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Commons Wikimedia

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

A4.3/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, consistent with a probing tool. The description adds behavioral context: it mentions default model, note about Anthropic API key being passed directly (BYO key), and the return structure. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph of about 4 sentences, efficiently conveying purpose, defaults, options, and return format. It is front-loaded with the core action and score range, followed by details and use cases. No 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?

Given no output schema, the description explains the return structure (per-model score, confidence, signals, raw_response, combined view). All 4 parameters are described and contextualized. The complexity of multiple models and optional API key is fully addressed. The tool seems adequately described for an agent.

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 the schema already describes parameters. The description adds valuable context: clarifies 'entity' as the thing to ask about, 'models' default to workers-ai, '_apiKey' is passed through directly to Anthropic, and 'context' helps disambiguate. This goes beyond the schema's bare 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 specifies the tool probes LLMs for knowledge about a business/brand/product/topic and returns a visibility score (0-100) per model. It clearly distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on LLM visibility rather than general scanning, and from 'ask_pipeworx' variants by providing a structured score.

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 explicitly lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also specifies when to provide an API key for Anthropic. However, it does not explicitly state when NOT to use this tool or suggest alternative sibling tools for different scenarios.

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

C2.9/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers, and the six polymarket_* tools plus bet_research all cover prediction-market analysis with blurry boundaries. The server name 'Commons Wikimedia' also clashes with 30+ Pipeworx tools, making the overall purpose ambiguous. Only the handful of Commons-specific tools (category_members, file_info, file_revisions, random_image, search) are clearly distinct.

Naming Consistency3/5

All names are lowercase snake_case and many follow a noun_phrase pattern (entity_profile, polymarket_edges, recent_changes), but verb styles are inconsistent: some are bare verbs (search, recall, subscribe), some are verb_noun (generate_llms_txt, resolve_entity, validate_claim), and several are noun-only (category_members, file_info, pipeworx_feedback). The style is readable but not predictable, mixing action-first and object-first conventions.

Tool Count2/5

36 tools is far too many for a server ostensibly named 'Commons Wikimedia' — the vast majority belong to the Pipeworx data platform, not Wikimedia Commons. The count exceeds the 25-tool threshold for 'too many,' and the scope mismatch between the server name and the actual toolset makes the abundance feel even more unjustified.

Completeness2/5

For 'Commons Wikimedia,' the surface is severely incomplete: there is no upload, no category tree navigation, no file download, and no structured search beyond full-text. For the Pipeworx domain, coverage is broad but indirect — most data access funnels through aggregate/meta tools (ask_pipeworx, entity_profile, deep_research) rather than direct per-source tools, leaving gaps for granular lookups and leaving the Commons tools stranded with no real integration.

Resources