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Reddit

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

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds transparency by clarifying the default model (Workers AI, free), the cost implication of using Anthropic (BYO key), and the return structure (per-model data + combined view). 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.

Conciseness5/5

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

The description is concise (3 sentences) yet packed with essential information: core function, parameters, return format, and use cases. No superfluous words.

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?

Given the 4 parameters (1 required) and no output schema, the description provides sufficient context: explains return format, use cases, and parameter roles. The input schema's descriptions are also comprehensive, making the tool fully understandable.

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 has 100% coverage with descriptions. The tool description adds context beyond the schema, such as the default model being free and the purpose of the _apiKey parameter, enhancing understanding of cost and usage.

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 precisely states the tool probes LLMs for brand visibility and scores it (0-100) per model, with specific use cases like AI-marketing audits and competitive monitoring. It clearly distinguishes from sibling tools by its unique function.

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 explains when to use the tool (AI-marketing audits, pre-launch checks, competitive monitoring) and how to use optional parameters (apiKey for Anthropic, context for disambiguation). However, it does not explicitly state when not to use it or mention alternative tools.

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

A3.5/5.0
Disambiguation2/5

The set has several near-duplicate tools: ask_pipeworx and ask_pipeworx_beta are explicitly designed to be identical right now, and ask_pipeworx_grounded, deep_research, and bet_research all overlap on routed data lookup. The three Reddit tools are distinct, but they are dwarfed by a large cluster of unrelated Pipeworx/Polymarket tools, making the actual purpose of the set hard to pin down.

Naming Consistency2/5

Naming conventions are wildly mixed: some tools use verb_noun (get_post, subscribe, suggest_questions), some use noun phrases (entity_profile, recent_alerts, pipeworx_trending), and some use ad-hoc names (ask_pipeworx, bet_research, scan_dependency). Within sub-families (polymarket_*, ask_pipeworx_*) names are consistent, but overall there is no single predictable pattern.

Tool Count1/5

34 tools is excessive for a server named 'Reddit' when only 3 of them (get_post, get_subreddit, search_posts) actually relate to Reddit. The remaining 31 tools cover unrelated domains like Pipeworx data routing, Polymarket betting, memory management, and npm dependency scanning—an extreme scope mismatch for the advertised server name.

Completeness1/5

The Reddit surface is severely incomplete: it offers read-only post/subreddit/search functionality with no create, edit, delete, vote, comment, or user-profile operations. Conversely, the Pipeworx tooling is over-complete relative to the 'Reddit' name, covering data lookups, prediction markets, subscriptions, and memory—none of which belong in a Reddit server.

Resources