Skip to main content
Glama

The Guardian

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

A4.4/5.0
Behavior4/5

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

Annotations (readOnlyHint, idempotentHint) are consistent. The description adds behavioral details: default model, BYO key for Anthropic, billing implications, return structure (score, confidence, signals, raw_response). This adds context beyond 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 two sentences, front-loaded with key action, and contains no superfluous words. It efficiently conveys purpose, behavior, and use cases.

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 no output schema, the description adequately explains return fields and use cases. It lacks details on error handling or rate limits but is sufficient for this tool's 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 coverage is 100%, but the description enhances parameter understanding: it explains 'entity' as the thing to ask about, 'models' as model selection with defaults, '_apiKey' as optional pass-through, and 'context' for disambiguation. This adds value beyond 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 clearly states the tool's function: probing LLMs for knowledge about an entity and scoring visibility. It uses specific verbs ('probe', 'score') and resources (LLMs, entity), and distinguishes from siblings like 'ask_pipeworx' which are for general Q&A.

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') and explains when to use the API key for Anthropic. It implicitly differentiates from sibling tools but does not explicitly list when not to use this tool.

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

All The Guardian 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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping purposes and similar names, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and the Polymarket tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, etc.). The Guardian-specific tools are distinct but are outnumbered by these confusing clusters, making it hard for an agent to reliably select the right tool.

Naming Consistency1/5

Tool names follow no consistent pattern: some are snake_case (ai_visibility_check, ask_pipeworx), some are verbs without objects (item, tags, forget, recall), and some are inconsistent in style (compare_entities vs. bet_research). There is no unifying naming convention across the set.

Tool Count2/5

With 36 tools, the server is bloated for its implied purpose ('The Guardian' suggests a focused news outlet). Many tools belong to unrelated domains (Pipeworx data platform, Polymarket prediction markets), making the count feel excessive and unfocused. A news-specific server should have far fewer tools.

Completeness1/5

The tool set lacks a coherent domain. The Guardian news tools are complete (search, item, sections, etc.), but the massive inclusion of Pipeworx and Polymarket tools creates dead ends and gaps (e.g., no direct tool to list all prediction markets or search patents). The surface feels like a random collection rather than a designed whole.

Resources