dataset_provenanceThe source, the date it was computed, the licence and the citation for the Abutly 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?
No annotations are present, so the description carries the burden. It frames the operation as a non-mutating read ('Read this') and specifies the exact metadata returned: source, computed date, licence, and citation. This is adequate for a simple provenance tool, though it stops short of describing the exact response format.
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 filler. It front-loads the specific metadata items and adds exactly one sentence about why to use the tool. 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 parameterless provenance tool with no output schema, the description is complete: it states what information is returned, names the dataset, and explains when to use it. An agent can select and invoke this tool with confidence based on the text alone.
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 parameters and schema description coverage is 100%, so there is nothing for the description to add. The baseline for a parameterless tool is 4, and the description doesn't need to explain parameter semantics.
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 identifies what the tool provides: source, computed date, licence, and citation for the Abutly dataset. It also gives the operational purpose, 'Read this to attribute a figure correctly,' and is easily distinguished from sibling tools focused on columns, rows, stats, search, and comparison.
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 explicitly states when to use the tool: when attribution or citation of a figure is needed. It doesn't name alternatives or exclusions, but the context is clear and sufficient for a metadata/provenance lookup among the listed 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 clearly distinct role: schema inspection, provenance, exact lookup, substring search, multi-value comparison, numeric stats, and top/bottom ranking. Even though dataset_row and dataset_compare both retrieve rows by column value, their descriptions make the single-value vs multi-value distinction clear.
All tools share a consistent dataset_ prefix and use clear snake_case names. The suffixes are either nouns or verbs that accurately reflect the operation, making the naming predictable and easy to navigate.
Seven tools is well-scoped for a single-dataset query server. Each tool covers a distinct query need without redundancy or bloat.
The toolset covers the full range of dataset querying: schema inspection, provenance, exact match, substring search, multi-value comparison, numeric aggregation, and ranking. Since this is a read-only dataset server, no update/create/delete tools are needed.