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Last Fm

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds behavioral details: it probes each entity with ai_visibility_check, ranks results, and outputs a ranked list with score, confidence, signal density. It also specifies first entity treated as 'subject' for narrative. No contradictions with annotations.

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 a single paragraph with clear structure: purpose, mechanism, use case, output. Every sentence adds value. No redundant information. Front-loaded with the core action.

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 what, how, and output format (ranked list with score, confidence, signal density). Though no output schema, the description provides sufficient detail for an agent to understand the return type. Slightly lacking in exact structure, but adequate given 4 parameters and no nested objects.

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 coverage is 100% (all 4 parameters described). The description adds meaning beyond schema: limits entities to 2-8, clarifies first is subject, states default model is workers-ai and _apiKey needed for anthropic. These details compensate for schema's lack of constraints.

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 explicitly states the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. It provides a concrete use case ('does Claude know about us as well as our competitors?') and clearly distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (generic compare).

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 clearly indicates when to use: for competitive AI-marketing audits. It implies not to use when only one entity is needed (since it probes multiple). However, it does not explicitly state exclusions or alternatives beyond the implied contrast with ai_visibility_check.

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

B3.1/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). Additionally, discover_tools and suggest_questions both serve as discovery/onboarding tools, and ai_visibility_check vs scan_competitor_ai_presence are closely related. While descriptions are detailed, the boundaries between these tools are unclear, causing potential misselection.

Naming Consistency4/5

All tool names use lowercase snake_case with no camelCase or mixed styles. The naming follows a mostly consistent verb_noun or data_subject pattern (e.g., ask_pipeworx, list_subscriptions, validate_claim, artist_info, recent_alerts). Minor deviations exist, such as recent_alerts and user_top_tracks not beginning with a verb, but the overall pattern is predictable and readable.

Tool Count2/5

40 tools is far too many for a server nominally about Last.fm; only 9 tools actually relate to its stated purpose. The remaining 31 tools cover unrelated domains (Pipeworx data routing, Polymarket betting, memory, subscriptions, etc.), making the set bloated and unfocused. Many tools are near-duplicates (ask_pipeworx, _beta, _grounded; four different polymarket_* analysis tools), inflating the count without adding distinct value.

Completeness2/5

Even within the Last.fm domain, the tool surface is incomplete: there is no artist search, album search, user profile info, recent scrobbles, loved tracks, or music recommendations. The unrelated Pipeworx/Polymarket tools, while individually comprehensive for their own domains, do not compensate for the lack of core Last.fm functionality given the server's stated purpose. The overall set is a fragmented mixture that leaves significant gaps for what the server name promises.

Resources