metadataGet a Mesa Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "2gkz-7z4f".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "2gkz-7z4f". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "2gkz-7z4f"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds concrete details about what is returned (columns, types, row count, category, last-updated), which is useful behavioral context beyond the annotations. No contradiction.
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?
Single sentence with clear verb, resource, and example. Every part is informative and no word is wasted. Ideal length for a simple tool.
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 tool with a single parameter, rich annotations, and no output schema, the description covers all necessary information: it specifies input (resource_id), output (schema + metadata details), and provides an example. Nothing needed is missing.
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?
Schema description coverage is 100% for the single parameter 'resource_id', which the description repeats. The description does not add new meaning beyond the schema's example and description, so baseline 3 is appropriate.
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 what the tool does: getting schema and metadata (columns, types, row count, category, last-updated) for a Mesa Open Data dataset by resource_id, with a concrete example. It distinguishes itself from sibling tools by being specific to Mesa Open Data metadata retrieval.
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 implies use when metadata for a Mesa Open Data dataset is needed, and provides an example. However, it does not explicitly state when not to use this tool or mention alternatives (e.g., the 'datasets' sibling tool might be related). No exclusions or context for choosing this over others is given.
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.
Several tool groups have overlapping purposes (e.g., ask_pipeworx variants, polymarket research tools, visibility checks), despite detailed descriptions. An agent may struggle to choose between closely related options.
Tool names mix snake_case verb_noun patterns (e.g., ai_visibility_check, compare_entities) with noun-heavy compound names (e.g., polymarket_arbitrage, deep_research) and single-word names (e.g., query, recall). No consistent convention.
With 34 tools, the surface is heavy for a research/data server. Many tools are variants of core capabilities (ask_pipeworx, polymarket edges), suggesting consolidation would improve usability.
The server covers a broad range: data research, prediction markets, entity profiles, monitoring. However, the abundance of specialized variants and gaps in unified workflows (e.g., needing separate tools for simple vs grounded queries) reduce completeness.