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Glama

Gnomad

by io.github.pipeworx-io

Pipeworx Trending

pipeworx_trending
Read-onlyIdempotent

What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNo24h (default) | 7d | 30d. Shorter windows surface what's hot right now; longer windows show steady-state demand.

Schema Changelog

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

  1. Added

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses several behavioral traits beyond the annotations: it is 'derived from CF analytics-engine', contains 'no PII, just (pack, tool, count)', and is 'Cached 5min-1h depending on window.' This adds meaningful context about data provenance, privacy, and caching that annotations do not provide.

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 long, front-loaded with the core purpose, followed by use cases and then transparency details. Every sentence adds value with no redundancy, making it highly efficient and well-structured.

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?

Given the tool's low complexity (one optional parameter, no output schema), the description is complete: it explains the return content (top tools, top packs, total call volume), the underlying data structure (pack, tool, count), and the caching behavior. This is sufficient for an AI agent to understand what the tool provides.

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?

The schema already provides 100% coverage for the single 'window' parameter with its enum values and semantics. The description repeats the same information ('24h, 7d, or 30d' and 'Shorter windows surface what's hot right now') without adding new meaning, so the baseline of 3 applies.

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: 'Returns the top tools, top packs, and total call volume over a recent window.' It uses a specific verb ('returns') and resource (trending Pipeworx calls), and distinguishes itself from siblings like discover_tools by emphasizing it is a 'self-aggregating signal' of agent activity, not a tool discovery catalog.

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 explicit use cases under 'Useful for:', including discovering hot data sources, confirming canonical tools, and checking alignment with agent needs. It gives clear context for when to use the tool, but does not name alternative tools or state when not to use it, which prevents a 5.

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

A3.6/5.0
Disambiguation2/5

Several tools are near-duplicates with only subtle differences, such as ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded, and ai_visibility_check vs scan_competitor_ai_presence. discover_tools and suggest_questions also overlap as discovery/onboarding tools, and bet_research/deep_research/ask_pipeworx all blur the line between single and multi-source lookup. Agents are likely to pick the wrong one without reading very long descriptions.

Naming Consistency3/5

Names are mostly snake_case and readable, but there is no consistent verb_noun pattern: some are bare nouns like gene, variant, transcript, region, and search while others are long descriptive phrases like generate_llms_txt and scan_competitor_ai_presence. The mix is not chaotic, but it is not a coherent naming scheme.

Tool Count2/5

36 tools is well above the 25+ threshold and feels heavy for a single server, especially when the server is named Gnomad but hosts many unrelated Pipeworx, subscription, memory, and data-retrieval tools. Several tools could be consolidated (e.g. the ask_pipeworx variants, ai_visibility_check with scan_competitor_ai_presence), which would make the surface more manageable.

Completeness4/5

The genomics slice is covered well with search, gene, variant, transcript, and region, and the broader data surface includes subscriptions, memory, feedback, and discovery. Minor gaps exist around version/coverage metadata for gnomAD and clearer lifecycle operations for subscriptions, but the core domains are largely covered.

Resources