Skip to main content
Glama

LiveDataLink

by io.github.blackboxfoundry

lobbying_search

Read-onlyIdempotent

Search U.S. federal lobbying disclosure filings (Senate LDA). Filter by year, filing type, registrant (lobbying firm), client (who hired them), or general issue code. Returns filings with the client, registrant, period, income/expenses, and lobbying issues. Pair with FEC and Congress tools to follow the money.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoPage number for pagination.
page_sizeNoResults per page (default 20, max 25).
issue_codeNoGeneral issue area code, e.g. 'ENG' (energy), 'TAX', 'HCR' (health).
client_nameNoClient name (the entity that hired the lobbyist), partial match.
filing_typeNoFiling type code, e.g. 'RR' (registration), 'Q1'-'Q4' (quarterly reports).
filing_yearNoFiling year, e.g. 2025.
registrant_nameNoLobbying firm / registrant name (partial match).

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by specifying the Senate LDA scope and listing the fields the response will contain. 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three concise sentences carry the core purpose, filtering dimensions, return contents, and a cross-tool usage hint. The most important information is front-loaded, with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

All 7 optional parameters are documented in the schema, and the description covers what the search returns and how it fits into a broader money-following workflow. There is no output schema, but the listed response fields mitigate that gap. Page/pagination details are left to the input schema, which is acceptable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter already documented including examples and partial-match behavior. The description merely restates the main filtering dimensions in prose, adding no meaning beyond the schema. A baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a clear action ('Search') and resource ('U.S. federal lobbying disclosure filings (Senate LDA)') and lists the kinds of records returned. It does not explicitly distinguish itself from sibling tools like lobbying_detail or lobbying_registrants, but the search-oriented framing and return fields make its role reasonably clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear context for when to use the tool: searching federal lobbying filings by year, filing type, registrant, client, or issue code. It also mentions pairing with FEC and Congress tools to follow the money. It stops short of naming alternatives or stating when not to use it, but the context is sufficient for typical selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LiveDataLink toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources