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Base64

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

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds operational transparency: default model (Workers AI, free), BYO key for Anthropic, and per-model return structure (score, confidence, signals, raw_response). 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?

The description is three sentences, front-loading the primary purpose and output. Every sentence adds necessary information (what it does, default model, return values, use cases). No wasted 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 4 parameters, no output schema, and no nested objects, the description is highly complete. It explains return structure (per-model fields + combined view), default behavior, optional key usage, and typical use cases. The tool is well-differentiated among many siblings.

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 value by explaining the default model behavior and the API key usage ('passed straight through to api.anthropic.com'), which is not fully captured in schema descriptions. This helps agents understand parameter semantics beyond the 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 clearly identifies the resource (LLMs about a business/brand/product/topic) and the output (visibility score 0-100 per model). It effectively distinguishes itself from sibling tools like ask_pipeworx or compare_entities which serve different purposes.

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 clear usage context: AI-marketing audits, brand checks, competitive monitoring. It also explains when to use the _apiKey parameter (to probe Anthropic). However, it does not explicitly mention when not to use this tool or point to alternative sibling tools, though the context is sufficient.

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

A3.7/5.0
Disambiguation2/5

Several tool groups have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, polymarket_edges / polymarket_arbitrage / polymarket_edge_tracker / polymarket_fill_risk / polymarket_kalshi_spread / bet_research all push prediction-market opportunities and can be confused, and ai_visibility_check is nested inside scan_competitor_ai_presence. discover_tools and suggest_questions also overlap as tool-discovery entry points.

Naming Consistency3/5

Everything is snake_case and mostly descriptive, but conventions are mixed: verb-first names (resolve_entity, validate_claim, compare_entities, search_within) sit beside noun-first names (entity_profile, recent_changes, bet_research, polymarket_edges), and brand-prefixed tools (pipeworx_feedback, pipeworx_trending) have unprefixed functional siblings (list_subscriptions, recent_alerts). The two actual Base64 tools (base64_encode, base64_decode) don't match the dominant Pipeworx naming style at all.

Tool Count2/5

33 tools is heavy for any single server, and the mismatch is extreme: the server is named 'Base64' yet only 2 of 33 tools relate to encoding/decoding — the other 31 form a sprawling data-research platform. Even judged as a data platform, the count exceeds the comfortable range and includes near-duplicates (the ask_pipeworx family, the Polymarket family).

Completeness4/5

The factual-data surface is well covered: query, grounded lookup, deep research, entity profiling, comparison, claim verification, entity resolution, subscriptions (subscribe/list/unsubscribe/recent_alerts), memory (remember/recall/forget), feedback, and tool discovery all exist with no obvious dead ends. The Base64 encoding domain is also complete (encode/decode across four variants). Minor gaps exist (e.g., no way to update a profile or edit memory entries) but they're workable.

Resources