Gerolamo — Competitive Science and Technology Intelligence for AI Agents
RemoteAbout 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
- 01Search projects, papers, and models
- 02Assess stacks and dependency risks
- 03Distill reusable patterns
- 04Compose enhancements and specifications
- 05Analyze creators and source lineage
- 06Use multi-step workflow templates
Use cases
- 01Find promising low-traction projects
- 02Check dependencies for competitive and obsolescence risks
- 03Compare technology options and compose solutions
- 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.
https://gerolamo.onrender.com/mcp/sseclaude mcp add --transport http gerolamo https://gerolamo.onrender.com/mcp/sse{"mcpServers":{"gerolamo":{"type":"http","url":"https://gerolamo.onrender.com/mcp/sse"}}}Open in Cursor{"servers":{"gerolamo":{"type":"http","url":"https://gerolamo.onrender.com/mcp/sse"}}}Open in VS Code{"mcpServers":{"gerolamo":{"type":"http","url":"https://gerolamo.onrender.com/mcp/sse"}}}{"mcpServers":{"gerolamo":{"serverUrl":"https://gerolamo.onrender.com/mcp/sse"}}}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.
https://gerolamo.onrender.com/mcp/sseAuth requiredTools
Details
From the project READMEGerolamo 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:
- Go to gerolamo.org and request an account
- Once approved, sign in
- 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 (setinclude_meta=trueto include concepts)search_intelligence— RAG-synthesized answer to a research questionfind_sleepers— High-scoring, low-traction sources — the hidden gemsfind_alternatives— Find projects that could replace a given entity
Analysis
score_stack— Weakest-link defensibility analysis for a dependency setexplain_score— Full reasoning behind an entity's scoreanalyze_competitive_landscape— Velocity-ranked topic analysisexplore_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 briefssave_composition— Persist a generated enhancement with lineage tracking and a shareable URLsuggest_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 provenancesearch_patterns— Search the pattern library — hybrid keyword + semantic, sortable and filterablecompose_enhancement— Compose patterns into an enhancement, attributed to their sources
Intelligence Briefs
get_intelligence_brief— Combined situation report: sleepers, trending, breakoutsget_my_latest_intelligence— Latest subscription alert results
Creator Analysis
get_creator_profile— Creator portfolio with authority statsget_creator_network— Creator collaboration graphget_creator_authority— Creator authority score lookupfind_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 lineagerealize_meta_molecule— Connect a concept to a real URL and queue for ingestiontrace_lineage— Trace ancestors or descendants of any entity or conceptfind_family— Full lineage family — ancestors, descendants, and direct edges
Workspace
create_workspace— Create a named workspace with entitiesadd_to_workspace— Add entities to an existing workspacesubmit_source— Submit a URL to add a source to the corpus
Workflows
list_workflows— List all available agent workflow templatesget_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
- Gerolamo — Web interface
- gerolamo-workflows — Agent workflow templates (open source)
- API Docs — OpenAPI documentation
- llms.txt — Agent discovery file
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.