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fec_candidate_financials

Read-onlyIdempotent

Get aggregate campaign finance totals for a candidate by FEC candidate_id, broken down by election cycle. Includes total receipts, disbursements, individual contributions, cash on hand, and debts. Filter to one cycle with the cycle parameter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoTwo-year election cycle (even year, e.g. 2024). Optional.
limitNoMaximum cycles to return (default 10, max 50).
candidate_idYesFEC candidate ID (e.g. 'S2MA00170').

Schema Changelog

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

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, and non-destructive behavior, so the description's main value is adding what the tool returns: aggregate totals by election cycle, including receipts, disbursements, contributions, cash on hand, and debts. It also implies that multiple cycles are returned by default and that the cycle parameter narrows to one, which is useful behavioral context beyond the 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?

Two sentences with no filler. The primary action is front-loaded, the resource and ID requirement are clear, the return fields are summarized, and the key optional filter is stated efficiently. Every sentence earns its place.

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?

For a simple read-only lookup with three well-documented parameters and no output schema, the description covers the essential semantics: what is returned, how it is grouped, and how to narrow it. Minor gaps include not stating the default number of cycles returned or whether the results include a summary/aggregate row, but these are not critical given the schema covers limit and cycle.

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?

All parameters are already fully described in the schema, so the baseline is 3. The description adds value by clarifying that the cycle parameter filters to a single election cycle and frames the data as cycle-level aggregates, but it does not meaningfully add semantics beyond what the schema already provides for candidate_id or limit.

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 verb ('Get'), a specific resource (aggregate campaign finance totals by FEC candidate_id), and a distinctive dimension (election cycle breakdown). It also enumerates the included fields, making the tool's purpose immediately recognizable and easily distinguishable from siblings like fec_candidate_search or fec_candidate_details.

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

Usage Guidelines2/5

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

The description gives no guidance on when to choose this tool over fec_candidate_details, fec_committee_search, or other FEC-related siblings. It also does not state that a user would first need to resolve a candidate to an FEC candidate_id via a search sibling, nor mention any exclusions or preferred alternatives. The only usage hint is the cycle parameter, which is a parameter instruction rather than tool-selection guidance.

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

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TDQS

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.