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openfec-mcp-server

by io.github.cyanheads

Openfec Get Legal Document

openfec_get_legal_document
Read-onlyIdempotent

Fetch one FEC legal document in full — advisory opinion, MUR, ADR, administrative fine, or statute — by its type and number. openfec_search_legal replaces each result's documents array with a count and category summary and cuts every commission vote down to a date and a 200-character action; this returns the record untouched. doc_type is the plural form of the document_type discriminator on a search result (advisory_opinion becomes advisory_opinions, mur becomes murs, adr becomes adrs, admin_fine becomes admin_fines, statute becomes statutes), and no is that result's no field — every document type carries it, and advisory opinions repeat it as ao_no.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noYesDocument number, copied from the no field of the matching openfec_search_legal result. Advisory opinions are year-serial (e.g. "2024-01", also repeated as ao_no); murs, adrs, and admin_fines are digit strings (e.g. "8363"); statutes are U.S. Code section numbers (e.g. "30123").
doc_typeYesLegal document type, always plural. openfec_search_legal reports the singular form in each result document_type — advisory_opinion, mur, adr, admin_fine, statute — so add an "s" to get the value this field wants.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent when the call failed. Absent on success.
documentNoThe complete legal document record. Carries the full documents array that openfec_search_legal replaces with a count and category summary, and the complete commission_votes entries it reduces to a vote date and a truncated action, alongside the dispositions and the scalar and date fields (name, type, url, penalty and determination amounts, case dates). Fields present vary by document type.
search_criteriaNoEcho of the search filters this call applied, as the server parsed them, minus paging arguments. Always present — compare it against what you sent to confirm every filter was honoured.
attachedDocumentCountNoNumber of related filings in the record documents array. Compare against the document_count openfec_search_legal reported for the same record.

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds valuable behavioral context beyond that by noting that openfec_search_legal truncates votes and arrays, while this tool returns the record 'untouched' — clarifying what the agent actually receives. It does not contradict 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?

The description is dense but every clause earns its place. It front-loads the purpose, then contrasts with the sibling, then gives parameter mapping details. No redundancy or filler; each sentence conveys necessary information without becoming unwieldy.

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?

With an output schema present for return format, the description covers everything an agent needs to correctly select and invoke the tool: when to use it, how to map parameters from search results, and what to expect in return. No gaps remain for a caller.

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

Parameters5/5

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

Though the input schema covers 100% of parameters with descriptions, the description adds substantial meaning beyond it: it explains the pluralization rule for doc_type, connects it to the search result's 'document_type' field, and provides concrete examples for the 'no' field across document types (year-serial, digit strings, U.S. Code sections). This elevates parameter understanding well beyond the raw schema.

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 clearly states the specific verb (fetch) and resource (one FEC legal document in full), and explicitly distinguishes it from the sibling openfec_search_legal, which returns truncated/summarized records. It also enumerates the document types accepted, making the purpose unambiguous.

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

Usage Guidelines5/5

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

The description explains when to use this tool versus openfec_search_legal: when the full document is needed, contrasting with the search tool's summarization behavior. It also provides explicit mapping instructions for converting the search result's singular 'document_type' to the plural 'doc_type' required here, leaving no inference to the agent.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a clearly distinct FEC resource or action, and even closely related tools are explicitly differentiated. Independent versus coordinated expenditures and legal search versus legal document retrieval are described in enough detail that an agent should not misselect.

Naming Consistency5/5

All tools share the openfec_ prefix and follow a predictable verb_noun snake_case pattern: get for direct retrieval, lookup for calendar/election lookups, and search for queryable datasets. The naming is highly consistent across the full tool set.

Tool Count5/5

Twelve tools is well-scoped for a campaign-finance data server covering candidates, committees, money flows, filings, legal documents, calendar, and elections. Each tool represents a meaningful slice of the domain without unnecessary overlap or bloat.

Completeness4/5

The tool surface covers core FEC workflows thoroughly: candidates, committees, contributions, disbursements, independent and coordinated expenditures, filings, legal documents, calendar, and elections. The only notable gaps are specialized FEC categories such as electioneering communications and communication costs, which are not exposed.

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