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Bhagavad Gita

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds meaningful behavioral detail: default model, fee implications, API key handling, and return format (score, confidence, signals, raw_response per model). This fully informs the agent of tool behavior.

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?

The description is two well-structured sentences with no extraneous information. It front-loads the core function and then adds necessary details, earning its place efficiently.

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?

For a 4-parameter tool with no output schema, the description covers all necessary aspects: what it does, models, API key, context use, and return structure. It is complete and self-sufficient.

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 description coverage is 100%, so baseline is 3. The description adds value by explaining defaults for 'models', the purpose of '_apiKey', and the optional 'context' for disambiguation, exceeding the schema alone.

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 uses a specific verb 'probe' and resource 'LLMs', clearly defining the action of scoring AI visibility. It distinguishes well from sibling tools like ask_pipeworx or scan_competitor_ai_presence by focusing on visibility 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 clear usage contexts such as 'AI-marketing audits, pre-launch brand checks, competitive monitoring' and explains model defaults and API key requirements. However, it does not explicitly state when not to use it or suggest alternatives among siblings.

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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TDQS

A3.6/5.0
Disambiguation4/5

Individual tools have detailed descriptions and mostly distinct purposes, but the mix of domains (scripture, finance, data lookup) could confuse an agent about which tool to choose for a given task. However, within each subdomain, tools are clearly differentiated.

Naming Consistency2/5

Tool names follow no consistent pattern: some are verb_noun (ask_pipeworx, compare_entities), some are noun_verb (entity_profile, recent_changes), and some are single verbs (forget, recall). The mix of snake_case with varied starting parts makes naming unpredictable.

Tool Count1/5

With 23 tools but only 3 related to the server's stated purpose (Bhagavad Gita), the tool count is severely mismatched. The vast majority belong to data analytics and finance, making the set feel bloated and mis-scoped.

Completeness1/5

For a Bhagavad Gita server, only list_chapters, get_chapter, and get_verse are provided, missing obvious features like search, commentary comparison, or multiple translations. The other tools are irrelevant to the domain, leaving it extremely incomplete.

Resources