dataset_provenanceThe source, the date it was computed, the licence and the citation for the Sittingly 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 full burden and does well: it states exactly what information is returned (source, date, licence, citation) and the phrase 'Read this' implies a non-mutating retrieval. It could go further by explicitly saying it has no side effects or requires no input, but for this simple metadata tool the behavior is 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 directive is appended in a single clear clause. Every word 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 tool with no output schema, the description is complete: it identifies the dataset, the fields available, and the correct use case. An agent has everything needed to decide when to call it and what to expect.
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 input schema has zero properties, so there are no parameter semantics to explain. The description reinforces that the tool is a standalone provenance lookup, which is the appropriate baseline for a no-parameter 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 names the exact kind of content the tool provides — source, computed date, licence, and citation for the Sittingly dataset — with an explicit intended action ('attribute a figure correctly'). It clearly distinguishes itself from the sibling tools, which are about columns, search, stats, and row-level data, not provenance.
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?
'Read this to attribute a figure correctly' gives a concrete context for when to call the tool: whenever a figure or result needs citation. It does not explicitly list exclusions or alternatives, but the sibling names make it obvious that this tool is not for data exploration or comparison.
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.
Most tools have clearly distinct purposes: schema discovery, provenance, exact lookup, substring search, stats, and top/bottom ranking are easy to tell apart. However, dataset_row and dataset_compare overlap in that both do exact column-value matching, with compare merely extending row to multiple values, which could cause some selection uncertainty.
All tools share the clean dataset_ prefix and use lowercase snake_case, making the set feel cohesive. The second part mixes nouns (columns, provenance, row, stats, top) with verbs (compare, search), but the pattern is still predictable and readable.
Seven tools is a well-scoped size for a dataset-querying server. Each tool covers a distinct query need without redundancy or bloat, and the count is comfortably within the ideal range.
The server covers the full read-only lifecycle of working with a dataset: schema discovery, provenance attribution, exact lookup, substring search, comparisons, numeric stats, and top/bottom ranking. There are no obvious dead ends for the stated purpose.