dataset_provenanceThe source, the date it was computed, the licence and the citation for the Termslane 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 of behavioral disclosure. It clearly lists what information is returned and implies a read-only operation, but it does not explicitly state that it has no side effects or what happens if provenance data is unavailable. This is acceptable for a trivial zero-parameter tool but not fully self-sufficient.
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 carry all essential information, front-loading the content fields and ending with the actionable use case. There is no filler, redundancy, or unnecessary detail.
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, read-only metadata lookup with no output schema, the description is complete: it states the data source, the fields returned (source, date, licence, citation), and the use case. Nothing else is required for an agent to invoke it correctly.
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 description does not need to explain inputs. The baseline of 4 applies, and the description adds useful context about the returned provenance fields, which is the only semantics an agent needs.
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 states a specific resource (the Termslane dataset) and the exact content provided (source, computation date, licence, citation), plus the intended purpose ('attribute a figure correctly'). This makes it self-evidently distinct from siblings like dataset_row or dataset_stats, which return data values rather than provenance metadata.
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?
It gives a clear when-to-use instruction: read this when you need to attribute a figure correctly. It does not explicitly name alternatives or exclusions, but no sibling tool covers provenance metadata, so the guidance is unambiguous and sufficient for this context.
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 target distinct query types: schema, provenance, exact match, substring search, group comparison, statistics, and top/bottom ranking. dataset_row and dataset_compare overlap somewhat (compare is a multi-value variant of row), and dataset_search could be used for the same purpose, but the descriptions clarify the differences well.
All tools follow a clear dataset_ prefix with snake_case names, making the family instantly recognizable. The second part mixes nouns (columns, row, stats, top) and verbs (compare, search), but the pattern is still predictable and readable.
Seven tools is a well-scoped set for a dataset querying server. Each tool addresses a common question type about the Termslane dataset without unnecessary bloat or missing fundamentals.
The toolset covers schema discovery, provenance, exact lookup, substring search, group comparisons, numeric statistics, and top/bottom ranking — a solid set for answering typical dataset questions. A possible gap is lack of pagination or arbitrary row listing, but the search and row tools cover most practical needs.