GreptimeDB MCP Server

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search_table_semantics

Find tables by observability concept when the exact table name is unknown. Searches table names and semantic metadata, ranking matches to identify which table holds a metric like 'Redis memory usage'.

Instructions

Find 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.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`limit`	No
`query`	Yes
`signal_type`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.6.0Sep 15, 2026

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full disclosure burden and does so exceptionally. It states the search is metadata-only, never reads telemetry row values; it clarifies ranking is based on metadata values, not key names; and it discloses coverage limits and the possibility of false negatives. This is rich behavioral context well beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Although long, every sentence adds unique operational value: purpose, matching behavior, usage timing, ranking caveat, follow-up guidance, scope boundary, and visibility limitation. The most important instruction is front-loaded, and there is no filler or tautology.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and no annotations, the description covers purpose, scope, limitations, alternative tools, follow-up actions, and fallback behavior. Since an output schema exists, the description need not explain return values, and nothing essential for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must compensate. It gives strong guidance for the `query` parameter ('search for `gauge` or `bytes` rather than `metric type`'), but it never explains `signal_type` or `limit`. This leaves meaning for two of three parameters under-specified, so the compensation is only partial.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Find tables by observability concept when the right table name is unknown.' It clearly distinguishes itself from describe_table by explaining it searches schema metadata and ranks matches, so an agent can tell what the tool does without reading the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is explicitly prescribed: 'Use it before describe_table when the schema is wide or table names do not say what they hold.' It also gives direct follow-up guidance ('query their data or describe one of them; do not describe every candidate in turn') and an explicit fallback ('fall back to SHOW TABLES'), making when-to-use and when-not-to-use unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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