list_collectionsList GovInfo collections (BILLS, CFR, USCODE, FR, CHRG, CRPT, HMAN, PLAW, SERIALSET, USCOURTS, etc.) with package counts. Use the collection code with search_packages to filter.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Total number of collections | |
| collections | Yes | List of available GovInfo collections |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "collections": {
+ "description": "List of available GovInfo collections",
+ "items": {
+ "properties": {
+ "code": {
+ "description": "Collection code (e.g., BILLS, CFR)",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "granule_count": {
+ "description": "Number of granules in collection",
+ "type": [
+ "number",
+ "null"
+ ]
+ },
+ "name": {
+ "description": "Collection name",
+ "type": [
+ "string",
+ "null"
+ ]
+ },
+ "package_count": {
+ "description": "Number of packages in collection",
+ "type": [
+ "number",
+ "null"
+ ]
+ }
+ },
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "count": {
+ "description": "Total number of collections",
+ "type": "number"
+ }
+ },
+ "required": [
+ "count",
+ "collections"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, and idempotent hints, so the description needs only to add behavior not captured there. It adds that the tool returns package counts per collection and lists known collection codes, which is useful context beyond the annotations. No contradictions 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 exactly two sentences; the first states the core function and the second provides an actionable cross-reference. No wasted words, with the action front-loaded.
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 low complexity (no parameters), rich annotations, and the presence of an output schema, the description covers all necessary aspects. It tells the agent what will be returned (package counts) and how to use the output in a downstream call.
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 tool has zero parameters, so parameter semantics are a non-issue. The description correctly focuses on the tool's purpose and output, and with schema coverage of 100% (vacuously), there is nothing for the description to add about parameters.
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 uses a specific verb ('List') and identifies both the resource (GovInfo collections) and the distinguishing output ('with package counts'). It also enumerates example collection codes (BILLS, CFR, USCODE, FR, etc.), making the scope clear and differentiating it from sibling search tools.
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 explicitly instructs the agent to 'Use the collection code with search_packages to filter,' providing a direct cross-tool usage pattern. While it does not describe when to avoid this tool, the sibling context and the nature of the operation make the primary use case evident.
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.
Many tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve lookup/discovery in similar ways, and the five polymarket_* tools plus bet_research blur together. The six genuinely GovInfo-specific tools are distinct, but an agent would struggle to pick between the numerous meta and market tools, especially when the server is supposed to be about government information.
Most tools follow a readable verb_noun pattern (list_collections, search_packages, get_granule, resolve_entity), but there are deviations: domain-prefixed nouns like polymarket_arbitrage, noun-ish names like pipeworx_trending, and verb phrases like ask_pipeworx_beta or generate_llms_txt. Overall it is mixed yet still navigable, not chaotic.
36 tools is excessive for a server named Govinfo: only about six tools (list_collections, search_packages, get_package, list_granules, get_granule, search_within) actually serve that domain, while the remaining ~30 are Pipeworx meta-tools, prediction-market helpers, memory utilities, and AI-visibility checks. The count is bloated relative to the apparent scope and dilutes the server's identity.
The core GovInfo workflow is present — list collections, search packages, fetch package metadata, list and fetch granules, and semantically search within fetched text. However, there is no tool that directly downloads or returns the full text/PDF/XML content of a package or granule; agents only get links, so a full-document workflow requires an external fetch. The unrelated tools do not fill that gap and instead distract from the domain.