Dataset columns and shape
dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Retainvo dataset. Call this first to learn the schema.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the Retainvo dataset. Call this first to learn the schema.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It discloses what the tool returns (columns, numeric flags, row count, provenance banner), which is meaningful for a zero-parameter exploration tool, but it never explicitly states the operation is a safe read with no side effects. The disclosure is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with zero waste. The first front-loads the full content scope, and the second delivers a crisp usage directive. Every word earns its place and nothing is redundant with the title.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter schema-exploration tool, the description covers the return contents and the recommended call order, which is nearly everything an agent needs. It could explicitly note the read-only/safe nature, but nothing critical for invoking the tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline of 4 applies — there is no schema burden for the description to compensate for. The description appropriately spends its words on return values instead of parameters, which is the correct allocation for a no-argument tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description enumerates the exact outputs (columns, which are numeric, row count, provenance banner) for the Retainvo dataset, making the resource and scope clear. It distinguishes from siblings by what it returns — schema-level info versus rows (dataset_row), stats (dataset_stats), or search results. A specific verb is implied rather than stated, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The sentence 'Call this first to learn the schema' gives an explicit, actionable usage directive that tells the agent this is the entry-point tool. It lacks explicit exclusions or named alternative tools, but the directive effectively routes the agent to the right first call among the siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools are mostly distinct: columns, provenance, stats, and top handle schema, metadata, aggregates, and sorted rows, while row, compare, and search retrieve rows with clearly different matching semantics. Row and compare could be confused (exact vs any-of) but the descriptions make the distinction explicit.
All tools share the 'dataset_' prefix and snake_case, giving a uniform and predictable family. However, the second element mixes nouns (columns, provenance, row, stats) with verbs (compare, search, top), so it is not a strict verb_noun pattern.
Seven tools is within the ideal range and each tool covers a distinct dataset querying capability. There is no redundancy or bloat, and every tool earns its place for the server's narrow purpose.
The set covers schema discovery, provenance attribution, exact and fuzzy row retrieval, numeric statistics, and top-N ranking. For a read-only dataset exploration server, there are no obvious missing operations or dead ends.