dataset_columnsThe columns, which of them are numeric, the row count and the provenance banner of the TakeoffDeck 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 burden. It describes what information is returned but does not explicitly state that the operation is read-only or side-effect-free, though the zero-parameter schema strongly implies it.
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 concise and well-structured, covering all key outputs in a single sentence plus a clear usage directive. No unnecessary words or redundancy.
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 description lists the returned elements well enough for an agent to know what to expect, despite lacking an output schema. It does not detail output formatting, but that is not essential for this simple schema-introspection tool.
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 and the schema coverage is complete. The description does not need to explain parameters because none exist, so the baseline score applies.
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 returns the schema-related details: columns, numeric flags, row count, and provenance banner. It also explicitly says to call it first, reinforcing its role as an introductory schema exploration 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?
It gives explicit usage guidance with 'Call this first to learn the schema', which tells the agent when to use it. It does not mention alternatives or when not to use it, but the first-step instruction is sufficient for typical schema discovery.
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 querying need: schema, provenance, exact match, substring search, comparison, statistics, and top/bottom ranking. Dataset_row and dataset_compare could be confused for single-value lookups, but their stated purposes (exact equality vs. X/Y comparisons) make them distinguishable.
All tools follow a consistent dataset_ prefix with lowercase snake_case naming. Although the second token mixes nouns and verbs, the pattern is highly predictable and easy to infer.
Seven tools is well-scoped for a read-only dataset querying server. Each tool covers a distinct query mode without unnecessary redundancy or bloat.
For the apparent domain of exploring and querying a single dataset, the surface is complete: schema discovery, provenance, exact lookup, search, comparison, statistics, and ranking are all covered. No obvious read-only query operations are missing.