Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | URL-encoded string to decode |
| Name | Required | Description | Default |
|---|---|---|---|
| decoded | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this is read-only, idempotent, and non-destructive, so the description's safety burden is lowered. The description adds the behavior of converting percent-encoded input to plain text and the intended use context, but it does not disclose edge-case behaviors like handling of malformed input, plus signs, or non-UTF-8 encodings.
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?
The description is extremely concise, using just two sentences that state the purpose and usage context without any filler or redundant details. Every phrase earns its place.
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 tool's low complexity (one parameter), full schema coverage, presence of an output schema, and comprehensive annotations, the description is complete. It covers the core function and primary use cases without needing to explain return values, which are presumably defined in the output schema.
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 for the single parameter is 100%, and the schema description already says 'URL-encoded string to decode.' The description adds the qualifier 'percent-encoded' and clarifies the output is 'plain text,' which is marginal value but not substantial 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 strong verb phrase, 'Decode a percent-encoded URL string back to plain text,' which clearly names the specific operation and resource. It distinguishes itself from sibling tools like url_encode, base64_decode, and unescape_html by explicitly citing percent-encoding.
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 gives concrete use cases: 'Use when parsing query parameters from raw URLs or when displaying encoded values to users.' This provides clear context for when to use the tool, though it does not mention explicit when-not-to-use scenarios or alternative tools, so it falls short of a perfect 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.