dataset_provenanceThe source, the date it was computed, the licence and the citation for the TakeoffDeck 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?
The phrase 'Read this' implies a read-only operation, but the description does not explicitly state that it has no side effects or permissions. Since no annotations are present, the description carries this burden and is only partially 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?
Two concise sentences with no redundancy; every word contributes to the tool's purpose and usage.
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 covers the essential provenance elements and gives a clear use case, making it complete for the tool's simple scope.
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 zero parameters, and the description adds no parameter-specific information. With full schema coverage (trivially), the baseline 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 provides provenance info (source, date, licence, citation) for the dataset, and distinguishes it from sibling tools by focusing on attribution.
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 explicitly advises reading to attribute a figure correctly, giving a clear when-to-use scenario and practical guidance.
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.