dataset_provenanceThe source, the date it was computed, the licence and the citation for the ReceivableLedger 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 provided, the description carries the burden of behavioral disclosure. It clearly states what information the tool provides (source, computed date, licence, citation) and implies a read-only, informational operation. No hidden side effects or permissions are relevant given the zero-parameter scope.
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 concise sentences with no filler. The first sentence front-loads the key content, and the second sentence supplies the practical usage context.
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 informational tool, the description is complete: it names the resource, the fields returned, and the scenario in which to use it. No output schema is needed for this level of complexity.
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, and the schema already documents this fully. Per the baseline for zero-parameter tools, the description does not need to add parameter-specific 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 names the specific resource (ReceivableLedger dataset) and the exact metadata fields (source, computation date, licence, citation). This distinguishes it from the sibling dataset tools, which operate on columns, rows, search, stats, and comparisons.
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 states when to use the tool: 'Read this to attribute a figure correctly.' It provides clear context for usage, though it does not mention explicit exclusions or alternatives.
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, provenance, search, stats, and top are easy to tell apart. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by matching a column value, with only the number of allowed values clearly differing.
All seven tools share the same dataset_ prefix and consistent snake_case formatting, making the family immediately recognizable. While some suffixes are nouns and some are verbs, the overall convention is uniform and predictable.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct common operation without adding redundant or overwhelming surface area.
The set covers schema discovery, provenance, exact lookup, multi-value comparison, fuzzy search, numeric statistics, and top/bottom ranking. Missing features like distinct-value listing or numeric-range filtering are minor gaps given the apparent Q&A-oriented purpose.