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openfec-mcp-server

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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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / properties / error / properties / data / properties / reason / description
      Previous value: -"Machine-readable failure mode. Declared by this tool: `legal_document_not_found`: No legal document exists at the requested doc_type and no Other values are possible when a failure originates below the handler."New value: +"Machine-readable failure mode. Declared by this tool: `legal_document_not_found`: No legal document exists at the requested doc_type and document number. Other values are possible when a failure originates below the handler."
  2. Changed6 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedInput schema / additionalProperties
      Added value: +false
    • changedOutput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedOutput schema / anyOf
      Added value: +[
+  {
+    "not": {
+      "required": [
+        "error"
+      ]
+    },
+    "required": [
+      "document",
+      "search_criteria",
+      "attachedDocumentCount"
+    ]
+  },
+  {
+    "required": [
+      "error"
+    ]
+  }
+]
    • addedOutput schema / properties / error
      Added value: +{
+  "additionalProperties": {},
+  "description": "Present when the call failed. Absent on success.",
+  "properties": {
+    "code": {
+      "description": "JSON-RPC error code for this failure.",
+      "maximum": 9007199254740991,
+      "minimum": -9007199254740991,
+      "type": "integer"
+    },
+    "data": {
+      "additionalProperties": {},
+      "properties": {
+        "reason": {
+          "description": "Machine-readable failure mode. Declared by this tool: `legal_document_not_found`: No legal document exists at the requested doc_type and no Other values are possible when a failure originates below the handler.",
+          "examples": [
+            "legal_document_not_found"
+          ],
+          "type": "string"
+        },
+        "recovery": {
+          "additionalProperties": {},
+          "description": "Actionable next step for the caller.",
+          "properties": {
+            "hint": {
+              "type": "string"
+            }
+          },
+          "required": [
+            "hint"
+          ],
+          "type": "object"
+        },
+        "retryable": {
+          "description": "Whether retrying may succeed.",
+          "type": "boolean"
+        }
+      },
+      "type": "object"
+    },
+    "message": {
+      "description": "Human-readable description of what went wrong.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "code",
+    "message"
+  ],
+  "type": "object"
+}
    • removedOutput schema / required
      Removed value: -[
-  "document",
-  "search_criteria",
-  "attachedDocumentCount"
-]
  3. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations readOnlyHint/idempotentHint already establish safety, so the description earns credit for adding context: it states the tool returns the record untouched and explains how search_legal's output is transformed. It doesn't cover error/missing-record behavior, but this is not a serious gap given the annotations and output schema.

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?

Three dense sentences, front-loaded with the core purpose, then a sibling comparison, then parameter conventions. No filler or repetition of schema enums.

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 two-parameter read tool with a full output schema and strong annotations, the description covers purpose, selection criteria, parameter derivation, and return behavior. Nothing critical an agent needs to call it correctly is absent.

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?

The schema already documents both params at 100% coverage, so the baseline is 3; the description adds value by explaining the plural/singular mapping from search results and noting that no is carried by every type (and repeated as ao_no for advisory opinions). This enriches the semantics of both fields beyond the 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?

States a specific verb ('Fetch'), a precise resource ('one FEC legal document in full'), and enumerates the five document types it accepts. It also distinguishes itself from openfec_search_legal, so an agent can tell the two apart without inspecting schemas.

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?

Names openfec_search_legal and contrasts its truncating behavior with this tool's untouched full-record return, effectively telling an agent to use this when the full record is needed. It also explains how to derive doc_type from a search result's document_type, which is direct usage 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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