Skip to main content
Glama

Sec Form D

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 behavior. The description adds context on per-model return format, cost implications (free default, BYO key for Anthropic), and that the API key is passed through. It does not mention any side effects or error handling.

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 with no filler: front-loaded purpose, then model details and return structure, then use cases. Every sentence adds distinct value.

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?

The description covers the tool's purpose, parameters, return shape, and use cases. Without an output schema, it provides enough detail for an AI agent. It could mention potential rate limits or error conditions, but otherwise complete.

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%, providing a baseline of 3. The description adds value by explaining the default model, the necessity of _apiKey for Anthropic, and the purpose of the context parameter 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 per model. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on generic entity visibility across models rather than competitive analysis.

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 specifies use cases (AI-marketing audits, pre-launch checks, competitive monitoring) and explains when to use the default vs paid model. However, it does not explicitly mention when not to use this tool or name alternatives among the many siblings.

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

All Sec Form D toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all route questions to data sources with minor differences. Form D tools and meta-tools (discover_tools, suggest_questions) further blur boundaries, making it hard for an agent to select the right tool.

Naming Consistency4/5

Most tools follow a consistent snake_case verb_noun pattern (e.g., resolve_entity, validate_claim, subscribe). However, there are minor deviations like bet_research and deep_research without clear verbs, and the ask_pipeworx variants use irregular suffixes.

Tool Count2/5

With 39 tools, the server is over-scoped, including many utility and meta-tools (remember, recall, forget, list_subscriptions) that inflate the count beyond the core domain (SEC Form D and data lookups). A more focused set of 10-15 tools would be more coherent.

Completeness4/5

The tool set covers a very broad range of data sources and actions, including SEC filings, prediction markets, entity profiling, and AI visibility. However, the completeness is uneven; for example, there are many Form D tools but few for other SEC forms, and some areas like weather or clinical trials are only accessible via ask_pipeworx.

Resources