Ranks the best AI tool or API per task: transcription, TTS, web search, scraping and OCR.
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Average 4.3/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly stated and distinct.
The single tool name 'find_best_tool' follows a verb_noun pattern, which is consistent and intuitive. With no other tools, consistency is trivially maintained.
The server has exactly one tool, which feels thin for a general-purpose toolkit. However, the tool is comprehensive and serves a focused purpose of recommending AI tools, so the count is borderline acceptable.
The tool covers the entire recommendation workflow, including category detection, provider comparisons, pricing, sources, and confidence labeling. For the stated domain, there are no obvious missing operations.
find_best_toolAInspectGiven a natural-language question about which AI tool or API is best for a task (currently transcription, text-to-speech, web search, scraping & browser and ocr & document extraction), return Syftly's ranked recommendation: a citeable summary, a provider table with prices and trade-offs, dated sources, and a confidence label. Optionally pass "category" to disambiguate; otherwise it is detected from the question.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The question in natural language, e.g. 'best transcription API for Dutch'. | |
| category | No | Optional category id to disambiguate the question; omit to let Syftly detect it. |
| Name | Required | Description |
|---|---|---|
| slug | Yes | |
| query | Yes | The human question (page H1). |
| routing | Yes | Honest routing outcome: 'matched' = answered from a real category; 'none' = no category matched (out-of-scope/gibberish) — an honest no-match, not a fabricated answer; 'ambiguous' = fit 2+ categories. 'none'/'ambiguous' carry `categories` and no real recommendation/providers. |
| sources | Yes | |
| summary | Yes | Citeable summary, 40-80 words, reused verbatim across all views. On a no-match, the plain-language message. |
| updated | Yes | ISO date. |
| category | Yes | e.g. 'transcription'; '' on a no-match. |
| providers | Yes | |
| categories | No | Present only when routing !== 'matched': the supported categories so the caller can re-ask in scope. |
| confidence | Yes | Confidence/depth label. |
| recommendation | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses what the tool returns (citeable summary, provider table, dated sources, confidence label), indicates that category detection is automatic, and lists supported categories. It does not mention rate limits or auth, but these are less critical for a read-only recommendation function.
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 two sentences long, front-loaded with the core purpose. The first sentence packs the action, supported categories, and output structure into a clear flow; the second covers the optional parameter. There is no wasted wording.
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 description is comprehensive for a tool with an output schema. It covers input semantics, category list, optional disambiguation, and expected output components. No additional context is needed for an agent to decide when and how to invoke it.
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 schema already describes 'query' as a natural-language question and 'category' as an optional disambiguator. The description repeats this information without adding new semantic details, so it does not exceed the baseline for full schema coverage.
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 the tool's purpose: given a natural-language question about which AI tool or API is best for a task, it returns a ranked recommendation. It lists specific supported task categories, making the scope concrete. The verb 'return' and resource 'Syftly's ranked recommendation' are explicit, and there are no siblings to confuse it with.
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 states when to use it: 'Given a natural-language question about which AI tool or API is best for a task' and lists the allowed domains. It also provides guidance on the optional 'category' parameter for disambiguation. However, it does not mention when not to use it or alternatives, though no siblings exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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