states when to use the tool with three explicit scenarios ('before storing it, sending a transactional email, or adding it to a mailing list') and the intended outcome ('avoid bounces and malformed data'). However, it names no alternatives or when-not-to-use scenarios, despite validation siblings existing in the tool list.
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.