Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | URL to validate and parse |
| Name | Required | Description | Default |
|---|---|---|---|
| full | No | ||
| hash | No | ||
| port | No | ||
| valid | No | ||
| origin | No | ||
| search | No | ||
| hostname | No | ||
| pathname | No | ||
| protocol | No | ||
| query_params | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds the return component breakdown but does not discuss edge cases like invalid URLs or normalization behavior, which would add extra value beyond 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?
Two short, front-loaded sentences cover purpose, validation aspect, and return contents. Every word earns its place with no redundancy or fluff.
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?
Given the simple one-parameter signature, existing output schema, and full annotations, the description is sufficient for an agent to select and invoke the tool correctly. It even mentions the return structure, making the tool's behavior immediately clear.
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 description coverage is 100% (the parameter is already described as 'URL to validate and parse'). The description essentially restates the input purpose and does not add format or behavior details beyond what the schema provides.
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 uses a specific verb phrase 'Parse and validate a URL' and clearly identifies the resource and output (decomposed components). This distinguishes it from sibling tools like url_encode/url_decode or validate_email.
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?
It clearly implies use for parsing/validating URLs and describes what will be returned, giving clear context. However, it does not explicitly mention when not to use it or point to an alternative sibling tool.
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.