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LiveDataLink

by io.github.blackboxfoundry

fec_candidate_search

Read-onlyIdempotent

Search federal candidates (President, House, Senate) by name, state, office, or party using FEC data. Returns candidate IDs needed for the other FEC tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoTwo-year election cycle (even year, e.g. 2024). Optional.
limitNoMaximum candidates to return (default 20, max 100).
partyNoParty code (e.g. 'DEM', 'REP', 'IND', 'LIB'). Optional.
queryNoCandidate name fragment (e.g. 'Warren', 'Smith'). Optional.
stateNoTwo-letter state code to filter by (e.g. 'MA', 'TX'). Optional.
officeNoOffice: 'P' (President), 'S' (Senate), or 'H' (House). Optional.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent/non-destructive behavior, and the description adds the important behavioral outcome that the tool returns candidate IDs, including their role as input to other FEC tools. It does not discuss pagination or rate limits, but those are secondary for this simple search.

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 short sentences with no filler: the first states the action and filters, the second states the output and contextual purpose. It is front-loaded and every sentence earns its place.

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

Completeness5/5

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

For a simple optional-parameter search tool, this description is complete: schema documents all parameters, annotations cover safety, and the description explains what the tool returns and why it matters. No critical information is missing for an agent to select and invoke it correctly.

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 six parameters are already documented in the input schema with descriptions (100% coverage), so the schema carries the parameter-semantics burden. The description only repeats the filter categories without adding syntax, defaults, or relationships beyond what the schema states.

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 uses a specific verb ('search') with a concrete resource ('federal candidates') and enumerates the filter dimensions (name, state, office, party). It also states the key output (candidate IDs) and positions the tool as a prerequisite for other FEC tools, which separates it from sibling FEC lookup 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 clearly indicates this tool is for finding candidate IDs needed before using the other FEC tools, which gives an agent a concrete reason to choose it. It does not explicitly name alternative tools or state when not to use it, so it stops short of a full 5.

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.

Resources