Greptimedb MCP Server vs Influxdb3 MCP Server
A side-by-side look at the Greptimedb MCP Server and Influxdb3 MCP Server MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Greptimedb MCP Server for databases work as a local process; choose Influxdb3 MCP Server for databases as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Greptimedb MCP Server when
- You need dedicated capabilities in the Databases domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You want tools like
execute_sql, describe_table, search_table_semantics.
Choose Influxdb3 MCP Server when
- You need dedicated capabilities in the Databases domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: API key required.
- You have the keys it needs:
INFLUX_MCP_TOOL_PROFILE. - You want tools like
load_database_context, get_help, write_line_protocol.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Databases | Databases |
| Quality signal | 61/100 · Good | 82/100 · Excellent |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | API key required | API key required |
| Pricing | Free | BYOK (pay the provider) |
| Required env vars | None listed | INFLUX_MCP_TOOL_PROFILE |
| Verified owner | Community listing | Verified |
| GitHub stars | 29 | 38 |
| Directory activity | 3 views 0 installs 0 upvotes | 41 views 0 installs 0 upvotes |
| Tools | 15 | 27 |
| Full listing | View Greptimedb MCP Server | View Influxdb3 MCP Server |
Tools & capabilities
Greptimedb MCP Server tools 15
execute_sqlExecute SQL query against GreptimeDB. Please use MySQL dialect. Read-only by default. When the server runs with write mode enabled (--allow-write / GREPTIMEDB_ALLOW_WRITE), destructive SQL (DDL/DML) is also permitted.describe_tableGet a table profile: schema, semantic metadata, sample rows, and guidance. Use it when deciding how to query an unfamiliar table. When the right table is not known yet, search_table_semantics first; describing candidates one by one is slower than querying the data. The semantic profile says what the table means, not what its data says. signal_type is metric, log, trace, or event; source and source_version name the ingestion protocol; pipeline names the schema that shaped the rows; semantic_options carries signal-specific facts such as metric type and unit. metadata_quality describes how the metric type was obtained -- `declared` by the protocol or `inferred` from the name -- and says nothing about telemetry quality. entity_declarations lists the entities the table contributes to the semantic graph. A null or missing semantic field means unknown, not the opposite fact.search_table_semanticsFind tables by observability concept when the right table name is unknown. Searches table names, semantic options, and entity declarations, and ranks tables by how many query terms they matched. Use it before describe_table when the schema is wide or table names do not say what they hold. It searches schema metadata only, never telemetry row values, so it can say which table holds Redis memory usage but not which row belongs to `Redis02`. It ranks on the values in that metadata, not on the schema's own key names, so search for `gauge` or `bytes` rather than `metric type`. Once it returns candidates, query their data or describe one of them; do not describe every candidate in turn. It covers only the database this server is connected to, unlike describe_table, which accepts a schema-qualified name. Only tables carrying a `greptime.semantic.*` option, or one a built-in convention derives a declaration for, are visible here. A table absent from the results may still exist and hold the data, so fall back to SHOW TABLES rather than concluding it is not there.query_semantic_graphQuery the semantic graph: which entities exist and which are related. Use view=summary when the entity and relationship types in this graph are not known yet; it reports them with the endpoint type pairs each relationship connects. With a type or an id already in hand, query entities or relationships directly. The window is required and half-open, [start_time, end_time), over observed_at -- the 60-second bucket an observation was recorded in. Rows are aggregated across the buckets in the window, and the result echoes the window and the limit it used. relationships returns one row per edge per confidence. The database reports confidence 1.0 for a bucket whose client and server spans paired and 0.5 for one where only the client was seen, and it switches request_count, error_count and the durations to whichever population that bucket describes: paired requests timed by the server span, or unmatched clients timed by their own. An edge observed both ways therefore comes back as two rows. unmatched_count reports client spans with no paired server span. Durations are in seconds. entities returns one row per distinct set of attributes, so an entity whose descriptive attributes changed inside the window appears more than once; item_count counts rows, not entities. first_seen and last_seen bound where the row was observed inside this window, not when the entity first existed. Ordering is by type and endpoint. Only `calls` edges carry request, error and duration counts, so pass rel_type=calls to order by error and request count instead. When complete is false, more rows matched than the limit and later types may be absent entirely rather than merely cut short. A missing edge is not evidence that two entities are unrelated: it can also mean the call was not instrumented, was sampled out, or fell outside this window. Entities are not deduplicated across identity schemes, so one process can appear under two ids if two sources named it differently. entity_id_attrs names the attributes an id was assembled from and source_tables names the telemetry tables that witnessed it. Identifiers from alerts and other tools are not graph ids unless a query here returned that exact string. When masking is on, a returned field is hidden if its own name matches a sensitive pattern, and an attribute map is also masked by the names inside it. entities additionally hides entity_id when a masked attribute helped build it; relationships cannot do the same, because its view does not carry attribute names, so such a value can still appear there as src_id or dst_id.health_checkCheck GreptimeDB connection status and server version.execute_tqlExecute TQL query for time-series analysis. TQL is PromQL-compatible - use standard PromQL syntax.query_rangeExecute time-window aggregation query using GreptimeDB's RANGE query syntax.explain_queryAnalyze SQL or TQL query execution plan.list_pipelinesList all pipelines or get details of a specific pipeline.create_pipelineCreate a new pipeline in GreptimeDB.dryrun_pipelineTest a pipeline with sample data without writing to the database. You can test a pipeline in two ways: - Provide 'pipeline' with inline YAML configuration - Provide 'pipeline_name' to test a previously saved pipeline Args: pipeline: Pipeline YAML configuration (inline) pipeline_name: Name of saved pipeline (mutually exclusive with pipeline) data: Test data in JSON/NDJSON format data_type: Optional content type (e.g., 'application/x-ndjson')delete_pipelineDelete a specific version of a pipeline from GreptimeDB.- +3 more on the listing page
Influxdb3 MCP Server tools 27
load_database_contextLoad optional custom database context and documentationget_helpGet help and troubleshooting guidance for InfluxDB operationswrite_line_protocolWrite data using InfluxDB line protocolcreate_databaseCreate a new database (with cloud-specific config options)update_databaseUpdate database configuration (retention for all; maxTables/maxColumns for Cloud Dedicated/Clustered)delete_databaseDelete a database by name (irreversible)execute_queryRun a SQL query against a database (supports multiple formats)query_sqlRun bounded read-only SQL with structured response metadataquery_influxqlRun bounded read-only InfluxQL with structured response metadataget_measurementsList all measurements (tables) in a databaseget_measurement_schemaGet schema (columns/types) for a measurement/tablelist_tablesList tables, also called measurements, in a database- +15 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Greptimedb MCP Server
{
"mcpServers": {
"greptimeteam-greptimedb-mcp-server": {
"command": "uvx",
"args": [
"greptimedb-mcp-server"
]
}
}
}
Influxdb3 MCP Server
{
"mcpServers": {
"influxdata-influxdb3-mcp-server": {
"command": "npx",
"args": [
"-y",
"usage"
],
"env": {
"INFLUX_MCP_TOOL_PROFILE": "YOUR_INFLUX_MCP_TOOL_PROFILE_HERE"
}
}
}
}
Frequently asked questions
Greptimedb MCP Server is categorized under Databases and uses a local stdio subprocess. Influxdb3 MCP Server belongs to Databases and uses a local stdio subprocess. Pick Greptimedb MCP Server for databases work and Influxdb3 MCP Server for databases.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Greptimedb MCP Server is listed as free. Auth: api key required. Influxdb3 MCP Server is bring-your-own-key: usage is billed by the API provider you supply a key for. Auth: api key required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add greptimeteam-greptimedb-mcp-server and influxdata-influxdb3-mcp-server as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Greptimedb MCP Server has a quality signal of 61/100 (Good) with 3 views, 0 installs and 29 GitHub stars. Influxdb3 MCP Server has 82/100 (Excellent) with 41 views, 0 installs and 38 GitHub stars.
