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Ai Briefing

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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Adds value beyond annotations by describing the free default model, BYO key for Anthropic cost implications, and per-model return structure. Aligns with readOnlyHint, openWorldHint, idempotentHint, and destructiveHint.

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, front-loaded with core purpose, then key details, then return structure and use cases—every sentence serves a purpose.

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?

Despite no output schema, the description adequately details return fields and use cases, making it complete for a tool with 4 simple parameters.

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

Parameters5/5

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

With 100% schema coverage, the description enriches meaning by specifying the default model, the conditional requirement for _apiKey, and the disambiguation role of context.

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, distinguishing it from sibling tools like deep_research or entity_profile.

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?

Explicitly mentions use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and implies when to use by contrasting with alternatives, though no explicit exclusions.

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

Many tools have overlapping purposes, such as multiple tools for querying Pipeworx data (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and numerous tools for AI news/tools (get_ai_news, get_ai_toolbelt, get_briefing, get_model_landscape, etc.). This will cause an agent to frequently misselect the appropriate tool.

Naming Consistency4/5

Most tools follow a verb_noun pattern in snake_case (e.g., compare_entities, discover_tools, get_briefing). However, a few deviate like 'bet_research' (noun_verb) and 'what_happened' (phrase), but overall the pattern is largely consistent.

Tool Count2/5

38 tools is excessive for a server called 'Ai Briefing', which suggests a focused purpose. The tool count spans multiple domains (AI visibility, Pipeworx queries, Polymarket betting, memory, subscriptions) making it feel overstuffed and unfocused.

Completeness3/5

The tool set covers many aspects of its broad domain (querying, comparing, subscribing, memory), but there are notable gaps: no tool for modifying subscriptions, no user profile management, and the AI news tools overlap rather than cover distinct needs.

Resources