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Gerolamo — Competitive Science and Technology Intelligence for AI Agents

Gerolamo — Competitive Science and Technology Intelligence for AI Agents

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github.com/adjective-robBusiness & Marketing1Updated 17w ago
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About Gerolamo — Competitive Science and Technology Intelligence for AI Agents

Gerolamo MCP gives agents competitive intelligence across open-source projects, research papers, and ML models. Researchers and technology teams can use it to evaluate technology choices and dependency risks; it also provides tools for search, scoring, pattern distillation, composition, lineage tracing, and multi-step workflows.

Key features

  1. 01Search projects, papers, and models
  2. 02Assess stacks and dependency risks
  3. 03Distill reusable patterns
  4. 04Compose enhancements and specifications
  5. 05Analyze creators and source lineage
  6. 06Use multi-step workflow templates

Use cases

  1. 01Find promising low-traction projects
  2. 02Check dependencies for competitive and obsolescence risks
  3. 03Compare technology options and compose solutions
  4. 04Generate ideas from research briefs

MCP support

Gerolamo MCP exposes competitive-intelligence search, scoring, analysis, composition, and workspace tools to agents through an MCP server. It also provides workflow templates that guide agents through sequences of tool calls.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://gerolamo.onrender.com/mcp/sse
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://gerolamo.onrender.com/mcp/sseAuth required

Tools

Details

From the project README

Gerolamo MCP

Connect your agent to Gerolamo — competitive intelligence across 36,000+ open-source projects, research papers, and ML models.

Every entity is scored for utility, momentum, and corroboration, with frontier-lab risk, threat profile, and composability. Your agent can search the corpus, distill reusable patterns, compose enhancements and specs, profile creators, and trace lineage.

Quick Setup

npx gerolamo-mcp setup

This adds Gerolamo to your Claude Code MCP configuration. You'll need an API key:

  1. Go to gerolamo.org and request an account
  2. Once approved, sign in
  3. Go to Connect and generate an API key

Manual Setup

Add to ~/.claude/mcp.json:

{
  "mcpServers": {
    "gerolamo": {
      "url": "https://gerolamo.onrender.com/mcp/sse",
      "headers": {
        "X-API-Key": "glm_your_key_here"
      }
    }
  }
}

Programmatic Usage

import { getConfig, TOOLS } from "gerolamo-mcp";

// Generate MCP config
const config = getConfig("glm_your_key_here");

// List available tools
console.log(Object.keys(TOOLS)); // 31 tools

Vocabulary

Gerolamo's corpus is organized into four altitudes. The tool names are stable wire identifiers, but the concepts they work on are:

  • Source — a real corpus entity (a repo, paper, or model).
  • Pattern — a reusable mechanism distilled out of a source (takes-X-produces-Y).
  • Concept — a speculative project idea with parent lineage (formerly "meta molecule").
  • Enhancement — a spec you compose from patterns and apply to a project.

Available Tools (31)

Intelligence Search
  • query_intelligence — Semantic search across all corpora (set include_meta=true to include concepts)
  • search_intelligence — RAG-synthesized answer to a research question
  • find_sleepers — High-scoring, low-traction sources — the hidden gems
  • find_alternatives — Find projects that could replace a given entity
Analysis
  • score_stack — Weakest-link defensibility analysis for a dependency set
  • explain_score — Full reasoning behind an entity's score
  • analyze_competitive_landscape — Velocity-ranked topic analysis
  • explore_connections — 5 rings of intelligence context around an entity
Composition
  • ideate_from_brief — Grounded, cited ideation from a solicitation/brief (RFI, Sources Sought, OTA, SBIR)
  • compose_molecules — Fuse a kit of entities, concepts, and/or patterns into specs, comparisons, or research briefs
  • save_composition — Persist a generated enhancement with lineage tracking and a shareable URL
  • suggest_tools — Recommend which Gerolamo tools to use for a task
Patterns & Enhancements
  • distill — Distill a source into its reusable patterns, each with an interface, effect, and provenance
  • search_patterns — Search the pattern library — hybrid keyword + semantic, sortable and filterable
  • compose_enhancement — Compose patterns into an enhancement, attributed to their sources
Intelligence Briefs
  • get_intelligence_brief — Combined situation report: sleepers, trending, breakouts
  • get_my_latest_intelligence — Latest subscription alert results
Creator Analysis
  • get_creator_profile — Creator portfolio with authority stats
  • get_creator_network — Creator collaboration graph
  • get_creator_authority — Creator authority score lookup
  • find_defensible_clusters — Cluster detection across the corpus
Topic Intelligence
  • get_tracked_topics — List all tracked topics with entity counts and risk stats
Concepts & Lineage
  • submit_meta_molecule — Create a speculative concept with required parent lineage
  • realize_meta_molecule — Connect a concept to a real URL and queue for ingestion
  • trace_lineage — Trace ancestors or descendants of any entity or concept
  • find_family — Full lineage family — ancestors, descendants, and direct edges
Workspace
  • create_workspace — Create a named workspace with entities
  • add_to_workspace — Add entities to an existing workspace
  • submit_source — Submit a URL to add a source to the corpus
Workflows
  • list_workflows — List all available agent workflow templates
  • get_workflow — Get a workflow's full prompt template by slug

Agent Workflows

Workflows are multi-step prompt templates that chain MCP tools into complete intelligence operations. Instead of figuring out which tools to call and in what order, your agent loads a workflow and follows the steps.

"Run the Domain Discovery workflow for autonomous agents"
→ agent calls get_workflow("domain-discovery")
→ agent reads the 6-step prompt
→ agent executes each step using MCP tools, thinking between steps
→ you end up with: concepts, enhancements, submitted research

Browse and contribute workflows at gerolamo-workflows.

Example Tool Chains

Scout before you build:

suggest_tools("I need to build an autonomous drone system")
→ get_intelligence_brief(topic="autonomous drone middleware")
→ find_sleepers(query="flight controller SLAM", min_score=6)
→ compose_molecules(entity_ids=[...], mode="compose")
→ save_composition(workspace_name="Drone Stack", mode="compose", result=<output>, entity_ids=[...])

Distill patterns and compose an enhancement:

query_intelligence(question="retrieval-augmented generation")
→ distill(source_id="<source-uuid>")
→ search_patterns(query="chunking strategy", effect="retrieval")
→ compose_enhancement(pattern_ids=[...])

Threat-check your dependencies:

score_stack(entity_ids=["uuid1", "uuid2", "uuid3"])
→ explain_score(entity_id="weakest-link-uuid")
→ find_alternatives(entity_id="weakest-link-uuid")

Compare before you choose:

query_intelligence(question="vector database")
→ compose_molecules(entity_ids=[...], mode="compare")

CLI Commands

npx gerolamo-mcp setup           # Configure Claude Code
npx gerolamo-mcp setup --key K   # Configure with specific key
npx gerolamo-mcp info            # List all tools

Links

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