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Glama

Data Lv

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

A4.4/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive. Description adds valuable behavioral context: default model is free, Anthropic requires BYO key and charges, returns per-model details. 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?

Two sentences, front-loaded with core purpose, no fluff. Each sentence adds value: what it does, default/optional behavior, return structure, use cases.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, description explains return format (per-model fields + combined view). Covers all 4 parameters meaningfully. Lacks only minor details like rate limits, but annotations imply idempotent and read-only, so not critical.

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% with good property descriptions. Tool description enriches: explains _apiKey passed through to Anthropic, context helps disambiguate, entity examples provided. Adds meaning beyond 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?

States specific verb 'Probe' and clear resource 'LLMs for what they know about a business/brand/product/topic', with explicit output 'score visibility (0-100) per model'. Distinct from siblings like ask_pipeworx which are about different domains.

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?

Clearly states when to use: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. Explains default model and optional Anthropic probe. Does not directly compare to sibling tools but context is sufficient given the unique purpose.

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

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct purpose: data lookup (ask_pipeworx vs deep_research), entity profiles, comparisons, memory, monitoring, and prediction market analysis. Overlaps are minimal and mitigated by explicit usage guidance (e.g., ask_pipeworx vs. ask_pipeworx_grounded vs. deep_research).

Naming Consistency5/5

All tools use descriptive snake_case names following a verb_noun or verb_preposition pattern (e.g., entity_profile, resolve_entity, scan_dependency). The naming is predictable and internally consistent, making it easy for an agent to infer tool purposes.

Tool Count3/5

33 tools is on the high end for typical MCP servers. However, the server covers an exceptionally broad domain (structured data across SEC, FDA, FRED, weather, news, crypto, etc.) and includes meta-tools, monitoring, and memory. The count is justified but may feel excessive for many use cases.

Completeness5/5

The tool surface covers the full lifecycle of data access and analysis: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, entity_profile), comparison, claim validation, monitoring, memory, and feedback. There are no obvious gaps for the declared domain, and a feedback tool is provided for missing functionality.

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