INDRA Agent

MCP server for the INDRA CoGEx biomedical knowledge graph.

Gives AI agents access to 20M+ nodes spanning genes, diseases, drugs, pathways, and their causal relationships — assembled from scientific literature and curated databases by INDRA (Integrated Network and Dynamical Reasoning Assembler).

Replaces 100+ individual function tools with 9 composable tools that expose the full power of the knowledge graph while maintaining safety and usability.

Quick Start

Public server — no installation needed:

{
  "mcpServers": {
    "indra-cogex": {
      "url": "https://discovery.indra.bio/mcp"
    }
  }
}

Add this to your MCP client config (Claude Desktop, Claude Code, Cursor, etc.) and start asking questions about genes, diseases, and drugs.

Local installation — for development or private Neo4j instances:

pip install git+https://github.com/gyorilab/indra_agent.git

Connection Modes

	Stdio (local)	HTTP (remote)
Use case	Claude Desktop, Cursor, Claude Code	Deployed server, shared access
Config key	"command": "indra-agent"	"url": "https://..."
Neo4j credentials	Required locally (env vars)	Configured server-side
Scaling	Single process	Multi-worker (gunicorn)

Stdio Mode (Default)

For local MCP clients:

# Via console script
indra-agent

# Or via module
python -m indra_agent.mcp_server

Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "indra-cogex": {
      "command": "indra-agent",
      "env": {
        "INDRA_NEO4J_URL": "bolt://your-server:7687",
        "INDRA_NEO4J_USER": "neo4j",
        "INDRA_NEO4J_PASSWORD": "your-password",
        "MCP_ALLOWED_HOSTS": "localhost",
        "MCP_ALLOWED_ORIGINS": "http://localhost"
      }
    }
  }
}

HTTP Mode

For network deployments:

indra-agent --http
indra-agent --http --host 0.0.0.0 --port 8000
indra-agent --http --stateful --streaming

Production (gunicorn):

gunicorn indra_agent.mcp_server.server:app \
  --bind 0.0.0.0:8778 \
  --worker-class uvicorn.workers.UvicornWorker \
  -w 4

Tools

9 tools in two groups:

Gateway Tools (5 tools)

High-level graph navigation — most agents start here:

Tool	Purpose
ground_entity	Natural language to CURIE with semantic filtering. Supports single term or batch mode.
suggest_endpoints	Given CURIEs, suggest reachable entity types and traversal functions
call_endpoint	Execute any of 100+ autoclient functions with auto-grounding, caching, and xref fallback
get_navigation_schema	Full edge map showing how entity types connect. Optional entity_type filter.
batch_call	Execute an endpoint for multiple entities in one call. Routes to native batch queries where available.

Query Infrastructure (4 tools)

Low-level Cypher access for complex queries:

Tool	Purpose
get_graph_schema	Discover entity types, relationships, patterns
execute_cypher	Run arbitrary Cypher with parameterization
validate_cypher	Pre-flight safety validation
enrich_results	Add metadata at configurable disclosure levels

call_endpoint Parameters

The primary query tool supports several optimization parameters:

Parameter	Type	Description
endpoint	str	Function name (e.g., "get_diseases_for_gene")
kwargs	str	JSON arguments. Entities as CURIE tuples {"gene": ["HGNC", "6407"]} or natural language {"gene": "LRRK2"}
auto_ground	bool	Auto-ground string params to CURIEs (default: True)
disclosure_level	str	Enrich results: "standard" (~250 tokens/item), "detailed" (~400), "exploratory" (~750)
offset	int	Pagination offset. Use next_offset from previous response to continue.
limit	int	Max items per page. Auto-truncates to ~20k tokens if exceeded.
fields	list	Project results to specific keys (e.g., ["db_ns", "db_id", "name"]). Reduces token usage.
estimate	bool	Probe query cost before fetching. Returns count, token estimate, available fields, sample.
sort_by	str	"evidence" (most-validated first) or "name" (alphabetical). Applied before pagination.
include_navigation	bool	Append suggested next traversal steps to results.

batch_call Parameters

Parameter	Type	Description
endpoint	str	Function name (e.g., "get_diseases_for_gene")
entity_param	str	Which parameter to batch over (e.g., "gene")
entity_values	list	Entity values to query (e.g., ["SIRT3", "PRKN", "MAPT"]). Auto-grounded.
fields	list	Project results to specific keys per item
max_concurrent	int	Max parallel queries (default: 10)
merge_strategy	str	"keyed" (default): {entity: results} dict. "flat": concatenated list.

For get_drugs_for_target and get_targets_for_drug, batch_call automatically routes to native batch functions that use a single Neo4j WHERE IN query instead of N separate queries.

Architecture

flowchart LR
    Agent["Agent"]

    subgraph Gateway["Gateway Tools"]
        direction TB
        Ground["ground_entity<br/><i>NL to CURIE</i>"]
        Suggest["suggest_endpoints<br/><i>navigation hints</i>"]
        Call["call_endpoint<br/><i>100+ functions</i>"]
        NavSchema["get_navigation_schema<br/><i>edge map</i>"]
        Batch["batch_call<br/><i>parallel execution</i>"]
    end

    subgraph Core["Query Infrastructure"]
        direction TB
        Schema["get_graph_schema<br/><i>progressive discovery</i>"]
        Execute["execute_cypher<br/><i>arbitrary queries</i>"]
        Validate["validate_cypher<br/><i>safety checks</i>"]
        Enrich["enrich_results<br/><i>metadata layers</i>"]
    end

    subgraph Cache["Cache Layer"]
        direction TB
        DiskCache["diskcache<br/><i>LRU + TTL</i>"]
        Coalesce["request coalescing<br/><i>dedup in-flight</i>"]
    end

    subgraph Data["Data Layer"]
        direction TB
        Neo[("Neo4j<br/>20M nodes")]
        GILDA["GILDA API"]
    end

    Agent -->|"ground terms"| Ground
    Agent -->|"explore edges"| Suggest
    Agent -->|"call functions"| Call
    Agent -->|"batch queries"| Batch
    Agent -->|"discover schema"| Schema
    Agent -->|"run Cypher"| Execute

    Ground --> GILDA
    Suggest --> Call
    Call --> DiskCache
    Batch --> Call
    NavSchema --> Call

    DiskCache -->|"miss"| Neo
    DiskCache -->|"hit"| Call
    Coalesce --> DiskCache

    Schema --> Neo
    Execute --> Validate
    Validate --> Neo

    classDef agent fill:#2C3E50,stroke:#1A252F,stroke-width:3px,color:#ECF0F1,font-weight:bold
    classDef gateway fill:#9B59B6,stroke:#8E44AD,stroke-width:2px,color:#FFFFFF
    classDef core fill:#3498DB,stroke:#2980B9,stroke-width:2px,color:#FFFFFF
    classDef cache fill:#27AE60,stroke:#1E8449,stroke-width:2px,color:#FFFFFF
    classDef data fill:#7F8C8D,stroke:#5D6D7E,stroke-width:2px,color:#ECF0F1

    class Agent agent
    class Ground,Suggest,Call,NavSchema,Batch gateway
    class Schema,Execute,Validate,Enrich core
    class DiskCache,Coalesce cache
    class Neo,GILDA data

Loading

Most agents use Gateway Tools — ground natural language to CURIEs, then call pre-built functions. When predefined functions cannot express the query (graph algorithms, multi-hop traversals, conditional aggregations), agents drop down to Query Infrastructure.

Context-Aware Grounding

Parameter semantics encode entity type, eliminating cross-type ambiguity:

ground_entity(term="ALS", param_name="disease")
# → MESH:D000690 (Amyotrophic Lateral Sclerosis)

ground_entity(term="ALS", param_name="gene")
# → HGNC:396 (SOD1, formerly ALS1)

ground_entity(term="aspirin", param_name="drug")
# → CHEBI:15365

Supported param_name filters: disease, gene, drug, pathway, tissue, cell_line, cell_type, side_effect.

Batch mode grounds multiple terms in one call:

ground_entity(terms=["SIRT3", "PRKN", "MAPT"], param_name="gene")
# → {mappings: {"SIRT3": {curie, name, score}, ...}, failed: [...]}

Organism context accepts common names or taxonomy IDs:

ground_entity(term="LRRK2", organism="human")   # resolved to 9606
ground_entity(term="LRRK2", organism="9606")     # passthrough
ground_entity(term="Trp53", organism="mouse")    # resolved to 10090

Cross-Reference Fallback

When a grounded query returns zero results, call_endpoint automatically looks up equivalent identifiers via xref relationships in the graph and retries. This handles cases where GILDA grounds to one namespace (e.g., MESH) but the relevant data is indexed under another (e.g., DOID).

Caching

All call_endpoint results are cached via a diskcache.FanoutCache:

• Cross-process safe — SQLite backend, works with gunicorn multi-worker
• LRU eviction — configurable max size (default 2GB)
• Per-key TTL — default 1 hour, schema cached for 24 hours
• Request coalescing — concurrent identical requests share a single Neo4j query
• Cache stores raw results; sort, field projection, and pagination are applied post-cache

Safety

• Validation layer prevents all write/mutate operations (DELETE, CREATE, MERGE, SET, REMOVE, DROP, DETACH)
• Parameterized queries prevent injection attacks
• Neo4j execute_read() enforces read-only semantics at the driver level

Token-Aware Pagination

Large result sets are automatically truncated with continuation hints:

{
  "results": [...],
  "pagination": {
    "total": 1500,
    "returned": 127,
    "has_more": true,
    "next_offset": 127
  }
}

Use estimate=True to probe query cost before fetching:

call_endpoint("get_drugs_for_target", '{"target": "EGFR"}', estimate=True)
# → {result_count: 78, token_estimate_full: 15200, fields_available: [...], sample: [...]}

Then fetch with projection:

call_endpoint("get_drugs_for_target", '{"target": "EGFR"}', fields=["db_ns", "db_id", "name"], limit=50)

Configuration

Neo4j Credentials

export INDRA_NEO4J_URL="bolt://localhost:7687"
export INDRA_NEO4J_USER="neo4j"
export INDRA_NEO4J_PASSWORD="your-password"

Or configure in ~/.config/indra/config.ini (the standard INDRA config file).

Transport Security (Required)

Variable	Required	Description
MCP_ALLOWED_HOSTS	Yes	Comma-separated allowed hosts (e.g., localhost,discovery.indra.bio)
MCP_ALLOWED_ORIGINS	Yes	Comma-separated allowed origins (e.g., http://localhost:3000,https://discovery.indra.bio)

Cache

Variable	Default	Description
INDRA_CACHE_DIR	~/.cache/indra_cogex_mcp	Cache directory path
INDRA_CACHE_SIZE_MB	2048	Max cache size in MB (LRU eviction beyond this)
INDRA_CACHE_TTL	3600	Default TTL in seconds
INDRA_CACHE_SHARDS	4	FanoutCache shards (higher = better concurrency, more file handles)

HTTP Mode

Variable	Default	Description
MCP_HOST	0.0.0.0	Host to bind in HTTP mode
MCP_PORT	8000	Port to bind in HTTP mode

Development

git clone https://github.com/gyorilab/indra_agent.git
cd indra_agent
pip install -e ".[dev]"

# Run tests
pytest tests/

# Run specific test file
pytest tests/mcp_server/test_gateway_tools.py -v

Dependencies

• indra_cogex — INDRA CoGEx knowledge graph client
• mcp>=1.2.0 — Model Context Protocol SDK (requires 1.2.0+ for transport security)
• gilda — Biomedical entity grounding
• diskcache>=5.6 — Persistent caching with LRU eviction
• pydantic>=2.0 — Data validation
• click>=8.0 — CLI framework
• starlette>=0.27.0 — ASGI framework
• uvicorn>=0.20.0 — ASGI server (HTTP mode)
• jinja2>=3.0.0 — Template engine

License

BSD-2-Clause