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Revternal Enrich Developer

revternal_enrich_developer
Read-onlyIdempotent

Enrich a developer from their GitHub profile URL — full firmographic + technical profile. Returns name, location, company, followers, years active, seniority estimate, and a ranked skills breakdown (languages by repo share). Use to enrich a lead or candidate you already have a GitHub URL for. Example: revternal_enrich_developer({ github_url: "https://github.com/torvalds" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
_apiKeyNoRevternal API key (x-api-key). Injected from the platform key when omitted.
github_urlYesFull GitHub profile URL, e.g. "https://github.com/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_url": "https://github.com/torvalds"
      +  }
      +]
  2. First observed

TDQS

A4.2/5.0
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 useful behavioral context by listing specific return fields and mentioning 'ranked skills breakdown (languages by repo share)', which goes beyond annotations. No contradictions.

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?

Two sentences plus a one-line example. Extremely concise and front-loaded with the main purpose. Every word earns its place, no fluff.

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 low complexity (2 parameters, no output schema), the description fully captures what the tool does and what it returns. It lists multiple return fields and provides an example, so an agent has enough context to use it correctly.

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% with descriptions for both parameters. The description adds value by providing an example usage and clarifying that the _apiKey is injected when omitted, which enhances understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Enrich' and resource 'developer' from a GitHub profile URL, listing specific outputs. However, it does not explicitly differentiate from sibling tools like 'revternal_developer_intel' or 'revternal_search_developers', which have overlapping purpose. Still, the verb and resource are specific enough.

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 tells when to use: 'Use to enrich a lead or candidate you already have a GitHub URL for.' This provides clear context for appropriate usage. It does not explicitly state when not to use or mention alternatives, but the context is clear.

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.