dataset_provenanceThe source, the date it was computed, the licence and the citation for the Endpointo 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 provided, the description carries the burden of behavioral disclosure. It implicitly communicates a read-only, informational nature by describing provenance metadata and a citation purpose. It doesn't mention side effects or access requirements, but none are expected for a metadata 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 concise sentences that front-load the key elements (source, date, licence, citation) and then state the intended usage. Every word earns its place with no redundancy.
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 the absence of parameters and output schema, the description fully covers what the tool provides and why it should be used. An agent can correctly invoke it without additional context.
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 trivially complete (100% coverage). The description adds no parameter details because none exist, meeting the baseline for parameterless tools.
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: providing the source, computation date, licence, and citation for the Endpointo dataset. It uses a specific verb-resource structure ('Read this to attribute a figure correctly') and is distinct from siblings that focus on data operations like columns, search, or 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 explicitly says 'Read this to attribute a figure correctly,' giving a clear use case and context. It doesn't list exclusions or alternative tools, but since no sibling covers provenance, this 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.
Each tool has a clearly distinct purpose: schema, exact match, search, comparison, stats, top values, and provenance. No two tools overlap in functionality; an agent can easily select the right tool based on the query type.
All tool names follow the consistent 'dataset_' prefix followed by a descriptive noun (columns, compare, provenance, row, search, stats, top). The naming pattern is uniform and predictable.
Seven tools is well-scoped for a dataset exploration server. Each tool covers a specific aspect of querying and understanding the dataset without unnecessary redundancy or bloat.
The surface covers schema, metadata, exact lookup, substring search, comparison, statistics, and ordering. It lacks features like distinct value enumeration or grouping, but for a read-only dataset exploration tool, the coverage is strong with only minor potential gaps.