dataset_provenanceThe source, the date it was computed, the licence and the citation for the Kickoffo 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 explaining behavior. It discloses the tool is informational (read this to attribute) and lists the fields returned. This makes the read-only, non-mutating nature clear, though it does not describe the exact format of the citation or licence fields.
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 compact sentences with no filler. The first sentence lists the key content fields, and the second states the practical purpose. Every word contributes value.
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 provenance tool with no output schema, the description fully covers what an agent needs to know: what information is available and why to use it. 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 the schema has 100% coverage, so the baseline is 4. The description adds useful semantic context about what the returned information represents, which exceeds the empty input schema's contribution.
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 resource ('the Kickoffo dataset') and the specific information it returns: source, computed date, licence, and citation. It is immediately distinguishable from sibling tools like dataset_columns or dataset_search, which operate on data content rather than provenance.
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 tells the agent when to use the tool: 'Read this to attribute a figure correctly.' This provides clear context for the intended use case. It does not name alternatives or exclusions, but for a zero-parameter 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.
Each tool targets a distinct operation—schema, provenance, exact lookup, substring search, multi-value comparison, statistics, and top/bottom ranking. The only possible confusion is between dataset_row and dataset_compare, but the descriptions make the multi-value distinction clear.
All tools share a consistent dataset_ prefix and lowercase snake_case style, making the family obvious. The suffixes mix nouns, verbs, and an adjective, so it is not a strict verb_noun pattern but remains predictable.
Seven tools is well-scoped for a dataset querying server; each tool provides a distinct query or metadata capability and none feel redundant.
The toolset covers schema exploration, provenance, exact lookups, text search, comparisons, statistics, and top/bottom ranking. A direct group-by or unique-values tool would improve grouped aggregation workflows, but the main querying surface is well covered.