Skip to main content
Glama

AlpineDataWorks Intelligence Server

by com.alpinedataworks

GitHub-Language Momentum

adw.adw_073
Read-only

Returns a 0-100 developer momentum score for TypeScript, Python, Rust, and Go (30-day new-repo creation via GitHub Search API, normalized to a 100k ceiling; hourly, history since 2016) with trend, confidence, top_drivers, per_language breakdown, and total_new_repos_30d. Call when the user asks about programming language popularity, adoption trends, developer ecosystem growth, or open-source activity, or when timing devtools GTM, developer-marketing spend, or language-community sponsorships. Updates: hourly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoOptional: return a daily HISTORY series of the last N days (up to 5 years of real archived data) instead of the current snapshot. History requires Gold tier; without it, the current snapshot is returned.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint:true, so no safety concern is needed. The description adds useful context: data source (GitHub Search API), normalization ceiling, update frequency (hourly), and history depth (since 2016). It doesn't mention rate limits or authentication, but these are not critical for a read-only metric tool.

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 two sentences (plus an update note) that front-load the output and then provide usage context. Every sentence contributes 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 no output schema, the description lists all output fields (trend, confidence, top_drivers, per_language breakdown, total_new_repos_30d) and explains the data source and update cadence. It could mention the days parameter's effect more explicitly in the description itself, but the schema already handles that.

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 covers 100% of parameter documentation, including the days parameter's behavior and Gold tier requirement. The description mentions history since 2016 but adds no additional semantics beyond the schema's already complete parameter description.

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 returns a 0-100 developer momentum score for four specific languages, with the metric definition (30-day new-repo creation via GitHub Search API), normalization, and output fields. It is specific and distinguishes from siblings by focusing on language ecosystem momentum.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to call: 'when the user asks about programming language popularity, adoption trends, developer ecosystem growth, or open-source activity, or when timing devtools GTM, developer-marketing spend, or language-community sponsorships.' This gives clear invocation context, though it doesn't name specific alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All AlpineDataWorks Intelligence Server toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.3/5.0
Disambiguation1/5

With 318 tools named adw.adw_###, agents cannot tell them apart without reading full descriptions. Multiple tools cover the same domain (e.g., at least three USD strength scores: adw_055, adw_250, adw_580; four supply-chain stress scores: adw_009, adw_019, adw_020, adw_547), making misselection highly likely.

Naming Consistency3/5

The vast majority follow a consistent numeric ID pattern (adw.adw_###), but a small set breaks this with descriptive snake_case names (adw.catalog, adw.sample, adw.county_cancer, etc.). The numeric IDs are predictable but convey no semantic meaning, mixing with the few named tools and creating moderate inconsistency.

Tool Count1/5

318 tools is far beyond any reasonable scope for an intelligence server; even the largest sophisticated APIs rarely exceed 50. This extreme count suggests poor curation and will overwhelm agents with choice, making efficient tool selection impractical.

Completeness3/5

The server covers an extremely broad range of domains (crypto, macro, supply chain, healthcare, climate, county demographics), and includes discovery tools like adw.catalog and adw.sample. However, the surface is redundant and not systematically complete—many overlapping indices exist while other potentially valuable operations (e.g., raw data export, historical trend queries) are missing, leaving moderate gaps.

Resources