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Feodotracker

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable context: the cost implication for Anthropic calls ('you pay Anthropic directly'), the default model being free, and the return structure with per-model details and a combined view.

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 a single paragraph of 4 sentences, front-loaded with the main action and use case. Every sentence adds value without redundancy.

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 tool has 4 parameters (100% schema coverage) and no output schema, the description covers what an agent needs: purpose, parameters, cost note, and return format (per-model score, confidence, signals, raw_response + combined view). It is complete for a read-only, idempotent probe 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 all parameters are documented in the schema. The description adds meaning beyond schema by stating the default model for 'workers-ai' and clarifying the _apiKey is passed directly to Anthropic. It also explains the context parameter's purpose for disambiguation.

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 scores visibility (0-100) per model, with a specific verb and resource. It distinguishes from sibling tools like scan_competitor_ai_presence by focusing on AI-marketing audits and brand checks.

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 explains default model and optional Anthropic with BYO key. However, it does not explicitly exclude alternative tools or provide when-not-to-use guidance.

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

A3.6/5.0
Disambiguation2/5

Multiple tools occupy the same conceptual space: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all answer 'what can this server do' or 'look this up' in overlapping ways. The Polymarket suite (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) and the memory trio (remember, recall, forget) also create boundary confusion despite long descriptions.

Naming Consistency2/5

Naming is a mixed bag: some tools are imperative verbs (check_ip, forget, remember, validate_claim), some are bare nouns or adjectives (list, recent, aggressive), and many are noun compounds (entity_profile, polymarket_edges, pipeworx_trending). No consistent verb_noun or domain-prefix pattern holds across the set.

Tool Count2/5

35 tools is excessive for a server ostensibly named Feodotracker, whose core blocklist surface is only four tools (list, recent, aggressive, check_ip). The rest is a sprawling collection of unrelated Pipeworx, Polymarket, memory, subscription, and utility features that would be better split into separate servers.

Completeness3/5

The core blocklist domain is minimally covered: you can list, filter by family/status, check an IP, and see recent additions, which covers basic read-only use. However, there are notable gaps and dead ends, such as no historical lookup beyond recent hours and no per-IP detail beyond membership, while the bundled Pipeworx/Polymarket features are thorough but make the overall surface feel scattershot.

Resources