Skip to main content
Glama

HillSignal

by com.hillsignal
OverviewAdmin

Super PACs: the biggest, and who is behind one

super_pacs
Read-only

Which super PACs are pulling the strings: the largest by money raised, spent or on hand for a cycle, from the FEC's own committee totals. With query or committee_id, one committee's profile: its top donors, what it spent for and against which candidates, and the vendors it paid for campaign work. With shared_vendors, the media buyers and consultancies working for more than one committee. With public_companies, listed companies ranked by the political money they give, resolved to tickers. A committee profile also names the public companies behind its money. Answers "biggest super PACs this cycle", "who funds MAGA Inc", "what is America PAC spending on", "which firms work for both sides", "which public companies give the most".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoreceipts (default) | independent_expenditures | disbursements | cash_on_hand
cycleNoTwo-year election cycle (default: current)
limitNoMax rows, 1-25 (default 10)
queryNoCommittee name (partial), e.g. "America PAC"
typesNo"all" to include every PAC type; default super PACs and hybrids only
tickerNoWith public_companies: one issuer, e.g. COIN
committee_idNoFEC committee id, e.g. C00879510
min_committeesNoWith shared_vendors: minimum committees sharing a vendor (default 2)
shared_vendorsNoReturn vendors paid by two or more committees instead of a committee ranking
public_companiesNoReturn publicly traded companies ranked by political money given, resolved to tickers

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / public_companies
      Added value: +{
+  "description": "Return publicly traded companies ranked by political money given, resolved to tickers",
+  "type": "boolean"
+}
    • addedInput schema / properties / ticker
      Added value: +{
+  "description": "With public_companies: one issuer, e.g. COIN",
+  "type": "string"
+}
  2. Changed2 schema fields changed
    • addedInput schema / properties / min_committees
      Added value: +{
+  "description": "With shared_vendors: minimum committees sharing a vendor (default 2)",
+  "type": "number"
+}
    • addedInput schema / properties / shared_vendors
      Added value: +{
+  "description": "Return vendors paid by two or more committees instead of a committee ranking",
+  "type": "boolean"
+}
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the description is not required to restate safety. It adds meaningful behavioral detail: the data source is FEC committee totals, different parameters switch the output mode, committee profiles include donors, candidate spending, and vendors, and public companies are resolved to tickers. It does not disclose pagination or exact response shape, but the main behaviors are transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is longer than average, but the tool has multiple distinct modes and ten parameters, so the detail is largely justified. It is front-loaded with the primary ranking purpose, then systematically covers each mode, and ends with concrete question examples. A few phrases are redundant with later examples, giving it a 4 rather than a 5.

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?

With no output schema, the description carries the burden of describing return behavior, and it does so at a useful level: committee lists, committee profiles with donors/candidates/vendors, shared vendor lists, and public company rankings. It does not specify exact response fields, potential mode conflicts, or edge cases like invalid committee ids, but for an agent selecting and invoking the tool it is largely sufficient.

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

Parameters5/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds essential interaction semantics that the schema alone does not convey: query/committee_id trigger a committee profile, shared_vendors switches to vendor sharing, public_companies switches to ticker-ranked companies, and ticker/min_committees are only meaningful within those modes. This goes well beyond individual parameter descriptions.

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

Purpose5/5

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

The description names a specific resource (super PACs) and a set of specific verbs/behaviors: ranking committees by money raised/spent/on hand, returning a committee profile with donors and expenditures, listing shared vendors, and ranking public companies by political contributions. It clearly distinguishes the tool's multi-modal scope from generic political money tools.

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?

The description provides strong usage context through conditionals: 'With query or committee_id', 'With shared_vendors', and 'With public_companies', and it gives representative natural-language questions to match against. However, it does not explicitly name sibling alternatives like outside_money or pac_lookup, nor state when this tool should not be used.

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

All HillSignal 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.

Resources