Skip to main content
Glama

Idf Events

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?

Adds context beyond annotations: details on API key cost (BYO key, pay Anthropic), return structure (per-model {score, confidence, signals, raw_response}), and default model selection. 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?

Three dense sentences with no wasted words. Action verb first, followed by key details and use cases. Perfectly sized for quick agent comprehension.

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?

Covers all necessary aspects: purpose, parameters, return structure, use cases, and cost implications. No output schema, but description adequately explains expected results.

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 description enhances semantics: 'Omit for just workers-ai' for models, 'Passed straight through' for _apiKey, example for context. Adds value beyond schema.

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 uses specific verbs ('Probe', 'score') and identifies the resource (LLMs, visibility). It clearly distinguishes from sibling tools like 'ask_pipeworx' which are for domain-specific queries.

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?

Explicitly states use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and default model behavior. Lacks explicit when-not-to-use or direct alternatives, but context is sufficient.

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

All Idf Events 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

A3.9/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., three ask_pipeworx variants, multiple Polymarket analysis tools, and several entity-focused tools). Agents may struggle to select the correct tool for tasks like querying data or analyzing prediction markets.

Naming Consistency4/5

All tool names use snake_case, and most follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities). A few names like ai_visibility_check are slightly less conventional, but overall the naming is consistent.

Tool Count3/5

With 32 tools covering a broad range of data services, the count is on the high side but still manageable. However, the server name 'Idf Events' is misleading, as only one tool relates to events in Paris.

Completeness4/5

The tool set covers core workflows for the Pipeworx platform: data querying, research, comparisons, subscriptions, memory, and feedback. Minor gaps exist, but most user needs are addressed.

Resources