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Vimeo

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 provide readOnlyHint, idempotentHint, etc. The description adds value by disclosing the default model (free), BYO key for Anthropic, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). It also hints at cost implications for Anthropic calls. No contradictions 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?

The description is two sentences long, front-loading the main purpose and return value. Every sentence provides essential information without redundancy. It is concise yet informative, earning its place with zero wasted words.

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?

With 4 parameters (1 required), 100% schema coverage, and no output schema, the description covers key aspects: return format, model options, optional context, and use cases. It lacks details on error handling or timeouts but is sufficient for a moderate-complexity tool. The mention of return structure compensates for missing output schema.

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 baseline is 3. The description adds context beyond the schema: it explains the entity scope ('business / brand / product / topic'), provides examples ('Pipeworx', 'OpenInvoice'), clarifies the default model behavior for the 'models' parameter, and explains the role of '_apiKey' and 'context'. This enriches parameter understanding.

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 probes LLMs for knowledge about an entity and returns a visibility score (0-100). It specifies the verb (probe, score), resource (LLMs, business/brand/product/topic), and outcome (score per model). This distinguishes it from siblings like 'deep_research' or 'scan_competitor_ai_presence' by focusing on LLM knowledge scoring.

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.' It also explains when to provide the optional _apiKey and models. However, it does not explicitly state when not to use this tool or compare it to alternatives, leaving some gaps for an agent to infer.

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 purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates, and the polymarket_* family has six tools with blurred boundaries. The Vimeo tools are distinct, but the massive unrelated Pipeworx set creates ambiguity about which tool is appropriate for a given task.

Naming Consistency2/5

Tool names mix conventions: Vimeo tools use bare nouns (video, channel, user), while Pipeworx tools use verb_noun (resolve_entity, validate_claim) or noun_verb (ai_visibility_check). Some names like ask_pipeworx and pipeworx_feedback do not follow a consistent verb-first pattern.

Tool Count2/5

40 tools is far too many for a Vimeo server; only 9 tools are Vimeo-related, and the remaining 31 are an unrelated Pipeworx data toolkit. This inflates the surface area and makes the set unwieldy.

Completeness2/5

The Vimeo surface covers read operations (search, get, list) but lacks any write operations like upload, update, or delete videos. It also misses common Vimeo features like comments, likes, or portfolio management, so common tasks would hit dead ends.

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