Skip to main content
Glama

Osha

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

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint as true, and destructiveHint false. The description adds key behavioral context: it performs API calls to external LLMs, requires the user to bring their own Anthropic key for that model, and implies direct payment to Anthropic. No contradictions.

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

Conciseness4/5

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

The description is a single paragraph that is information-dense but still readable. It front-loads the main action (probe and score) and provides examples. Could benefit from slight restructuring (e.g., separate return format), but remains efficient.

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?

No output schema exists, but the description explicitly outlines the return structure: per-model {score, confidence, signals, raw_response} plus combined view. Parameter semantics are fully addressed. The description is complete for the tool's complexity and annotations.

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 meaningful context beyond schema: explains entity examples, clarifies context usage, and notes default model and key requirement. Slightly above baseline.

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 probes LLMs for entity knowledge and returns a visibility score (0-100) per model. It specifies default model and optional Anthropic with BYO key. This differentiates it from sibling tools, which do not focus on LLM probing for brand visibility.

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 suggests use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It provides guidance on when to provide an API key for Anthropic probing. However, it does not explicitly state when not to use or mention alternative tools.

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

All Osha toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation3/5

Several tools have similar purposes, such as the four ask_pipeworx variants and multiple prediction market analysis tools (bet_research, polymarket_edges, polymarket_arbitrage, etc.). While descriptions clarify differences, the overlap could cause misselection by an agent, especially with the high number of specialized market tools.

Naming Consistency4/5

All tool names use snake_case, but the pattern is not fully consistent: some start with verbs (ask_pipeworx, bet_research, compare_entities) while others are noun phrases (entity_profile, recent_alerts, osha_search). This minor inconsistency does not severely hinder readability.

Tool Count3/5

33 tools is on the high side, but the server acts as a comprehensive data gateway covering multiple domains (financials, prediction markets, OSHA, etc.) and includes meta-tools (memory, subscriptions, feedback). The count is justified by the breadth, though it borders on being overwhelming.

Completeness4/5

The tool set covers core workflows: data lookup (ask_pipeworx), entity profiles, comparisons, prediction market analysis, and memory management. There are minor gaps, such as no direct SEC filing retrieval tool (handled via ask_pipeworx), but the overall surface is comprehensive for the server's stated purpose.

Resources