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Glama

Corporate Apology

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. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable behavioral context: default model (Workers AI Llama-3.3-70b free), Anthropic BYO-key with direct payment, and the per-model return structure. No contradiction 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 three sentences, front-loaded with the primary purpose, followed by operational details (default/cost) and return format. Every sentence adds value with no filler, making it concise and well-structured.

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 there is no output schema, the description compensates by specifying return fields: {score, confidence, signals, raw_response} + a combined view. It covers use cases and key behavior. Missing details like error handling or rate limits, but for a read-only tool with strong annotations, it is reasonably complete.

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 meaning beyond the schema by explaining that omitting models uses the free default, and that _apiKey is passed through to Anthropic with the user paying directly. This goes beyond the schema's field descriptions.

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

Purpose4/5

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

The description clearly states the tool's action: 'Probe one or more LLMs for what they know about a business / brand / product / topic' and score visibility 0-100 per model. It uses a specific verb ('probe') and resource ('LLMs'), and the unique output (visibility score) distinguishes it from siblings like ask_pipeworx or entity_profile, though it does not explicitly name alternatives.

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 explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives clear when-to-use context, but it does not explicitly state when not to use this tool or name alternative sibling tools, so it falls short of a 5.

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

All Corporate Apology toolsInspect
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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions. Some overlap exists in the Polymarket-related tools, but each has a specific focus (arbitrage detection, edge scanning, persistence tracking, fill risk, cross-venue spread). The two ask_pipeworx variants are similar but differentiated by hallucination resistance.

Naming Consistency5/5

All tool names use lowercase with underscores, following a consistent pattern of verb_noun or noun_verb. Examples include 'ai_visibility_check', 'bet_research', 'entity_profile', and 'validate_claim'. There are no mixed conventions or erratic naming.

Tool Count2/5

The server is named 'Corporate Apology' but contains 31 tools, only one of which (corporate_apology_generate) relates to apologies. The vast majority are unrelated data retrieval and analysis tools, making the count excessive and poorly scoped for the server's stated purpose.

Completeness1/5

For a server focused on corporate apologies, the only tool is corporate_apology_generate. There are no tools for analyzing apology impact, managing crisis response, or tracking apologies. The tool surface is severely incomplete relative to the server's name.

Resources