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Glama

Wikiquote

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

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds value by specifying the default model, BYO key requirement, and return structure ({score, confidence, signals, raw_response}). 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 three sentences, front-loaded with the main action, and every sentence adds value. No fluff or redundancy.

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?

With no output schema, the description adequately describes return structure (per-model + combined). It covers parameters, behavior, and output. Could mention rate limits or cost implications but not necessary for completeness.

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%, baseline 3. The description adds meaning: explains how to omit models for default, the purpose of _apiKey (passed through to Anthropic), and context for disambiguation. This exceeds baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool probes LLMs and scores visibility (0-100) per model, with specific examples. It does not explicitly differentiate from sibling tools like 'scan_competitor_ai_presence', but the action is distinct enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') but does not provide explicit when-to-use vs alternatives or exclusions. Sibling tools exist but no guidance on choosing.

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

B3/5.0
Disambiguation2/5

The tool set mixes four Wikiquote tools with 31 unrelated Pipeworx/Polymarket tools, creating a confusing dual identity. Within the research tooling, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-synonyms, and polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk overlap heavily, making selection ambiguous.

Naming Consistency3/5

All names are snake_case, but patterns vary: some are verb-first (ask_pipeworx, compare_entities, resolve_entity), some noun-first (entity_profile, polymarket_arbitrage, quote_of_the_day), and several are bare verbs or nouns (search, summary, remember, quotes). Prefix groups like ask_pipeworx* and polymarket_* are consistent, but the overall convention is mixed.

Tool Count2/5

35 tools is excessive for a server named Wikiquote, as only 4 tools (quote_of_the_day, quotes, search, summary) actually serve that domain. The remaining 31 form an unrelated general research and prediction-market toolkit, making the set feel bloated and off-scope for its stated name.

Completeness1/5

As a Wikiquote server, the surface is severely incomplete: there is no random quote, page listing, author/topic browsing, or any write/update operations, and the few quotation tools are buried among unrelated functionality. The unrelated research tools may be internally rich, but they do not address the server's stated purpose.

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