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Jisho

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate idempotent, read-only, non-destructive behavior. The description adds value by explaining the default model, optional Anthropic probing with BYO key and cost implications, and the return structure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences that front-load the main action and then add important details and use cases without any fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description thoroughly explains the return format (per-model score, confidence, signals, raw_response + combined view). It also covers model options, cost, and typical use cases. No gaps identified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds extra context like default model being free, `_apiKey` being passed straight through, and `context` helping disambiguation, which enhances parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: probing LLMs for knowledge about an entity and scoring visibility per model. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on per-model scoring.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage context (AI-marketing audits, pre-launch checks, competitive monitoring) but does not explicitly mention when not to use it or name alternative tools for similar tasks.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Jisho toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Most tools have distinct purposes, but there are overlapping tools like the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and ai_visibility_check/scan_competitor_ai_presence. A few pairs could cause confusion, but overall differentiation is moderate.

Naming Consistency2/5

Tool names follow no consistent pattern: some are imperative verbs (remember, forget), some are compound nouns (entity_profile, polymarket_arbitrage), some are descriptive phrases (recent_alerts, scan_dependency). The verb_noun pattern is absent, leading to inconsistency.

Tool Count1/5

33 tools is far too many for a server named 'Jisho' (a Japanese dictionary), especially since only 2 tools (lookup, search_words) are dictionary-related. The majority of tools belong to an unrelated data platform, making the count wildly inappropriate for the server's apparent purpose.

Completeness3/5

For the dictionary domain, the server lacks common features like example sentences or kanji details. However, the extended tool set covers many data retrieval and analysis tasks, though it is read-heavy with no update/delete capabilities for most resources.

Resources