Skip to main content
Glama

Mastodon

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint true and destructiveHint false. The description adds valuable behavioral context: default model is Workers AI Llama (free), BYO key for Anthropic, and per-model return structure (score, confidence, signals, raw_response) plus combined view. 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?

The description is concise and well-structured: one sentence for purpose, one for defaults and optional parameters, one for return format, and one for 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.

Completeness5/5

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

Given no output schema, the description adequately explains the return format (per-model and combined view). All 4 parameters are covered, and the use cases are clear. No additional context needed for a tool of this complexity.

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 parameters are already documented. The description adds meaning beyond schema by explaining default model behavior (workers-ai used by default), when to provide `_apiKey` (if 'anthropic' in models), and the purpose of `context` (disambiguation).

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's purpose: probing LLMs for knowledge about a business/brand/product/topic and scoring visibility (0-100). It uses specific verbs ('probe', 'score') and distinguishes itself from sibling tools by focusing on multi-model AI visibility 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 explains when to use the tool (AI-marketing audits, pre-launch brand checks, competitive monitoring) and how to use optional parameters like `_apiKey` for Anthropic probes. However, it does not explicitly state when not to use it or name alternatives among siblings, leaving some implicit guidance.

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

All Mastodon 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

A3.6/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but there is some overlap between research-oriented tools like ask_pipeworx and deep_research, and between entity_profile and compare_entities. Descriptions help differentiate them, so overall an agent can tell them apart.

Naming Consistency2/5

Tool names are inconsistent, mixing snake_case (ask_pipeworx, list_subscriptions) and camelCase (bag_research, compare_entities). There is no uniform naming pattern, which can be confusing.

Tool Count2/5

With 35 tools, the set is too large for a server named 'Mastodon'. Only a few tools are actually Mastodon-related (e.g., get_account, get_timeline), while the majority are PipeWorx tools unrelated to the core purpose.

Completeness1/5

As a Mastodon server, the toolset is severely incomplete: it lacks basic social media operations like posting statuses, following/unfollowing, and engaging with content. The name misrepresents the actual capabilities.

Resources