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Glama

Revternal

by io.github.pipeworx-io

Revternal Search Developers

revternal_search_developers
Read-onlyIdempotent

Search a live index of ~11.7M developers (GitHub-centric) by role, primary programming language, location, and recent activity — for recruiting, GTM/lead-gen, and developer-audience research. Returns matching GitHub usernames with role, primary language, location, and last-active timestamp. Filters: role (e.g. "backend engineer", "ai/ml engineer"), primary_language (e.g. "Python", "Rust"), location, active_within_days (only devs active in the last N days), sort_by (default last_active). Paginate with limit + offset. Example: revternal_search_developers({ primary_language: "Rust", active_within_days: 30, limit: 20 }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNoDeveloper role filter, e.g. "backend engineer", "ai/ml engineer", "frontend engineer".
limitNoResults per page, 1-100 (default 20).
offsetNoPagination offset (default 0).
_apiKeyNoRevternal API key (x-api-key). Injected from the platform key when omitted.
sort_byNoSort field. Default "last_active".
locationNoLocation filter, e.g. "Berlin", "United States".
primary_languageNoPrimary programming language, e.g. "Python", "Rust", "TypeScript".
active_within_daysNoOnly developers active within the last N days (>=1).

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: +[
+  {
+    "active_within_days": 30,
+    "limit": 20,
+    "primary_language": "Rust"
+  },
+  {
+    "active_within_days": 7,
+    "limit": 50,
+    "location": "Berlin",
+    "offset": 0,
+    "role": "backend engineer",
+    "sort_by": "last_active"
+  }
+]
  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, non-destructive operations. The description adds value by specifying the return fields (GitHub usernames with role, language, location, last-active timestamp) and pagination behavior (limit, offset, default sort). 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 an example; no filler. Front-loaded with core function and use cases. Every sentence contributes meaning.

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 8 parameters (all optional, well-described in schema) and no output schema, the description covers the key return data and pagination. It could mention that results are GitHub-only and what happens with no filters, but overall it is sufficient for an agent to use the tool correctly.

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?

Schema description coverage is 100%, so baseline is 3. The description mentions the key filters (role, primary_language, location, active_within_days) and provides an example, but does not add significant meaning beyond the schema descriptions.

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 searches developers by role, language, location, and activity for specific use cases like recruiting and lead-gen. It distinguishes itself from sibling Revternal tools (e.g., revternal_developer_intel, revternal_enrich_developer) by focusing on live search of a large index.

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 gives explicit use cases (recruiting, GTM, research) and provides a concrete example. It does not explicitly state when not to use this tool versus alternatives, but the sibling context implies it is the primary search tool for developer discovery.

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