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GreptimeDB MCP Server

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by GreptimeTeam

describe_table

Retrieve a table's schema, semantic profile, sample rows, and query guidance. Use it to understand an unfamiliar table before writing SQL.

Instructions

Get 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableYes
sample_limitNo
include_samplesNo
include_semanticsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.5.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full disclosure burden and largely succeeds: it explains that the semantic profile is about meaning, not data, defines signal_type/source/pipeline, and cautions that metadata_quality says nothing about telemetry quality and null means unknown. It does not explicitly state read-only behavior, but the 'profile' framing makes that reasonably clear.

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

Conciseness4/5

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

The structure is effective: a front-loaded one-sentence summary, a short usage-routing paragraph, then a focused semantic-field explanation. It is longer than strictly necessary but every section carries useful content, with no filler.

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

Completeness4/5

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

Because an output schema is present, the description needn't spell out return values. The description covers semantic interpretation, null semantics, and sibling routing. The main remaining gaps are sample_limit behavior and an explicit statement of read-only/no-side-effect guarantees.

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?

Schema description coverage is 0%, so the description must compensate. It does add meaning by detailing what the semantic profile contains and mentioning sample rows, which indirectly clarifies include_semantics and include_samples. However, it never directly explains sample_limit, include_samples/include_semantics flag effects, or the required table parameter, leaving a portion of the parameter semantics to inference.

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 object ('Get a table profile') and lists its contents: schema, semantic metadata, sample rows, and guidance. It also positions the tool against the nearest sibling by explicitly saying search_table_semantics is the choice when the right table is unknown, so there is no ambiguity about this tool's distinct role.

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?

It gives an explicit trigger ('Use it when deciding how to query an unfamiliar table') and an explicit exclusion ('When the right table is not known yet, search_table_semantics first'), with a rationale that describing candidates is slower than querying the data. This is strong, actionable routing guidance.

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