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Glama

Smithsonian

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate the tool is read-only, open-world, idempotent, and non-destructive. The description adds valuable context beyond this: default model, API key requirement for Anthropic, and the exact return format (per-model {score, confidence, signals, raw_response} + combined view). 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?

The description is extremely concise: two sentences for the main action and one for use cases. No redundant information. Front-loaded with the core operation.

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?

The description is complete for a tool with no output schema: it explains what each model probe returns and the overall combined view. It also clarifies cost implications (free vs BYO key). All necessary behavioral context is provided.

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%, so baseline is 3. The description adds nuance beyond the schema: explains default model behavior, that omitting 'models' uses only workers-ai, and that _apiKey is passed to Anthropic. This extra context earns a 4.

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's purpose: to probe LLMs about an entity and score visibility (0-100) per model. The verb 'probe' and resource 'LLMs' are specific, and the outcome (scoring visibility) distinguishes it from sibling tools like ask_pipeworx or deep_research.

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 lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), providing clear context for when to use the tool. However, it does not explicitly state when not to use it or mention alternative tools.

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

A3.9/5.0
Disambiguation3/5

Many tools serve overlapping purposes, such as the three ask_pipeworx variants (stable, beta, grounded) and the six Polymarket-specific tools. While detailed descriptions help distinguish them, an agent could still confuse bet_research with polymarket_edges or the ask_pipeworx versions.

Naming Consistency2/5

Naming is highly inconsistent: verb_noun (ask_pipeworx, compare_entities), noun_noun (bet_research, entity_profile), single verbs (forget, recall, search), and adjective_noun (recent_alerts, deep_research). No clear pattern emerges across the set.

Tool Count3/5

33 tools is on the high side for a server named 'Smithsonian' that actually covers a broad range of data sources (SEC, FDA, Polymarket, npm, etc.). The number feels borderline heavy but is still manageable if the server's true purpose is general research.

Completeness4/5

The tool set covers multiple domains (company financials, drugs, economics, prediction markets, npm, museum data) with reasonable depth. Minor gaps exist, such as lack of PyPI scanning or missing update/delete operations for some memory features, but core workflows are well-supported.

Resources