datasetsSearch the FCC Open Data catalog of open datasets by keyword. Returns each dataset's resource_id, name, description, category and update date — pass the resource_id to query/metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword to search dataset titles/descriptions (e.g. "budget", "crime", "health"). | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "broadband"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "spectrum"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, open-world, non-destructive. Description adds context about returned fields and how to use results, exceeding annotation coverage.
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 that efficiently conveys purpose, return data, and next step. No wasted words.
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 search tool with 3 parameters and no output schema, the description completes the picture by specifying return fields and linking to the metadata tool. Annotations further cover safety and behavior.
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 covers all 3 parameters with descriptions. The tool description does not add additional semantic meaning beyond what is in the schema, 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?
Clearly states the tool searches the FCC Open Data catalog by keyword, mentions return fields (resource_id, name, description, category, update date), and distinguishes from siblings by indicating next steps (pass resource_id to query/metadata).
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?
Implies usage for keyword-based search and provides follow-up action, but does not explicitly state when not to use it or compare with alternatives among siblings.
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.