dataset_provenanceThe source, the date it was computed, the licence and the citation for the Nofovo 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 provided, so the description must disclose behavior. It states the exact information returned (source, date computed, licence, citation), indicating a read-only retrieval operation with no side effects. It does not mention any error cases or rate limits, but for a simple zero-parameter lookup, this is adequate.
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 key content (what it provides) followed by a usage note. No wasted words; every clause contributes. The structure is efficient and immediately scannable.
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 tool with no output schema, the description is complete. It tells the agent what data will be returned (source, date, licence, citation) and when to use it. No additional context is needed for correct invocation.
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 schema is empty. Per the baseline for 0 params, the description doesn't need to explain parameter details. The description adds value by specifying the dataset name (Nofovo) and the nature of the returned data, which is more than necessary.
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 states the tool's purpose: to provide the source, computed date, license, and citation for the Nofovo dataset. It uses a specific verb ('read this') and names the resource, making it distinct from sibling tools like dataset_columns or dataset_stats.
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 gives explicit usage guidance: 'Read this to attribute a figure correctly.' This tells the agent when to use it, though it doesn't explicitly contrast with alternatives or state when not to use it. The context of sibling tools (columns, stats, etc.) implies this is for provenance only.
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, stats, top, search, exact-row, and compare all serve different question types. However, dataset_row and dataset_compare both filter by column values and could be confused for single-value lookups, and dataset_search adds a third overlapping retrieval path.
All tools share a consistent dataset_ prefix and use lowercase snake_case, making the set predictable. The second part mixes nouns (columns, provenance, row, stats, top) with verbs (compare, search), so the naming is mostly consistent but not a strict verb_noun pattern.
Seven tools is well-scoped for a dataset query server. Each tool addresses a distinct common question type, and none feel redundant or unnecessary.
The set covers schema discovery, provenance, exact lookups, fuzzy search, comparisons, summary statistics, and extreme values, which handles most dataset question-answering needs. Minor gaps exist around arbitrary inequality filters or combining conditions across multiple columns, but agents can typically work around these.