metadataGet a FCC Open Data dataset's schema + metadata (columns, types, row count, category, last-updated) by resource_id, e.g. "3xyp-aqkj".
| Name | Required | Description | Default |
|---|---|---|---|
| resource_id | Yes | Dataset id, e.g. "3xyp-aqkj". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "resource_id": "3xyp-aqkj"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false. The description adds value by specifying the exact metadata returned (columns, types, row count, category, last-updated), which is beyond the annotation scope. There is no contradiction with annotations.
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, front-loaded sentence that efficiently conveys purpose, resource, and an example. Every word is necessary and earns its place.
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?
Given the tool's simplicity (one parameter, no output schema, rich annotations), the description is complete. It explains the input, the expected output fields, and provides an example. No further information is needed for an agent to use it correctly.
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 input schema covers 100% of parameters, and the description duplicates the parameter example. The description does not add significant semantic meaning beyond what the schema provides (e.g., types, required). 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 the tool's purpose: to retrieve schema and metadata (columns, types, row count, category, last-updated) for an FCC Open Data dataset by resource_id. The verb 'Get' is precise, and the example '3xyp-aqkj' reinforces usage. It distinguishes itself from siblings by focusing on FCC 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 provides a clear context (retrieving metadata by resource_id) but does not explicitly state when to use it versus alternatives. There is no guidance on when not to use it or mention of limitations. The annotations (readOnlyHint, idempotentHint) imply safe usage, but the description itself lacks explicit usage guidelines.
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 tools overlap in purpose: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, and validate_claim all query the same underlying data catalog in similar ways. The descriptions provide detailed usage guidance, but the query family and the five-strong Polymarket family still create real misselection risk. Memory, subscription, and FCC tools are clearly distinct.
Tool names are consistently snake_case and mostly follow recognizable verb_noun or prefixed-family patterns (ask_pipeworx_*, polymarket_*, pipeworx_*). The main deviations are bare one-word names like datasets, metadata, query, remember, and forget, plus a few noun-first names like entity_profile and polymarket_arbitrage. Overall the naming is readable and predictable, with only minor inconsistencies.
At 34 tools, this server is well past the 25+ threshold and feels overloaded. It bundles a general-purpose data-query gateway, FCC open-data access, Polymarket analytics, AI-visibility scanning, memory, subscriptions, npm dependency checks, and llms.txt generation into one surface. Each functional area is small on its own, but the combined set would be more coherent split into several focused servers.
The core data lifecycle is well covered: discovery (suggest_questions, discover_tools), single queries (ask_pipeworx), grounded/evidence-backed answers, deep multi-source research, entity resolution/profiling/comparison, change feeds, and fact-checking all exist. FCC open data has search, schema, and query tools, and subscriptions/memory have full CRUD-style coverage. Minor gaps remain (e.g., no direct pipeworx:// URI fetch tool, no enumeration of all discoverable data sources), but they do not block typical workflows.