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Glama

Phishtank

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. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, so the bar is lower. The description adds valuable behavioral context: default model is free, passing _apiKey incurs Anthropic billing and sends data to api.anthropic.com, and the return format is per-model with score/confidence/signals/raw_response plus a combined view. 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?

Three sentences, each earning its place: the first defines the action and scoring, the second covers model/billing options, the third specifies return structure and use cases. Front-loaded and free of filler.

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 there is no output schema, the description compensates by detailing the return fields (score, confidence, signals, raw_response, combined view), the default model, optional parameters, and example use cases. This is sufficient for an agent to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions for all 4 parameters, so baseline is 3. The description reinforces that models is optional and _apiKey is only needed for Anthropic, but adds no new syntax or format details 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 states a specific verb ('probe'), resource ('one or more LLMs'), and outcome ('score visibility 0-100 per model'). It distinguishes from siblings by focusing on quantitative visibility scoring rather than general Q&A or research, and lists concrete use cases like AI-marketing audits and pre-launch 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 gives clear context for when to use: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains the default free model versus optional paid Anthropic key. However, it does not name alternative tools or explicitly state when not to use this tool over siblings like scan_competitor_ai_presence.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Phishtank toolsInspect
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TDQS

B3.4/5.0
Disambiguation1/5

The server is named 'Phishtank' but only one tool (check_url) relates to phishing. The remaining 31 tools cover a wide range of unrelated topics (data research, prediction markets, memory, etc.), many with overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research). This makes it extremely difficult for an agent to select the right tool.

Naming Consistency2/5

Tool names mix conventions inconsistently: some use underscores (ai_visibility_check, check_url), some are camelCase (ask_pipeworx, bet_research), and others are compound phrases. There is no predictable pattern across the set.

Tool Count1/5

With 32 tools, the count is high, but only one aligns with the server name 'Phishtank' (check_url). The vast majority belong to an entirely different domain (Pipeworx tools), making the tool count severely inappropriate for the server's stated purpose.

Completeness1/5

For a phishing detection server, the tool surface is severely incomplete. It lacks essential tools like report_phish, verify_phish, get_stats, etc. The single phishing tool (check_url) is insufficient, while the other 31 tools are completely out of scope.

Resources