Official Solana MCP Server
OfficialLeverages Databricks Vector Search for semantic retrieval of documentation chunks, with optional cross-encoder reranking via Model Serving, and falls back to SQL queries on Databricks Delta tables.
Provides tools to search and retrieve up-to-date documentation across the Solana ecosystem, including semantic search and program autofixing for Anchor and Pinocchio Rust programs.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
Official Solana MCP Server
Try it out at https://mcp.solana.com !
The official Solana Developer MCP. Purpose: serve up-to-date documentation across the Solana ecosystem to AI agents and developer tooling.
Architecture
Ingestion (
ingestion/): Databricks notebook crawls the sources listed iningestion/sources.yaml, chunks markdown, and writes embeddings into a Delta-backed Vector Search index.Retrieval (
lib/services/databricks/): MCP tools query the index via the Databricks managed AI Search MCP server, which retrieves and reranks results server-side.get_documentationfalls back to a SQL read of thedocs_chunksDelta table when a source has no publishedllms.txt.Server (
lib/index.ts,server/cloudrun.ts): Exposes five tools over MCP —Solana_Expert__Ask_For_HelpandSolana_Documentation_Search(semantic RAG),list_sectionsandget_documentation(canonical-spec retrieval modelled after the Svelte AI server), andprogram_autofixerfor Anchor and Pinocchio Solana program Rust checks. Deployed on Cloud Run as a containerised Node service frontingmcp.solana.com; calls the Databricks workspace REST API directly for retrieval.Section catalogue (
ingestion/sources.yaml→lib/sources.generated.ts):pnpm gen:sourcesemits a typed catalogue of every source, its tags from a closed 21-section taxonomy, anduse_caseskeywords used bylist_sectionsto route the agent.Analytics (
lib/services/s3/analytics.ts): Tool calls + initializations are buffered in memory and uploaded as JSONL objects to the S3 prefix configured byANALYTICS_S3_URI.
Related MCP server: solagent-mcp
CLI
cli/ ships @solana/mcp, a dependency-free command-line client for agents and scripts without MCP support. Each command is one tools/call against mcp.solana.com, so it needs no credentials.
npx @solana/mcp search "derive a PDA with Anchor"
npx @solana/mcp ask "why does my CPI fail with PrivilegeEscalation?"
npx @solana/mcp sections
npx @solana/mcp docs anchor-docs frameworks
npx @solana/mcp check programs/vault/src/lib.rs # or: cat lib.rs | npx @solana/mcp check -Add --json for the raw tool result, and --framework anchor|pinocchio to check when the framework is not detected from imports. Exit codes: 0 ok, 1 check found a syntax error or an undismissed critical/high issue, 2 usage, network, or server error. Set SOLANA_MCP_URL to point at another server, e.g. http://localhost:8080/mcp.
To release, bump version in cli/package.json, merge, then run the Publish CLI workflow from main (dry run first). It publishes to npm through Trusted Publishing and creates a cli-v<version> GitHub release.
Local Development
pnpm install
cp .env.example .env # set DATABRICKS_HOST + DATABRICKS_TOKEN + DATABRICKS_VS_INDEX
pnpm dev:local
pnpm inspector # connects MCP Inspector at http://127.0.0.1:6274Deploy
Production runs on Cloud Run (mcp.solana.com → server/cloudrun.ts, built via the root Dockerfile). Push to main triggers .github/workflows/deploy-cloudrun.yml, which submits a Cloud Build and rolls the new revision. Runtime env vars (DATABRICKS_HOST, DATABRICKS_TOKEN, DATABRICKS_VS_INDEX, DATABRICKS_WAREHOUSE_ID, ANALYTICS_S3_*, AWS_*) and deploy config (GCP_*, VPC_CONNECTOR) are loaded from the Doppler prd_github config at deploy time.
The Databricks side (databricks.yml) deploys two resources via just deploy:
the daily ingestion job (
crawl_and_index.pynotebook) — crawls sources, MERGEs into Delta, syncs the Vector Search index;the Lakeview dashboard. Analytics source files land in S3 and require downstream ingestion before dashboard consumption.
Per-environment values (catalog, warehouse, index) live in the gitignored prod.yml (see template inline in databricks.yml).
just deploy # builds, pushes ingestion job + dashboardEvals
Per-environment values (catalog, warehouse, index) live in the gitignored prod.yml; supply each variable listed under variables: in databricks.yml.
This server cannot be deployed
Maintenance
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