dataset_provenanceThe source, the date it was computed, the licence and the citation for the WallChartBook 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. It discloses what the tool provides (source, date, licence, citation) and implies a read-only operation via 'Read this', which is adequate for a provenance lookup. It does not discuss edge cases such as missing or incomplete provenance information, but the zero-parameter schema limits the risk.
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 sentences, front-loaded with the most useful content, and every phrase earns its place. The description is compact without sacrificing clarity.
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?
Given a zero-parameter schema and no output schema, the description adequately explains the return values (source, date computed, licence, citation) and the reason to call it. It could specify the citation format or note that provenance may be unavailable, but for this simple tool the description is practically complete.
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 with 100% schema description coverage, so the baseline is 4. The description adds no further parameter meaning, and none is needed because no inputs are required.
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 identifies a specific resource (WallChartBook dataset) and the exact kind of metadata returned (source, date computed, licence, citation). It clearly distinguishes this provenance tool from data-manipulation siblings like dataset_search or dataset_stats, though it lacks an explicit verb such as 'returns' or 'gets'.
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 clear, concrete use-case for when the tool should be invoked. It does not explicitly list alternatives or exclusions, but the purpose is specific enough for an agent to select it over the other dataset tools.
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, but dataset_row and dataset_compare overlap for single-value equality checks, and dataset_search's substring match can also return exact matches. The descriptions largely resolve this, so the ambiguity is minor.
All tool names use the same dataset_ prefix and lowercase snake_case, which is predictable and clean. However, the suffix mixes nouns (columns, row, stats, provenance) with verbs (compare, search) and the adjective-like 'top', so it is not a fully consistent verb_noun pattern.
Seven tools is an appropriate size for a dataset-query server; each tool covers a distinct query pattern (schema, exact lookup, search, comparison, stats, ranking, provenance) without redundancy or bloat.
The server covers the core read-only workflows for the dataset: schema discovery, row retrieval, text search, comparisons, numeric summaries, top/bottom rankings, and citation metadata. It lacks a way to list all rows or get distinct column values, but these are workable gaps rather than dead ends.