dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the FMlane dataset. Call this first to learn the schema.
| 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 explicitly lists what the call returns (columns, numeric flags, row count, provenance banner) and frames the tool as an introspective schema-learning step. It does not state side effects or error behavior, but for a zero-parameter schema-shape read, the behavior is sufficiently clear.
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 a single, well-structured sentence that front-loads the output contents and ends with actionable usage guidance. Every word earns its place, with no wasted filler.
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, no-output-schema tool, the description provides sufficient context: it names the resource, enumerates the data returned, and tells the agent this is the first call to make. A minor gap is that it does not clarify how its provenance banner relates to the sibling dataset_provenance tool, but this does not prevent correct invocation.
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 no parameters, so there is no schema ambiguity to clarify. The baseline of 4 is appropriate because the description does not need to compensate for undocumented parameters.
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 FMLane dataset and enumerates the specific information returned: columns, numeric flags, row count, and provenance banner. The phrase 'learn the schema' reinforces the tool's purpose. It does not explicitly distinguish itself from siblings, though 'Call this first' implies its role as an initial schema-discovery tool.
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 instruction 'Call this first to learn the schema' provides clear guidance on when to use the tool. However, it does not mention alternatives or conditions where another sibling like dataset_provenance or dataset_stats would be more appropriate, so it falls short of fully explicit usage routing.
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.