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Entso E

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. Changed4 schema fields changed
    • addedInput schema / properties / _apiKey
      Added value: +{
+  "description": "Optional Anthropic API key (sk-ant-...) — only needed if \"anthropic\" is in models. Passed straight through to api.anthropic.com.",
+  "type": "string"
+}
    • changedInput schema / properties / context / description
      Previous value: -"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\", \"Polish painter\"). Helps disambiguate common names."New value: +"Optional: a phrase locating the entity (e.g. \"Boston restaurant\", \"B2B SaaS\"). Helps disambiguate common names."
    • changedInput schema / properties / entity / description
      Previous value: -"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\", \"the company behind ChatGPT\"."New value: +"The thing to ask about. Brand/business name, product name, person, or topic. E.g. \"Pipeworx\", \"OpenInvoice\", \"Acme Corp pricing\"."
    • addedInput schema / properties / models
      Added value: +{
+  "description": "Which models to probe. Supported: \"workers-ai\" (free default), \"anthropic\" (requires _apiKey). Omit for just workers-ai.",
+  "items": {
+    "type": "string"
+  },
+  "type": "array"
+}
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. Description adds that the tool makes external API calls (Anthropic) when a key is provided, and returns per-model data. No contradictions; however, rate limits or cost implications beyond BYO key are not mentioned.

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 sentences deliver essential information without waste. Front-loaded with action and output, followed by supporting details. No redundant phrases.

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 4 parameters and no output schema, the description adequately covers return structure ('per-model {score, confidence, signals, raw_response} + combined view'). Could specify that output is JSON, but overall sufficient for a read-only information tool.

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. Description adds meaningful context: explains the default model, that _apiKey enables Anthropic queries, and that context helps disambiguate entities. This goes beyond the schema's property 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?

Description uses specific verb 'probe' and resource 'LLMs' to define the tool's action: scoring visibility (0-100) per model for a business/brand/product/topic. It clearly distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on visibility scoring rather than broader scanning.

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?

Explicit use cases are provided: AI-marketing audits, pre-launch brand checks, competitive monitoring. It specifies default model and requirement of BYO API key for Anthropic. Though no explicit when-not-to-use, the context is clear enough for an agent to decide relevance.

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

B3.1/5.0
Disambiguation2/5

Several tools have overlapping or duplicate roles: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and deep_research/ask_pipeworx/discover_tools/suggest_questions all serve query routing. The five ENTSO-E tools are distinct but are lost among the unrelated Pipeworx/prediction-market tooling.

Naming Consistency2/5

Naming mixes single verbs (remember, forget, subscribe), noun phrases (actual_load, entity_profile), verb_noun patterns (compare_entities, discover_tools), and brand-prefixed groups (pipeworx_*, polymarket_*). Snake_case is consistent, but the verb style and naming logic vary widely with no discernible overall pattern.

Tool Count2/5

36 tools is too many for a server supposedly focused on ENTSO-E electricity data, especially since only 5 tools serve that domain. Even as a general data-access server, the set is heavy and includes redundant/beta variants (ask_pipeworx_beta, ask_pipeworx_grounded) that inflate the count.

Completeness1/5

The server name 'Entso E' implies electricity-market data, but only 5 of 36 tools cover generation, load, prices, capacity, and cross-border flow. Missing typical ENTSO-E operations like forecasts, balancing, or real-time grid status, while the remaining 31 tools belong to an unrelated data platform — severely incomplete for the advertised purpose.

Resources