dataset_provenanceThe source, the date it was computed, the licence and the citation for the Sacristo 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 full disclosure burden. It implies a read-only informational operation and lists the fields returned, but it does not explicitly state return format, side effects, or the significance of 'date it was computed' (e.g., staleness). No contradiction exists, but disclosure is minimal.
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 communicate the full purpose with no filler. The description first enumerates the returned fields, then states the actionable reason to read it. 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-argument tool, the description is nearly complete: it names the dataset, the fields provided, and the intended use. It omits the output format (text, JSON, etc.), but the field list and title largely cover what an agent needs to invoke and interpret the call 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 input schema is already exhaustive. The description adds value by clarifying the fixed target dataset ('Sacristo') and the semantic content of the result, which the schema cannot convey. Baseline 4 for zero params is appropriate.
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 a provenance/metadata lookup for the Sacristo dataset and enumerates the exact contents: source, computed date, licence, and citation. This is immediately distinguishable from sibling tools like dataset_columns or dataset_search.
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 sentence 'Read this to attribute a figure correctly' provides a clear, actionable use case. It does not explicitly contrast with sibling tools or state when not to use it, but the context is unambiguous enough for an agent to select it appropriately.
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 clearly distinct operations—schema, provenance, search, stats, top, and exact lookup. Minor overlap exists between dataset_compare and dataset_row, both filtering rows by column values, but the descriptions clarify that compare handles multiple values while row is for a single exact match.
All tools follow a uniform dataset_<operation> pattern with consistent snake_case naming. The suffixes are a mix of nouns and verbs, but the prefix and format are fully predictable.
Seven tools is well-scoped for a single-dataset querying server. Each tool covers a distinct query need without redundancy.
The toolset covers schema discovery, provenance, exact and fuzzy matching, multi-value comparison, numeric statistics, and top/bottom rankings—good coverage for a read-only dataset exploration server. A possible minor gap is lack of a distinct-values or category-listing tool, but common analytical questions appear addressable.