dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the ReceivableLedger 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?
No annotations are provided, so the description carries the transparency burden. It discloses that this is a learning/read-style operation and enumerates what the agent will receive. It stops short of stating side-effect-free or auth requirements, but for a zero-argument metadata tool the behavior is reasonably transparent.
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 one dense sentence plus a direct instruction. It front-loads the key content and every phrase contributes meaning, with no 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?
The tool has no parameters and no output schema, so the description must stand alone. It lists the main return elements and the intended first-step role, which is enough for an agent to call it correctly. A named sibling or return-format note could push it to 5.
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?
There are no parameters, so the schema already fully covers this dimension. The baseline for 0 parameters is 4, and the description adds nothing contradictory or misleading.
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 tool's outputs — columns, numeric flags, row count, and provenance banner — for the ReceivableLedger dataset, and frames it as the schema-learning entry point. It is specific, but it does not explicitly name a sibling it is not, so it earns a 4 rather than a 5.
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?
"Call this first to learn the schema" gives direct guidance on when to use the tool. It does not mention alternatives or explicit when-not-to-use conditions, but the sequencing instruction is a clear usage signal.
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.
Most tools have clearly distinct purposes: schema, provenance, search, stats, and top are easy to tell apart. The main ambiguity is between dataset_row and dataset_compare, since both retrieve rows by matching a column value, with only the number of allowed values clearly differing.
All seven tools share the same dataset_ prefix and consistent snake_case formatting, making the family immediately recognizable. While some suffixes are nouns and some are verbs, the overall convention is uniform and predictable.
Seven tools is a well-scoped size for a single-dataset querying server. Each tool covers a distinct common operation without adding redundant or overwhelming surface area.
The set covers schema discovery, provenance, exact lookup, multi-value comparison, fuzzy search, numeric statistics, and top/bottom ranking. Missing features like distinct-value listing or numeric-range filtering are minor gaps given the apparent Q&A-oriented purpose.