get_filingGet the full detail of a single LDA lobbying filing by its filing UUID — registrant, client, income/expenses, every lobbying activity (issue + description + named lobbyists), foreign entities, and affiliated organizations. Example filing_uuid: "306576ff-0ef4-4269-b5fa-1a2256685950". Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| filing_uuid | Yes | Filing UUID from search_filings, e.g. "306576ff-0ef4-4269-b5fa-1a2256685950". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to reiterate safety. The description adds the 'Keyless' detail, indicating no authentication is required, which is a valuable behavioral trait beyond annotations. No contradictions found.
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 three sentences: the first clearly states the main function, the second provides a concrete example, and the third notes the lack of authentication requirement. Every sentence adds value, though the example could be considered redundant if the schema already includes one. No unnecessary fluff.
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 simple tool (1 parameter, no output schema, comprehensive annotations), the description compensates well by listing the types of data returned (registrant, client, income/expenses, lobbying activities, foreign entities, affiliated organizations). This provides sufficient context for an agent to understand what the response contains without an output schema.
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 provides full coverage for the single parameter (filing_uuid), including a description and an example. The description adds an extra example and clarifies the format is a UUID, but does not significantly enhance understanding beyond the schema. 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 retrieves the full detail of a single LDA lobbying filing by its filing UUID. It explicitly lists the types of data returned (registrant, client, income/expenses, lobbying activities, etc.), making the purpose specific and unambiguous. However, it does not explicitly distinguish from siblings like search_filings, which could be a retrieval tool for summaries.
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 usage after obtaining a filing UUID from search_filings, but does not provide explicit guidance on when to use versus alternatives. There is no stated exclusion criteria or mention of when not to use this tool. The context is clear enough for an agent experienced with the toolset, but lacks explicit usage boundaries.
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.
There are several near-duplicate clusters: ask_pipeworx, ask_pipeworx_beta (explicitly described as currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all route to the same underlying data; the six Polymarket tools (bet_research, arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) heavily overlap in purpose and are easy to confuse. Even detailed descriptions do not fully resolve which tool an agent should pick first.
All names use snake_case and many follow a verb_noun pattern (search_filings, get_filing, list_issue_codes, resolve_entity), but there is a mix of verb-first names (ask_pipeworx, compare_entities, generate_llms_txt), noun-first names (recent_changes, entity_profile, pipeworx_trending), and brand-prefixed families (pipeworx_*, polymarket_*). The naming is readable but not a single predictable pattern.
A server named 'Senate Lobbying' exposes 34 tools, but only three (search_filings, get_filing, list_issue_codes) relate to LDA lobbying data. The remaining 31 cover generic Pipeworx data lookup, prediction markets, memory, subscriptions, AI visibility, and npm package audits—an extreme overreach for the apparent scope and likely to confuse an agent expecting a focused lobbying toolkit.
For the lobbying domain implied by the server name, only search, get-one-filing, and issue-code enumeration exist; there is no aggregation/stats tool, no lobbyist/client entity resolution for LDA, no registrant or foreign-entity browsing, and no coverage of related concepts like lobbying firm hierarchies or spending trends. The generic Pipeworx tools fill a different domain, so the lobbying-specific surface has significant gaps.