dataset_provenanceThe source, the date it was computed, the licence and the citation for the Clauselane 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?
There are no annotations, so the description carries the behavioral burden. It clearly indicates a read-only informational operation ('Read this') and specifies exactly what information will be exposed: source, date computed, licence, and citation. For a zero-parameter metadata tool, this is sufficient transparency.
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 short sentences, immediately front-loads the substantive content, and avoids repeating the title or schema. Every word contributes to the agent's understanding of what the tool returns and when to use it.
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 no-parameter, no-output-schema provenance tool, the description is complete: it lists all the fields the agent will receive and explains the practical purpose (correct attribution). Nothing essential is missing.
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 100% schema description coverage, so there is nothing for the description to clarify about inputs. The baseline of 4 applies because no parameter guidance is 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 explicitly identifies the tool's resource (Clauselane dataset) and the specific provenance details it covers: source, computation date, licence, and citation. The title reinforces the purpose ('Where this data comes from, and how to cite it'), and the content clearly distinguishes it 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 a clear use case: 'Read this to attribute a figure correctly.' This tells the agent when the tool is appropriate. It does not name alternatives, but none of the sibling tools serve a provenance purpose, so no exclusion is strictly needed.
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 separated by operation: schema, provenance, exact lookup, search, stats, top, and comparison. Dataset_row and dataset_compare overlap somewhat for exact-value lookups, but their intended use cases are mostly distinguishable.
All tools share the dataset_ prefix and snake_case convention, which creates a strong pattern. However, the suffix mixes nouns (columns, row, stats) and verbs (compare, search), so it is not a fully consistent verb_noun scheme.
Seven tools is well-scoped for a dataset exploration server. Each tool serves a distinct read/query need without unnecessary bloat or overlap.
The set covers schema discovery, provenance, exact lookup, fuzzy search, numeric statistics, top/bottom values, and category comparisons. For a read-only dataset querying purpose, there are no obvious missing operations.