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Glama

Metabolights

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

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds context beyond annotations: it explains the default free model, that Anthropic requires a BYO key, and the return structure per model (score, confidence, signals, raw_response). 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.

Conciseness5/5

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

Two concise paragraphs front-load the core purpose and output, then detail parameters and use cases. Every sentence adds value without redundancy or filler.

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?

The description is complete for a tool with no output schema: it explains the per-model return fields and combined view, covers all parameters (required and optional), and gives clear usage context. No important gaps.

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%, but the description adds value by specifying the default model (Workers AI Llama-3.3-70b free), that _apiKey is passed through to Anthropic, and that context helps disambiguate entities. This enriches the schema descriptions.

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 explicitly states the verb (probe), resource (LLMs for business/brand/product/topic), and outputs a visibility score per model. It distinguishes from sibling tools like scan_competitor_ai_presence by focusing on scoring (0-100) and use cases like AI-marketing audits.

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 lists specific use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to provide optional parameters (context, _apiKey). However, it does not explicitly exclude itself from similar sibling tools or state when not to use it.

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

B3.3/5.0
Disambiguation1/5

The vast majority of tools (e.g., ask_pipeworx, ask_pipeworx_grounded, bet_research, polymarket_arbitrage, etc.) are unrelated to Metabolights and overlap heavily with each other in purpose. Only two tools (get_study and search_studies) are clearly distinct and relevant to the server's stated domain.

Naming Consistency2/5

Tool names are highly inconsistent, mixing snake_case (get_study, search_studies), camelCase (ask_pipeworx), and varied verb styles (e.g., validate_claim vs bet_research). No consistent pattern across the set.

Tool Count1/5

With 28 tools, the server is extremely overpopulated for its stated purpose (Metabolights). Only 2 tools actually pertain to metabolomics study access, while the remaining 26 are general-purpose or unrelated (e.g., bet_research, polymarket_edges), making the count highly inappropriate.

Completeness1/5

The Metabolights domain severely lacks completeness: only search_studies and get_study are provided, with no create, update, delete, or upload functionality. The server fails to cover essential operations for its supposed domain.

Resources