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Figshare

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. Added

TDQS

A4.3/5.0
Behavior4/5

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

The description discloses the internal process: 'Probes each entity ... with ai_visibility_check, ranks by score, surfaces which is most/least recognized' and the return format: 'Returns ranked list with score, confidence, signal density per entity.' This adds behavioral context beyond the annotations (readOnly, openWorld, idempotent), such as the delegation to ai_visibility_check and the aggregation behavior.

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, each earning its place: purpose, mechanics, and use case/output. It is front-loaded with the main action and includes no redundant wording.

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?

For a tool with no output schema, the description adequately covers the return values ('ranked list with score, confidence, signal density per entity') and explains the overall comparison narrative. It also gives a realistic example that clarifies the tool's value in competitive analysis, making it complete for an agent to understand when and how to use it.

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 all parameters are documented in the schema. The description adds minimal param-specific insight beyond what the schema already provides, such as clarifying the first entity is the subject ('your brand + N competitors'), but this is also in the schema's entities description. Baseline 3 is appropriate.

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 function: 'Compare AI visibility across multiple entities side-by-side.' This is a specific verb+resource+scope, and it distinguishes from siblings like ai_visibility_check (single entity) and compare_entities (generic comparison) by focusing on AI visibility and multi-entity ranking.

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 provides a clear use case: 'Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?"' It implies when to use (comparing multiple entities) but does not explicitly name alternatives or exclusions. This is clear context without explicit when-not-to-use guidance.

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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are documented as currently identical, ask_pipeworx_grounded/deep_research route the same class of questions, and the six polymarket_* tools plus bet_research form a confusing cluster. Some clusters (Figshare fetch/search, memory, subscriptions) are distinct, but overall boundaries are frequently unclear.

Naming Consistency4/5

Names are uniformly lowercase snake_case and mostly follow a predictable verb_noun pattern (ask_pipeworx, list_subscriptions, resolve_entity, scan_dependency). The main inconsistency is the mix of bare-noun Figshare resource names (article, articles, collection, collections) with verb-phrase tool names.

Tool Count2/5

38 tools is well above the comfortable scope for a coherent server, especially one named Figshare. Most of the surface is unrelated to Figshare, bundling Pipeworx research, Polymarket analysis, memory, subscriptions, AI visibility, and npm checks into a single connection.

Completeness2/5

The Figshare read-side is reasonably covered (search, article metadata, files, collections, categories, licenses), but there are no create/update/delete or account/upload operations. The broader advertised surface is a grab bag with no clear domain boundary, and several subdomains are shallow while prediction markets are over-represented.

Resources