Get FEC campaign finance data for federal election candidates. Returns fundraising totals, cash on hand, and spending by office (President/Senate/House), state, or party.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | Candidate name search | |
| limit | No | Number of results (max 50) | |
| party | No | DEM, REP, IND | |
| state | No | 2-letter state code (TX, CA, NY) | |
| office | No | P=President, S=Senate, H=House | |
| election_year | No | Election cycle year (default: 2026) | 2026 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations present, the description carries the full burden of behavioral disclosure. It implies a read operation via 'Get' and 'Returns', but does not explicitly confirm it is read-only or free of side effects. More importantly, it leaves ambiguous whether the result is a list of individual candidates or aggregated sums per office/state/party, which is a key behavioral trait.
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 extremely concise—two sentences that front-load the primary purpose and follow with output details. Every word contributes, with no redundancy or filler.
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?
Without an output schema, the description needs to clarify the return structure. It mentions fundraising totals, cash on hand, and spending, but doesn't say whether these are per-candidate or aggregated. It also fails to mention pagination, sorting, or any typical usage flows. The ambiguity between 'candidates' and 'by office/state/party' makes this incomplete for an agent to predict the tool's behavior.
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?
Schema description coverage is 100% for all six parameters, providing a solid baseline. The description additionally mentions filtering by office, state, and party, which aligns with the parameters and reinforces their use. However, it does not add meaningful detail beyond the schema, such as the effect of 'limit' or the exact meaning of election_year.
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 it retrieves FEC campaign finance data for candidates and lists key metrics. However, it doesn't explicitly distinguish itself from siblings like get_fec_spending, and the phrase 'by office, state, or party' suggests aggregation, which could confuse whether it returns individual candidate records or grouped totals.
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?
No guidance is provided on when to use this tool versus get_fec_donors or get_fec_spending. The description does not mention alternatives, prerequisites, or scenarios where another tool would be more appropriate.
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.
Each tool targets a distinct aspect of FEC data: candidates, donors, and spending. Descriptions clearly differentiate them, leaving no ambiguity.
All tools follow the consistent 'get_fec_' prefix with a specific noun (candidates, donors, spending), adhering to a clear verb_noun pattern.
With 3 tools covering the primary areas of campaign finance data, the count is well-scoped for a focused FEC query server, avoiding bloat or inadequacy.
Covers candidates, donors, and spending, which are the core elements of FEC data. Minor gaps exist (e.g., committees or detailed filings), but the set is sufficient for most common queries.