dataset_provenanceThe source, the date it was computed, the licence and the citation for the Hydrantly 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, the description carries the behavioral burden. It states what the tool returns and that it is meant for reading, implying a safe, read-only operation. It does not explicitly mention side-effect absence, but the described purpose makes that clear.
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 compact sentences, no filler: the first lists the return content, the second states the intended use. Key information is front-loaded and every sentence earns its place.
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 metadata tool with no output schema, the description is complete. It names the dataset, the specific metadata fields, and the scenario in which the tool should be used, leaving no critical gap for invocation.
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?
There are zero parameters, so the schema already fully covers this dimension. The description correctly focuses on the returned provenance information rather than adding unnecessary parameter detail.
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 identifies a specific resource (the Hydrantly dataset) and a distinct purpose: retrieving source, computed date, licence, and citation. It clearly separates this metadata tool from the sibling data-exploration tools.
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 gives an explicit trigger: 'Read this to attribute a figure correctly.' It does not explicitly name alternatives or exclusions, but the attribution use case is distinct enough for an agent to know when to invoke it.
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 targets a distinct data access pattern: schema, provenance, exact row lookup, fuzzy search, comparisons, top/bottom rows, and numeric stats. There is mild overlap between dataset_row and dataset_compare since both filter by column values, but their intended use cases are clearly differentiated.
All tools share a consistent dataset_ prefix and use snake_case, which makes the set look cohesive. However, the suffixes mix nouns (columns, row, stats, top, provenance) with verbs (compare, search), so the pattern is not perfectly uniform.
Seven tools is a well-scoped size for a single-dataset query server. Each tool covers a meaningful access mode without unnecessary redundancy or overwhelming the agent.
The tool set covers the core data exploration lifecycle: schema discovery, metadata, exact lookup, search, comparison, top/bottom ranking, and summary statistics. It lacks arbitrary multi-condition filtering or grouped aggregations, but those are not clearly required for the stated dataset Q&A purpose.