hyperdb-mcp

An MCP (Model Context Protocol) server that turns the Hyper columnar database into an instant SQL analytics engine. Data flows in from other MCP plugins or files, lands in Hyper automatically, and becomes queryable with SQL — no setup, no schema files, no database management.

Built on the pure-Rust hyperdb-api crate for maximum performance: 22M+ rows/sec inserts, 18M+ rows/sec queries, constant memory for billion-row results.

Why

LLMs are powerful at reasoning but cannot natively crunch millions of rows. This plugin bridges that gap: another MCP tool produces data, the LLM passes it to hyperdb-mcp, Hyper ingests it and makes it SQL-queryable, the LLM runs analytical SQL, and results come back as JSON. Optionally export to CSV, Parquet, Apache Iceberg, Arrow IPC, or .hyper (opens directly in Tableau Desktop).

Features

• Zero setup — HyperProcess auto-starts the Hyper server
• Any data in — JSON, CSV, Parquet, Arrow IPC, Apache Iceberg; schema inferred or exact
• SQL at scale — thousands to billions of rows
• Data out — export to CSV, Parquet, Apache Iceberg, Arrow IPC, or .hyper (Tableau Desktop-ready)
• One-shot queries — query_file("/tmp/sales.csv", "SELECT ...") — single call, zero management
• Persistent workspace — load multiple tables, JOIN across them, persist across sessions
• Read-only safe mode — --read-only flag for safe deployment
• Schema resources — auto-discover table schemas via resources/list
• Guided prompts — analyze-table, compare-tables, data-quality, suggest-queries
• Inline charts — bar/line/scatter/histogram as PNG or SVG
• Incremental ingest — watch_directory monitors for .ready sentinel files
• Performance telemetry — every response includes throughput stats
• Smart schema inference — exact (Arrow/Parquet), structural (JSON), heuristic (CSV) with full-file numeric widening
• Pre-ingest file inspection — inspect_file dry-runs the same inference without touching Hyper so LLMs can build safe schema overrides in one shot
• Partial schema overrides — supply just the columns you want to correct (e.g. {"population":"BIGINT"}) — the rest keep their inferred type
• Rich resource surface — workspace readme, per-table JSON and CSV samples, and one JSON + one CSV resource per table so LLMs can orient themselves via resources/list without any tool calls
• Saved queries — register named read-only SQL with save_query; each query becomes hyper://queries/{name}/definition (metadata) + hyper://queries/{name}/result (live re-run). Persisted in --workspace mode, session-only otherwise
• Live resource-update notifications — MCP clients can resources/subscribe to any hyper://... URI; the server fires notifications/resources/updated after every ingest, DDL, watcher event, or saved-query mutation

Installation

From npm

Requirement: Node.js v21 or later. Earlier versions ship an older npx whose argument parsing is incompatible with the npx -y hyperdb-mcp invocation in the MCP config below. If you're on an older Node, see Upgrading Node.js with nvm below.

npm install -g hyperdb-mcp

The npm package bundles both the hyperdb-mcp binary and the hyperd database server — no additional setup required.

Upgrading Node.js with nvm

nvm (Node Version Manager) makes it easy to install and switch between Node.js versions.

macOS / Linux (nvm-sh/nvm):

# install nvm if you don't have it
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash

# install and use the latest LTS (>= 21)
nvm install --lts
nvm use --lts
node --version    # should report v22.x.x or newer

Windows (coreybutler/nvm-windows): download the installer, then in a new shell:

nvm install lts
nvm use lts
node --version

After upgrading, restart your MCP client so it picks up the new Node binary on PATH.

Building from Source

cd hyper-api-rust
cargo build --release -p hyperdb-mcp

The binary is at target/release/hyperdb-mcp.

When building from source the hyperd executable is not bundled, so you'll need to provide one. The easiest path is the companion hyperdb-bootstrap CLI, which downloads a matching pinned hyperd for your platform:

cargo install hyperdb-bootstrap
hyperdb-bootstrap download                # installs into ./.hyperd/current/
export HYPERD_PATH="$PWD/.hyperd/current" # or pass via your MCP config

hyperdb-bootstrap also has a library API if you'd rather wire the download into your own build script — see its README. If you already have hyperd elsewhere (Tableau Hyper API for C++/Python/Java ships one), point HYPERD_PATH at it or add it to your PATH.

MCP Client Configuration

Each AI tool reads MCP server config from a different file but uses the same JSON shape. The base config block using npx (recommended):

{
  "mcpServers": {
    "HyperDB": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "hyperdb-mcp"]
    }
  }
}

Or if you built from source:

{
  "mcpServers": {
    "HyperDB": {
      "type": "stdio",
      "command": "/path/to/hyperdb-mcp",
      "env": {
        "HYPERD_PATH": "/path/to/hyperd"
      }
    }
  }
}

For a persistent workspace (tables survive across sessions), add "args":

"args": ["--workspace", "/path/to/my-project.hyper"]

This is still experimental and will only work with only one session at a time since the Hyper database is locked by Hyper. Each session is isolated and has its own Hyper instance running. Future work will allow multiple sessions to share the same database but requires work to spin up a shared Hyper instance.

Claude Code / AI Suite

Create or edit ~/.claude/.mcp.json (global) or .mcp.json in the project root (project-scoped). Use the base config block above.

After adding the config:

1. Start a new Claude Code session. You'll be prompted to approve the server on first use.
2. Auto-approve tools (optional): Add "mcp__HyperDB__*" to the permissions.allow array in ~/.claude/settings.json.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows). Use the base config block above.

Cursor

Edit ~/.cursor/mcp.json (global) or .cursor/mcp.json (project root). Use the base config block above.

Other MCP Clients

Any tool that supports the MCP stdio transport can use this server. Point it at the hyperdb-mcp binary and set HYPERD_PATH in the environment.

MCP Tools

One-Shot Tools

query_data

Ingest inline data and run a SQL query in a single call.

query_data(data: '[{"region":"West","revenue":1200},...]', sql: 'SELECT region, SUM(revenue) FROM data GROUP BY region')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | data | string | yes | JSON array of objects, or CSV text | | sql | string | yes | SQL query to run against the data | | format | string | no | "json" or "csv" — auto-detected if omitted | | table_name | string | no | Table name for use in SQL — defaults to "data" | | schema | object | no | Partial column-name → type map (see Schema Overrides) |

query_file

Ingest a file and run a SQL query in a single call. Streams from disk — handles files of any size.

query_file(path: '/tmp/sales.parquet', sql: 'SELECT TOP 10 * FROM sales ORDER BY amount DESC')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | path | string | yes | Path to CSV / JSON / JSONL / Parquet / Arrow IPC file | | sql | string | yes | SQL query to run | | table_name | string | no | Table name — defaults to filename stem | | schema | object | no | Partial column-name → type map (see Schema Overrides) |

Workspace Tools

load_data

Load inline data into a named workspace table.

load_data(table: 'customers', data: '[{"id":1,"name":"Alice"},...]')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | table | string | yes | Table name | | data | string | yes | JSON array of objects, or CSV text | | format | string | no | "json" or "csv" — auto-detected | | mode | string | no | "replace" (default) or "append" | | schema | object | no | Partial column-name → type map (see Schema Overrides) |

load_file

Load a file into a named workspace table.

load_file(table: 'orders', path: '/tmp/orders.csv')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | table | string | yes | Table name | | path | string | yes | Path to CSV / JSON / JSONL / Parquet / Arrow IPC file | | mode | string | no | "replace" (default) or "append" | | schema | object | no | Partial column-name → type map (see Schema Overrides) |

When you're unsure of the right types — or recovering from a previous SCHEMA_MISMATCH — call inspect_file first. It reports the exact schema load_file would use plus per-column min / max / null_count so you can build a minimal, correct override in one shot.

load_iceberg

Load an Apache Iceberg table into a named workspace table. Pass the absolute path to the Iceberg table root (the directory containing metadata/ and data/); hyperd's native Iceberg reader derives the schema and resolves the snapshot.

load_iceberg(table: 'sales', path: '/lake/warehouse/db/sales')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | table | string | yes | Target Hyper table name | | path | string | yes | Absolute path to the Iceberg table root directory | | mode | string | no | "replace" (default) or "append" | | metadata_filename | string | no | Pin a specific snapshot, e.g. "v2.metadata.json". Omit for latest. | | version_as_of | integer | no | Pin a snapshot by version number |

Schema overrides are not accepted — hyperd derives the schema from the Iceberg table metadata.

query

Run a read-only SQL query against the workspace. Accepts SELECT, WITH, EXPLAIN, SHOW, VALUES. For DDL/DML use execute.

query(sql: 'SELECT c.name, SUM(o.amount) FROM orders o JOIN customers c ON o.customer_id = c.id GROUP BY c.name')

execute

Execute a mutating SQL statement: CREATE TABLE, INSERT, UPDATE, DELETE, DROP TABLE, ALTER, COPY, etc. Returns the affected row count. Disabled in read-only mode.

execute(sql: 'CREATE TABLE archived_orders AS SELECT * FROM orders WHERE year < 2024')

describe

List all workspace tables with their schemas, column types, and row counts.

sample

Return the schema, total row count, and first N rows of a table in a single call.

sample(table: 'orders', n: 10)

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | table | string | yes | Table name | | n | int | no | Rows to return (default: 5, clamped to 1..=100) |

Diagnostics

inspect_file

Dry-run schema inference on a CSV, Parquet, or Arrow IPC file without ingesting it. Returns the exact schema load_file / query_file would use (including the full-file numeric widening pass) plus per-column min, max, null_count, and sample_values. Nothing is written to Hyper and hyperd is not even started.

Use it before load_file whenever you are unsure about types, or after a SCHEMA_MISMATCH failure to pick the right widening. The LLM can feed the reported type + min / max directly into a partial schema override on the subsequent load_file call.

inspect_file(path: '/tmp/owid-population.csv')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | path | string | yes | Path to CSV / JSON / JSONL / Parquet / Arrow IPC file | | sample_rows | int | no | Sample values / rows per column (default 5, clamped 1..=50) |

Response shape:

{
  "file_format": "csv",
  "row_count": 63000,
  "file_size_bytes": 4831204,
  "columns": [
    { "name": "Entity",     "type": "TEXT",   "nullable": true, "null_count": 0,   "sample_values": ["Afghanistan", ...] },
    { "name": "Year",       "type": "INT",    "nullable": true, "null_count": 0,   "min": 1800, "max": 2023, "sample_values": ["1800", ...] },
    { "name": "Population", "type": "BIGINT", "nullable": true, "null_count": 12,  "min": 500, "max": 8002572256, "sample_values": ["4000000", ...] }
  ],
  "sample_rows": [ { "Entity": "Afghanistan", "Year": "1800", "Population": "2805829" } ]
}

sample_values and sample_rows are always strings, regardless of the inferred column type — they report what the file contains on disk, before any type coercion, so the LLM can compare the raw text against min / max when building a schema override. Use type (and min / max) for the typed view; use sample_values for the raw view.

Saved Queries

Register a named read-only SQL query once; read its live result as many times as you like via a resource URI. Useful for dashboard-style recurring views and for giving LLMs a stable "bookmark" set of key queries that resources/list advertises up front.

Each saved query produces two resources:

• hyper://queries/{name}/definition — the stored SQL plus metadata (description, created_at) as JSON.
• hyper://queries/{name}/result — re-runs the SQL on every read and returns { name, result: [...], stats: {...} }.

Persistence: queries saved while --workspace <path> is set are stored in the _hyperdb_saved_queries meta-table inside the .hyper file and survive server restarts. In ephemeral workspaces they live only for the lifetime of the server process.

save_query

save_query(name: 'top_5_customers', sql: 'SELECT customer, SUM(amount) AS total FROM orders GROUP BY customer ORDER BY total DESC LIMIT 5', description: 'Biggest spenders this year')

| Parameter | Type | Required | Description | |---|---|---|---| | name | string | yes | Unique identifier used as the URI path component | | sql | string | yes | Read-only SQL (SELECT / WITH / EXPLAIN / SHOW / VALUES) | | description | string | no | Human-friendly summary |

Duplicate names are rejected with INVALID_ARGUMENT — use delete_query first if you intend to overwrite. Non-read-only SQL is rejected with SQL_ERROR. Disabled in read-only mode.

delete_query

delete_query(name: 'top_5_customers')

| Parameter | Type | Required | Description | |---|---|---|---| | name | string | yes | Name of the saved query to remove |

Returns { "deleted": true } when the query existed, { "deleted": false } when it did not (no error on unknown names). Disabled in read-only mode.

Export Tools

export

Write query results or a table to a file.

export(table: 'orders', path: '~/Desktop/orders.parquet', format: 'parquet')
export(sql: 'SELECT ...', path: '~/Desktop/analysis.hyper', format: 'hyper')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | sql | string | no | Query to export (if omitted, exports whole table) | | table | string | no | Table name (used if sql omitted) | | path | string | yes | Output file path | | format | string | yes | "csv", "parquet", "iceberg", "arrow_ipc", or "hyper" |

The "hyper" format produces a .hyper file that opens directly in Tableau Desktop.

Visualization

chart

Render a chart from a SQL query and return it inline as an image.

chart(sql: 'SELECT product, SUM(revenue) as total FROM sales GROUP BY product', chart_type: 'bar', x: 'product', y: 'total', title: 'Revenue by Product')

| Parameter | Type | Required | Description | |-----------|------|----------|-------------| | sql | string | yes | Read-only SQL query returning the data to plot | | chart_type | string | yes | bar, line, scatter, or histogram | | x | string | yes* | X-axis column (for histogram, the value column) | | y | string | yes* | Y-axis column (not required for histogram) | | series | string | no | Grouping column for multi-series plots | | title | string | no | Chart title | | format | string | no | png (default) or svg | | width | int | no | Pixels (default 800, clamped 200..4096) | | height | int | no | Pixels (default 480, clamped 150..4096) | | bins | int | no | Histogram bins (default 20, clamped 1..500) |

Returns an ImageContent (base64 PNG or SVG) plus a stats JSON block.

Incremental Ingest

watch_directory / unwatch_directory

Monitor a directory for data files and auto-append them to a target table.

watch_directory(path: '/tmp/inbox', table: 'events')
unwatch_directory(path: '/tmp/inbox')

Producer protocol (.ready sentinel):

1. Write data file (e.g. foo.csv) and close it.
2. Create a zero-byte companion foo.csv.ready — this is the atomic signal.
3. Poll for the absence of foo.csv.ready to confirm the watcher is done.

On success, both files are deleted. On failure, both are moved to failed/ with a .error JSON file.

Key properties:

• One directory, one table, append mode — files must match the target schema.
• Initial sweep — pre-existing .ready files are processed immediately.
• Read-only mode — watch_directory is blocked; unwatch_directory is always allowed.
• Cleanup — dropping the server or calling unwatch_directory terminates the background thread.

Utility Tools

status

Returns plugin health, workspace mode, table count, total rows, disk usage, read-only flag, and active directory watchers with per-watcher stats.

MCP Resources

The server exposes workspace state as MCP Resources, discoverable via resources/list. Each resource advertises its own MIME type so clients can route it appropriately (LLM context vs. file download vs. chart).

| URI | MIME | Content | |-----|------|---------| | hyper://workspace | application/json | Workspace mode, table count, total rows, disk usage | | hyper://tables | application/json | Full list of tables with schemas and row counts | | hyper://readme | text/markdown | Workspace overview as markdown: table catalog, related resources per table, and tool hints for a cold-started LLM | | hyper://tables/{name}/schema | application/json | Columns, types, nullability, and row count for one table | | hyper://tables/{name}/sample | application/json | First 5 rows of a table as JSON, with schema | | hyper://tables/{name}/csv-sample | text/csv | First 20 rows of a table as CSV, header-first | | hyper://queries/{name}/definition | application/json | Stored SQL + metadata for a saved query | | hyper://queries/{name}/result | application/json | Live result of a saved query — re-runs on every read |

Resource templates (discoverable via resources/templates/list):

• hyper://tables/{name}/schema
• hyper://tables/{name}/sample
• hyper://tables/{name}/csv-sample
• hyper://queries/{name}/definition
• hyper://queries/{name}/result

The internal _hyperdb_saved_queries meta-table used to persist saved queries is deliberately hidden from resources/list and hyper://tables — callers see only user-visible data tables.

Resource-update notifications

HyperDB advertises both the resources.subscribe and resources.listChanged capabilities in its initialize response. Clients can subscribe to any hyper://... URI via resources/subscribe and will then receive notifications/resources/updated messages whenever the server detects a change, without polling.

The server fires targeted updates for the URIs affected by each kind of mutation:

| Trigger | Updated URIs | resources/list_changed? | |---|---|---| | load_data / load_file (replace mode) | hyper://workspace, hyper://tables, hyper://readme, per-table schema + sample + csv-sample | Yes | | load_data / load_file (append mode) | Same per-table + summary URIs | No ¹ | | watch_directory ingest of a .ready pair | Same per-table + summary URIs | No ¹ | | execute (INSERT / UPDATE / DELETE) | Workspace summary URIs | No | | execute (CREATE / DROP / ALTER / TRUNCATE / RENAME) | Workspace summary URIs | Yes | | save_query | (none per-URI) | Yes — two new hyper://queries/{name}/... resources | | delete_query | hyper://queries/{name}/definition, hyper://queries/{name}/result | Yes — two resources disappeared |

¹ Append-mode ingest (both load_* and the watcher) auto-creates the target table when it doesn't exist, but does not fire list_changed for that creation. Clients that need to discover watcher-created tables should re-read hyper://tables after subscribing, or use the per-table updated notification as a trigger to refresh their list. Tracked in DEVELOPMENT.md as tech debt.

Notifications are fire-and-forget — send failures (typically due to a client disconnect) are logged at the debug level and the registry prunes dead peers lazily. This keeps mutation paths fast and free of back-pressure concerns.

All JSON-typed resources return a pretty-printed object; Markdown and CSV resources are returned verbatim.

MCP Prompts

Four guided analytical workflows registered as MCP Prompts.

| Prompt | Arguments | What it does | |--------|-----------|--------------| | analyze-table | table | Schema walkthrough, column statistics, data quality flags | | compare-tables | table_a, table_b | Schema alignment, JOIN key suggestions, analytical opportunities | | data-quality | table | Systematic NULL / duplicate / cardinality / outlier checks | | suggest-queries | table, goal? | 5 analytical SQL queries with explanations, optionally goal-guided |

Read-Only Mode

hyperdb-mcp --workspace ~/analytics.hyper --read-only

• Allowed: query, query_data, query_file, describe, sample, inspect_file, status, export
• Blocked: execute, load_data, load_file, watch_directory, save_query, delete_query — return READ_ONLY_VIOLATION
• Resources, prompts, and resource subscriptions work normally — read-only clients can still subscribe to hyper://... URIs and receive notifications when other (non-read-only) connections mutate state

The query tool also enforces read-only at the SQL level — only SELECT/WITH/EXPLAIN/SHOW/VALUES are accepted.

Data Flow Patterns

• Small data (LLM relay): For <10K rows. The LLM gets data from another plugin and passes it inline via query_data.
• Large data (file intermediary): For thousands to billions of rows. Source plugin exports to a file, the LLM calls query_file. Data never enters the LLM context — constant memory regardless of file size.

Schema Inference

Three tiers, chosen automatically based on the data source:

| Tier | Source | How | |------|--------|-----| | Exact | Arrow IPC, Parquet | Schema read from file metadata. Types preserved exactly. | | Structural | JSON | All objects scanned. Per-column type widening: Int → BigInt → Double. Mixed types → TEXT. | | Heuristic | CSV | Header row for names, first 1,000 rows sampled for types. A second full-file streaming pass then widens numeric columns if needed (INT → BIGINT → NUMERIC(38,0); INT/BIGINT → DOUBLE PRECISION if any later row contains a decimal). |

JSON file shapes. load_file and query_file accept two JSON representations and auto-detect between them from the first non-whitespace byte: a top-level JSON array of objects (e.g. [{...}, {...}]) or newline-delimited JSON (JSONL / NDJSON — one JSON object per line, the format hyperd's own logs use). Blank lines are tolerated. Malformed JSONL surfaces a SCHEMA_MISMATCH error naming the offending line number.

Content sniffing for unknown extensions. Files with extensions the dispatcher doesn't recognize (.log, .txt, no extension at all) are classified by peeking at the first non-whitespace byte: [ or { routes to JSON, anything else to CSV. This means hyperd's raw .log files load through load_file directly, no rename or preprocessing required. Binary formats (.parquet, .arrow, .ipc, .feather, .pq) always win by extension since they're not text-sniffable. inspect_file uses the exact same dispatcher so its report can never disagree with what load_file would do.

CSV NULL handling. Unquoted empty cells (,,) load as SQL NULL — matching PostgreSQL's CSV convention and inspect_file's null_count diagnostics. Quoted empty strings (,"",) load as the literal empty string. This means downstream WHERE col IS NULL works directly without a defensive OR col = '' clause.

The full-file CSV widening pass specifically protects against the "big value hidden at the end of the file" failure mode — e.g. an aggregate row whose population is ~8 billion tucked in after 60 000 country-sized rows. Without it, the first-pass sample would pick INT and the COPY would fail with SCHEMA_MISMATCH / SQLSTATE 22003 mid-ingest.

For implementation details (widening rules, type mapping tables), see the module docs in src/schema.rs and src/ingest_arrow.rs.

Schema Overrides

Every data-in tool (query_data, query_file, load_data, load_file) accepts an optional schema parameter: a partial map from column name to Hyper SQL type.

{ "schema": { "population": "BIGINT", "order_date": "DATE" } }

Semantics:

• Keys are matched to columns by name (case-sensitive). Column order in the JSON object does not need to match the file — the inferred order from the file is preserved.
• Columns not listed in the override keep their inferred type. You only specify the columns you want to correct.
• Unknown column names and unknown type strings are rejected up front with a SCHEMA_MISMATCH error that lists the real column names, so the LLM can self-correct without another round-trip.
• Supported type strings: INT, BIGINT, NUMERIC(p,s) (e.g. NUMERIC(38,0) or NUMERIC(12,2)), DOUBLE PRECISION, TEXT, BOOL, DATE, TIMESTAMP.

Recommended workflow for unfamiliar data:

1. Call inspect_file → read the reported type + min / max per column.
2. For any column whose max exceeds its inferred type's range, or where you want stricter parsing than CSV heuristics give, build a partial override.
3. Pass it to load_file / query_file.

SQL Dialect

Hyper uses the Salesforce Data Cloud SQL dialect (PostgreSQL-compatible with extensions). Supports SELECT, JOINs, subqueries, CTEs, window functions, aggregations, DDL, DML, and COPY FROM.

Full reference: Data Cloud SQL Reference

CLI Reference

hyperdb-mcp [OPTIONS]

Options:
  --workspace <PATH>    Path to the `.hyper` workspace file for persistent mode (omit for ephemeral)
  --read-only           Disable mutating tools (execute, load_data, load_file, save_query, delete_query, watch_directory)
  --bare                Skip MCP-managed auxiliary tables (`_table_catalog`) and force saved queries into in-memory storage, even with --workspace

Environment:
  HYPERD_PATH           Path to hyperd binary (auto-detected if on PATH)

Error Handling

Errors include a machine-readable code and a suggestion:

| Code | When | Recovery | |---|---|---| | HYPERD_NOT_FOUND | hyperd not found | Set HYPERD_PATH or install Hyper | | FILE_NOT_FOUND | File path doesn't exist | Verify the path | | UNSUPPORTED_FORMAT | Unrecognized file type | Specify format explicitly | | SCHEMA_MISMATCH | Data doesn't match inferred types, numeric overflow (SQLSTATE 22003), or invalid text for target type (SQLSTATE 22P02) | Call inspect_file then retry with a partial schema override (e.g. {"population":"BIGINT"} or {"id":"TEXT"}) | | SQL_ERROR | Invalid SQL | Fix the query | | TABLE_NOT_FOUND | Table doesn't exist | Use describe to list tables | | READ_ONLY_VIOLATION | Mutating op in read-only mode | Use query_* / inspect_file, or restart without --read-only | | CONNECTION_LOST | hyperd crashed or wire protocol desynchronized | Retry — the server tears down the engine and reconnects on the next call |

Server-returned errors include a machine-readable code, a message, and a suggestion with concrete retry guidance. The SCHEMA_MISMATCH suggestion for an overflow names the workflow directly: "call inspect_file, then retry with a partial schema override", so the LLM does not need to infer the recovery steps from the SQLSTATE alone.

Troubleshooting

Tools not discovered by the client — Verify the initialize response advertises "capabilities": {"tools": {}}. Pipe a raw initialize JSON-RPC request to the binary to check.

Server registered but tools not callable (Claude Code) — Add "mcp__HyperDB__*" to the permissions.allow array in ~/.claude/settings.json.

hyperd not found — Set HYPERD_PATH in the MCP server's env config, or place hyperd on your PATH.

Related Documentation

• Main README — Getting started with the Hyper API
• hyperdb-api — Core Rust API (sync/async connections, inserter, query)
• DEVELOPMENT.md — Internal architecture, design decisions, contributor guide
• ROADMAP.md — Forward-looking design sketches for features that aren't built yet
• Design Spec — Full design document