Get full detail for a single federal candidate by FEC candidate_id (e.g. 'P80001571'). Includes office, party, status, election years, and mailing address. Use fec_candidate_search to find the candidate_id.
| Name | Required | Description | Default |
|---|---|---|---|
| candidate_id | Yes | FEC candidate ID (e.g. 'P80001571', 'S2MA00170'). |
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?
Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar is lower. The description adds value beyond that by disclosing the response scope ('full detail... includes office, party, status, election years, and mailing address'), giving the agent a concrete expectation of what comes back. It doesn't mention behavior for invalid IDs, but that's a minor gap for a simple lookup.
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?
Two sentences, no filler. The first establishes purpose with an example; the second routes to the sibling tool. Both sentences earn their place and the core action is front-loaded.
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?
For a single-parameter, read-only lookup, everything needed to invoke it correctly is present: the parameter is fully documented, the return content is described (field list compensates for the absent output schema), annotations cover safety, and the prerequisite relationship to fec_candidate_search is stated.
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% — the schema already documents candidate_id with two examples ('P80001571', 'S2MA00170'). The description repeats one example but adds no meaning beyond what the schema provides, so the baseline 3 applies.
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 uses a specific verb-resource pair ('Get full detail for a single federal candidate') and identifies the lookup key (candidate_id) with a concrete example. It also enumerates the included fields (office, party, status, election years, mailing address), which distinguishes this from the financial-oriented sibling fec_candidate_financials.
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?
It explicitly routes to fec_candidate_search when the ID is unknown ('Use fec_candidate_search to find the candidate_id'), which is clear when-not-to-use guidance. However, it doesn't explicitly address the other FEC siblings (fec_candidate_financials, fec_committee_search); the field list implies the boundary, but an explicit exclusion would make it fully complete.
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.
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.
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.
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.
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.