dataset_provenanceThe source, the date it was computed, the licence and the citation for the Funnelvo 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 full burden of behavioral disclosure. It clearly enumerates the content the tool returns (source, date, licence, citation) and frames the operation as a read/reference action, implying no side effects. This is adequate transparency for a simple 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 short sentences with no filler. The essential information is front-loaded: the dataset name and the specific provenance fields, followed by a practical use case. Every word earns its place.
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 tool with no output schema, the description is complete. It names the dataset, lists exactly what information will be returned, and explains why an agent would call it. Nothing necessary for correct invocation 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 schema coverage is 100%, so the baseline is 4. There is no parameter semantics to add, and the description appropriately focuses on the tool's output and usage rather than nonexistent 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 that the tool provides the source, computation date, licence, and citation for the Funnelvo dataset. It uses the directive 'Read this to attribute a figure correctly,' making the purpose explicit. This distinguishes it well from sibling tools that focus on columns, rows, stats, or comparisons.
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 phrase 'Read this to attribute a figure correctly' explicitly indicates when to use the tool: whenever attribution or citation is needed. While it does not name sibling alternatives or state when not to use it, the context of provenance versus data operations is clear enough.
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 clear role, but dataset_row and dataset_compare both retrieve rows by column equality, and dataset_search adds another filter-based lookup. The descriptions distinguish exact vs. multi-value vs. substring matching well enough that an agent can choose correctly.
All tools share a consistent dataset_ prefix, making the family obvious. However, the second part mixes nouns (columns, stats, row), verbs (compare, search), and adjectives (top), so the pattern is not a uniform verb_noun convention.
Seven tools is well-scoped for a read-only dataset exploration server. Each tool covers a distinct query mode or metadata need without redundancy or excessive surface area.
The set covers schema discovery, provenance, exact lookup, substring search, multi-value comparison, summary statistics, and top/bottom ranking. This is a complete surface for the stated purpose of interacting with the Funnelvo dataset.