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Glama

Revternal

by io.github.pipeworx-io

Revternal Developer Intel

revternal_developer_intel
Read-onlyIdempotent

Get behavioural intelligence for one developer by GitHub username — how they actually work, not just their bio. Returns work pattern (chronotype, peak hours, work rhythm, activity trend, velocity change), collaboration style (solo vs team, external PR ratio, reviews given), professionalism signals (commit quality, conventional commits, primary work type), and activity recency. Use to assess engagement, seniority signals, and outreach timing. Example: revternal_developer_intel({ github_username: "torvalds" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_apiKeyNoRevternal API key (x-api-key). Injected from the platform key when omitted.
github_usernameYesGitHub username (login), e.g. "torvalds".

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "github_username": "torvalds"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by detailing the type of data returned (work patterns, collaboration style, etc.) without contradicting annotations. It could be improved by noting data freshness or limitations.

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: function and outputs, use cases, and an example. Every sentence is essential, front-loaded, and waste-free. Excellent conciseness.

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?

Despite no output schema, the description thoroughly lists all return categories (work pattern, collaboration style, professionalism signals, activity recency) and provides concrete use cases. This gives the agent sufficient context to understand and use the tool.

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 input schema has 100% coverage with descriptions for both parameters (_apiKey and github_username). The tool description only restates the example, adding no new semantics. 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 purpose: 'Get behavioural intelligence for one developer by GitHub username' and lists the specific categories returned. This distinguishes it from sibling tools like 'revternal_enrich_developer' which likely focuses on bio data.

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 clear usage contexts: 'Use to assess engagement, seniority signals, and outreach timing.' However, it does not mention when not to use this tool or explicitly name alternatives, missing the bar for 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

A4/5.0
Disambiguation3/5

Several tool families (ask_pipeworx variants, polymarket analysis tools) have overlapping purposes, which could confuse an agent. However, descriptions are detailed and help differentiate them in most cases.

Naming Consistency4/5

All tool names use snake_case and are descriptive, but prefixes vary (ask_, polymarket_, revternal_, etc.) and some verbs are standalone (forget, recall, remember), breaking a strict verb_noun pattern.

Tool Count3/5

35 tools is on the high side, but the scope is broad (data research, prediction markets, developer intel). Some redundancy (multiple ask_pipeworx modes) could be consolidated, making the set feel slightly heavy.

Completeness4/5

The tool set covers core CRUD for data, memory, subscriptions, and analytics. Minor gaps exist (e.g., no file upload, limited account management), but the domain is well-served.

Resources