dataset_provenanceThe source, the date it was computed, the licence and the citation for the Handovra 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, but the description conveys that this is a read-only metadata lookup and enumerates the returned information: source, date, licence, and citation. It does not mention authentication or rate limits, but those are minor for a zero-parameter informational tool.
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 sentences long, front-loaded with the key content, and contains no filler. Every word adds useful information.
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 provenance metadata tool, the description is complete: it identifies the dataset, the specific metadata fields available, and the purpose (attribution). No output schema or deeper behavioral details are necessary 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 no parameters, so the schema is trivially complete and there are no parameter semantics to document. The description appropriately focuses on what the tool returns rather than inputs.
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 what the tool provides: source, computed date, licence, and citation for the Handovra dataset. It also gives the intended action ('Read this to attribute a figure correctly'), which distinguishes it from the sibling data-access tools.
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 a clear usage context: use this tool when attribution or citation is needed. It does not name alternatives or explicitly state when not to use it, but for a provenance metadata tool the guidance is sufficient.
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 are clearly differentiated by operation: schema, provenance, exact match, substring search, multi-value comparison, stats, and ranking. The only minor overlaps are dataset_row vs dataset_compare for single-value lookups and the shared metadata banner in dataset_columns vs the full provenance record.
All tools share a consistent lowercase dataset_ prefix and snake_case style, making the family immediately recognizable. The suffixes mix nouns (columns, provenance, row, stats) with verbs (compare, search), so it is not a strict verb_noun pattern, but the deviation is minor and readable.
Seven tools is well within the ideal range for a read-only dataset exploration server. Each tool covers a distinct query need without redundancy, and none feels like filler.
The set covers the core data-exploration lifecycle: schema discovery, provenance, exact lookup, text search, multi-value comparison, numeric summaries, and top/bottom ranking. It lacks direct numeric range filtering or group-by, but agents can often approximate these with top, stats, or search, so the gaps are minor rather than blocking.