get_granuleFetch summary metadata for a single granule (sub-unit) within a GovInfo package, given package_id and granule_id (both from list_granules). Returns title, class, and provenance fields from the GovInfo summary endpoint.
| Name | Required | Description | Default |
|---|---|---|---|
| granule_id | Yes | Granule ID | |
| package_id | Yes | GovInfo packageId |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "granule_id": "2024-05-12-1",
+ "package_id": "FR-2024-05-12"
+ }
+]Output schema / (root)Previous value: -nullNew value: +{
+ "description": "Granule metadata for sub-unit within package",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations by naming the 'GovInfo summary endpoint' and listing the returned fields ('title, class, and provenance'), which helps the agent set expectations without over-explaining.
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 two sentences, front-loaded with the main action and scope, then efficiently details the inputs and return fields. No wasted words; every sentence contributes.
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?
This is a simple read-only tool with an output schema and strong annotations. The description covers the purpose, input provenance, and return fields, and no additional context (e.g., error behavior, rate limits) is necessary for correct agent usage.
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 schema covers 100% of parameters with basic descriptions ('Granule ID', 'GovInfo packageId'), but the description adds meaningful context by stating both are 'from list_granules', clarifying where the agent should obtain these IDs. This enriches the bare schema info.
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 ('Fetch') and resource ('summary metadata for a single granule within a GovInfo package'), clearly distinguishing it from sibling tools like get_package or list_granules. It also specifies the return fields ('title, class, and provenance'), making the scope unambiguous.
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 explains that both package_id and granule_id should come from list_granules, establishing a clear workflow and context for when this tool should be used. It does not explicitly state when not to use it, but this is a minor gap since the workflow guidance is sufficient.
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.