Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injection and malformed URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | "component" (default) or "full" for encodeURI behavior | |
| input | Yes | String to URL-encode |
| Name | Required | Description | Default |
|---|---|---|---|
| encoded | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds rationale (prevent injection) but does not disclose deeper behavioral details like which characters are encoded, UTF-8 handling, or edge cases, though the mode parameter hints at encodeURI behavior.
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 core purpose, and every clause adds value. No fluff or repetition.
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 simplicity and the presence of an output schema, the description sufficiently covers purpose, usage, and safety. The mode distinction is left to the schema, which is acceptable. A brief example could push it to 5, but current coverage is solid.
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%, with both input and mode clearly described. The description adds usage context (e.g., what to encode) but does not introduce new parameter-level details beyond what the schema already provides. Baseline 3 is appropriate.
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 and resource: 'Percent-encode a string for safe use in URLs.' It clearly distinguishes from sibling encoding tools like base64_encode by specifying URL percent-encoding, and even lists common use cases (query strings, path segments, form-encoded bodies).
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 explicitly tells when to call the tool ('before programmatically building query strings, path segments, or form-encoded bodies') and why (to prevent injection and malformed URLs). It provides clear context but does not mention when not to use it or name alternatives like url_decode.
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.