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ianderso

nara-catalog-mcp

by ianderso

get_transcriptions

Read-only

Read volunteer transcriptions for a NARA Catalog record's pages, including handwritten documents OCR cannot read. Use the text as a lead and verify names or dates against the page image.

Instructions

Read the citizen transcriptions of a record's pages.

Volunteers have transcribed handwritten pension files, service records and letters that OCR cannot touch, which makes this the fastest way into a document in copperplate. It is still a lead, not evidence: a transcription is one stranger's reading, unreviewed, and names are exactly where such a reading goes wrong. Check the page image before citing a name or date you found here, and cite the image.

Returns one entry per transcription with its text, its author and the page it belongs to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
naidYesThe record's NAID.
refreshNoTrue re-reads from the Catalog instead of the cache, spending one call and replacing the cached copy. The cache never expires on its own, so use this when the answer may have changed since you last asked.
max_charsNoCharacters of each transcription to return. 0 returns the whole thing.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations cover readOnly/openWorld, but the description adds real behavioral context the annotations cannot: the content is unreviewed volunteer output, names are the most error-prone element, and the correct workflow is to verify against the page image. It does not restate the mutation/caching behavior that the schema already documents.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first sentence, followed by a caveat and a return summary. The prose is somewhat embellished ('copperplate', 'one stranger's reading'), which costs a point against strict economy, but each sentence carries usable information.

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 no output schema, the description supplies the return structure, the trustworthiness caveat, and the verification workflow. For a simple three-parameter read tool this is fully sufficient to call and interpret the results 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?

Schema description coverage is 100%, so naid, refresh and max_chars are already fully documented in the schema. The description adds return-shape detail (one entry per transcription with text, author, page) but no additional parameter semantics, matching the baseline for full schema coverage.

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 and resource ('Read the citizen transcriptions of a record's pages') and scopes it to a single record via the required naid. The explanation that OCR cannot touch handwritten files clearly distinguishes it from the OCR-oriented sibling get_extracted_text.

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?

Gives strong contextual guidance: this is the fastest route into handwritten documents, but it is 'still a lead, not evidence' and the image should be checked before citing names or dates. It lacks an explicit when-not-to-use or a named alternative (e.g. get_extracted_text for printed text), which keeps it shy of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.