dataset_provenanceThe source, the date it was computed, the licence and the citation for the FMlane 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 behavioral disclosure burden. It discloses the type of information returned and implies a read-only, metadata-retrieval behavior through 'Read this.' It does not describe side effects, caching, or computation, but for a simple zero-parameter provenance tool the disclosure is adequate.
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 short sentences, no filler. The content is front-loaded in the first sentence, and the second sentence adds a practical instruction for use. 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 metadata lookup, the description is complete: it lists the returned fields (source, computed date, licence, citation), identifies the dataset, and explains when to consult it. No output schema exists, but the description sufficiently explains what the agent can expect to learn.
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 the description needs to add about parameter meaning. The baseline of 4 applies because no parameter gaps exist for the description to compensate for.
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 identifies the resource (the FMlane dataset's provenance) and enumerates the exact content returned: source, computed date, licence, and citation. It lacks an explicit action verb like 'returns' or 'provides', and it does not explicitly contrast itself with sibling tools, but the purpose is unambiguous.
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 relevant. It does not name alternatives or explicitly state when not to use it, but the provenance/citation focus makes the appropriate context clear.
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 query mode: schema, provenance, exact lookup, substring search, multi-value comparison, numeric aggregates, and top/bottom rows. Although dataset_row and dataset_compare both filter on column equality, their descriptions clearly separate single-value from multi-value ordered use.
All tools share a consistent dataset_ prefix and snake_case, making the family obvious. The suffix is not uniformly verb_noun, mixing nouns (columns, provenance, stats) with verbs (compare, search), so it is predictable but not perfectly consistent.
Seven tools is well-scoped for a single-dataset query server. Each tool corresponds to a common question type about the FMlane dataset, and none feel redundant or superfluous.
The set covers the core data-exploration surface: schema, provenance, exact/contains lookup, comparisons, numeric summaries, and extremes. Minor gaps exist, such as no distinct-value enumeration or grouped counts, but they can usually be worked around with dataset_compare and dataset_search.