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Glama

Isbn

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable details: it probes LLMs, costs (free default, BYO for Anthropic), and output structure (per-model {score, confidence, signals, raw_response} + combined view). No contradictions with annotations.

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 concise (3-4 sentences) and front-loaded with the core action. Every sentence adds value: purpose, default model, key parameter effect, output format, and use cases. No redundant or wasted words.

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

Completeness4/5

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

Given 4 parameters, no output schema, and the complexity of probing multiple models, the description adequately covers the tool's capabilities: entity, models selection, API key for Anthropic, context disambiguation, and a summary of the return format. It could be slightly more detailed about the confidence and signals fields, but it's sufficient for an AI agent to use correctly.

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%, but the description adds meaning beyond the schema: it explains the default model (Workers AI Llama-3.3-70b), the effect of `_apiKey` (bypass to Anthropic with user's own key and cost), and the `context` parameter's role in disambiguation. This helps the agent understand parameter behavior.

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 verb ('probe'), the resource ('LLMs'), and the specific output ('visibility score 0-100 per model'). It differentiates the tool from siblings like 'scan_competitor_ai_presence' by specifying use cases like 'AI-marketing audits, pre-launch brand checks, competitive monitoring'.

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 (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use the optional `_apiKey` for Anthropic. It does not explicitly state when not to use this tool or contrast it directly with alternatives, but the listed contexts are sufficient for decision-making.

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

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (entity_profile, compare_entities, ask_pipeworx, etc.). However, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are very similar, and suggest_questions/discover_tools overlap slightly. Overall, tools are well-disambiguated with only minor ambiguity.

Naming Consistency3/5

All tool names use snake_case, but the pattern is inconsistent: some start with a verb (ask_, validate_), others with a noun (entity_, bet_). Several are noun_noun (entity_profile), and some are single verbs (remember). No consistent verb_noun pattern across the set.

Tool Count2/5

33 tools is excessive for a server named 'Isbn' that only has 2 ISBN-specific tools. The majority are from Pipeworx, creating scope mismatch. Multiple redundant tools (three ask_pipeworx variants, several polymarket tools) inflate the count unnecessarily.

Completeness2/5

For a server named 'Isbn', completeness is very low—only conversion and validation are provided; no metadata lookup or other ISBN-related features exist. The Pipeworx tools themselves are comprehensive, but they do not align with the server's implied domain.

Resources