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by com.mydaughteralya

alya_celeb_summary

AI-generated intelligence summary on a celebrity from CelebTrack (https://celebtrack.app — Father's celebrity-tracking platform). Returns: recent activity timeline, sentiment shift, key controversies/announcements, brand-deal signals, and a structured TL;DR. Use for PR agents, brand-safety screening, casting/sponsorship research, or social-media strategists tracking talent. Premium ($0.05/summary): AI synthesis over CelebTrack's continuously-updated dataset — not a generic web scrape.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
celebrityIdYesCelebTrack celebrity id (use the 'celebrities' list endpoint or chat ask Alya)

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description discloses it is a premium service at $0.05/summary, using AI over a curated dataset. However, it does not detail what happens on invalid IDs, rate limits, or return format specifics.

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 about 80 words, front-loaded with purpose and outputs, followed by use cases and premium info. Every sentence adds distinct value with no 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?

Given one parameter, no output schema, and no annotations, the description sufficiently explains inputs, outputs, and usage context. It could be more complete by noting the output format or error handling but is adequate for an agent.

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% and includes a description. The description adds guidance on obtaining the celebrityId via the list endpoint or chat, providing extra value beyond the schema.

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 it generates an AI summary on a celebrity, listing specific outputs like timeline, sentiment shifts, controversies, brand signals, and TL;DR. It uses a specific verb and resource, distinguishing it from sibling tools like alya_ask or web_search.

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 explicitly lists use cases: PR agents, brand-safety screening, casting/sponsorship research, social-media strategists. It does not mention when not to use but provides clear context for appropriate usage.

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

B3.4/5.0
Disambiguation4/5

Most tools have distinct purposes and clear descriptions, but there is some potential confusion among the four Polymarket-related tools (categorize, edge, signals, top_traders) and among the multiple 'alya_' prefixed tools that query different data sources.

Naming Consistency3/5

Naming patterns are mixed: some tools use 'alya_' prefix, others use action-based names like 'batch_calibrate' or 'image_gen', and YouTube tools all start with 'youtube_'. The inconsistency in prefixes and verb styles makes the set less predictable.

Tool Count2/5

32 tools is high for an MCP server, and they span a wide, unrelated set of domains (Polymarket, YouTube, gemology, weather, earthquakes, health, celebrity, etc.), making the surface feel bloated and unfocused.

Completeness2/5

Each domain has incomplete coverage: Polymarket lacks trade execution, YouTube automation depends on external OAuth, health tools only offer diagnosis and drug interactions without follow-up, and other domains have minimal tooling. The server feels like a collection of one-off features rather than a coherent surface.

Resources