dataset_provenanceThe source, the date it was computed, the licence and the citation for the Subbielane 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 disclosure burden. It conveys that this is a read-only provenance lookup by listing returned metadata and instructing the agent to read it. It does not explicitly state 'no side effects', but the content and instruction make that sufficiently 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 short sentences with no filler. The content list is front-loaded and the usage instruction is placed second, making each sentence earn 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 parameterless provenance tool with no output schema, the description fully covers what the agent needs: the fields returned and the reason to call it. Nothing essential 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?
There are zero parameters and the schema is empty, so the baseline is 4. The description adds no parameter details, but none are needed.
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?
States a specific resource (the Subbielane dataset) and the exact content delivered: source, computed date, licence, and citation. This differentiates it clearly from sibling tools like dataset_columns or dataset_stats.
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 tells the agent when to use it: 'Read this to attribute a figure correctly.' It does not list exclusions or name alternatives, but the context is clear and self-sufficient.
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 operation on the Subbielane dataset: schema, provenance, exact row match, fuzzy search, statistics, top/bottom, and value-list comparison. Some mild overlap exists between dataset_search, dataset_row, and dataset_compare for lookups, but their matching semantics are clearly differentiated.
All tools follow the consistent 'dataset_' prefix followed by a short, descriptive noun or verb. The naming convention is uniform and predictable, making it easy to infer each tool's purpose.
Seven tools is a well-scoped number for a dataset-querying server. Each tool covers a distinct query pattern without redundancy or bloat.
The tool surface covers the core dataset exploration needs: schema, provenance, exact lookup, search, statistics, extremes, and comparisons. A minor gap is the lack of a tool to list all rows or paginate through the dataset without a filter, but most practical questions can be answered.