dataset_provenanceThe source, the date it was computed, the licence and the citation for the Requly dataset. Read this to attribute a figure correctly.
| 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 behavioral disclosure burden. It implies a read-only action through 'Read this' and lists the returned provenance fields, but it does not explicitly state that there are no side effects, no required authentication, or no special access conditions.
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?
The description is two short sentences with no wasted words. It front-loads the key content (source, date, licence, citation) and adds the practical instruction to use it for correct attribution.
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 simple zero-parameter metadata tool, the description is complete enough. It names the exact provenance fields that will be returned and explains why an agent would need them, so no critical context 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 and the schema coverage is 100%, so there are no parameter semantics to clarify. The baseline for a zero-parameter tool is 4, and the description needs no additional parameter information.
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 clearly states that the tool provides the source, computed date, licence, and citation for the Requly dataset. This is a specific resource plus a distinct metadata purpose, and it is easily distinguishable from sibling tools that focus on columns, rows, search, stats, and comparisons.
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 description explicitly says to read this tool when attributing a figure correctly, giving a clear usage context. It does not explicitly name alternatives or exclusions, but the provenance purpose is sufficiently distinct from the data exploration 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.
Each tool has a distinct query mode: schema discovery, provenance, exact lookup, substring search, ordered multi-value comparison, numeric statistics, and ranking. dataset_row and dataset_search overlap slightly for exact-match cases, but their descriptions clarify the intended use.
All tools consistently use the dataset_ prefix with lowercase snake_case and clear operation names. Minor grammatical inconsistency like dataset_row for plural rows and dataset_top instead of top_rows is present, but the pattern is still predictable.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a distinct question type without redundancy or bloat.
The surface covers schema discovery, provenance, exact/pattern matching, numeric stats, and ranking, which handles most common dataset questions. Missing operations like distinct-value listing or grouped aggregation are minor gaps, not blocking ones.