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Glama

Wikiquote

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint false. Description adds that it probes each entity with ai_visibility_check, ranks by score, and returns score/confidence/signal density per entity. 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?

Four sentences with front-loaded purpose. Each sentence adds unique value: purpose, method, use case, output. No redundant or extraneous content.

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?

No output schema, but description lists return fields (score, confidence, signal density) and explains the process. Could mention the entity count constraint (2-8) explicitly, but overall complete for the given complexity.

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

Parameters3/5

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

Schema description coverage is 100%, so baseline is 3. Description does not add new parameter information beyond the schema; it mentions the 'entities' parameter contextually but does not elaborate on optional params like models, _apiKey, or 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?

Description clearly states the verb 'compare' and resource 'AI visibility across multiple entities'. It distinguishes itself from sibling ai_visibility_check by focusing on side-by-side comparison of multiple entities, and gives a concrete use case example.

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?

Provides explicit context for use in competitive AI-marketing audits with an example question. Implies it is intended for multi-entity comparison versus single-entity check (ai_visibility_check), though does not explicitly state when not to use.

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.

Resources