The Mine Works

search_fec_campaign_finance

Search US federal campaign finance via the official OpenFEC API: candidates by name, committees/PACs by name, or itemized Schedule A contributions for one committee.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	candidates/committees search by name; contributions pulls itemized donations for one committee
`query`	No	Name to search for candidates/committees modes (e.g. "Warren")
`apiKey`	No	Your free api.data.gov key. Falls back to FEC's public DEMO_KEY (rate-limited) if omitted.
`maxResults`	No	Max rows to return (default 25)
`committeeId`	No	FEC committee ID (e.g. "C00401224") - required for contributions mode. Find it via committees mode first.

TDQS

A4.2/5.0

Behavior3/5

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

With no annotations, the description carries the transparency burden. It discloses that the tool uses the official OpenFEC API and is a search operation, but it does not mention rate limits, authentication fallback, or error behavior. However, the schema's parameter descriptions (apiKey fallback, maxResults) cover some of these aspects, and the description adds the 'official' API context.

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?

The description is a single, front-loaded sentence that immediately identifies the tool's domain and enumerates the three modes. It is concise, free of redundancy, and every word 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?

The description provides a clear overview of the tool's capabilities, and the schema fills in parameter details. While it doesn't describe return values, the simplicity of a search tool and the absence of an output schema make this adequate. It would benefit from mentioning the need for an API key, but the schema covers that.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds meaningful domain context by labeling contributions as 'itemized Schedule A' and specifying 'for one committee,' which clarifies the committeeId requirement beyond the schema's description. This extra context justifies a 4.

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 specific resource ('US federal campaign finance via the official OpenFEC API') and enumerates three distinct search modes (candidates, committees/PACs, Schedule A contributions). This clearly distinguishes it from sibling search tools with different domains.

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?

It explicitly states the tool is for US federal campaign finance, providing clear context for when to use it. There are no sibling tools with similar functionality, so no explicit alternatives are needed. The schema further clarifies mode-specific requirements (e.g., committeeId for contributions mode).

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

A3.5/5.0

Disambiguation4/5

Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.

Tool Count2/5

With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.

Completeness4/5

The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.

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