get_recordFetch one record by id — for example, look up a FIPS 140 certificate's status, a state's boiler inspection rules, or a county's loan limit. Returns the full record with its source URL and verbatim source quote. Record ids appear in search_records results and in each record's url.
| Name | Required | Description | Default |
|---|---|---|---|
| record_id | Yes | The record's id field, e.g. "ak". | |
| dataset_slug | Yes | Dataset slug from list_datasets. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safe-read nature is clear. The description adds value by stating it returns the full record with source URL and verbatim quote, which annotations don't cover. For a simple read tool, this is sufficient behavioral disclosure.
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?
The description is two sentences long with no wasted words. It front-loads the core action and examples, then explains the return value and ID source in a logical order. Every sentence earns its place.
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 simple 2-parameter read tool with good annotations and no output schema, the description covers the return format and ID provenance. It is slightly lacking in explicitly noting that dataset_slug comes from list_datasets, but this is implied by the sibling tool. Overall, it is sufficiently complete for the tool's complexity.
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%, so the schema already documents both parameters (record_id and dataset_slug) adequately. The description mentions 'record ids appear in search_records results' but does not add new syntax or format details beyond the schema. Baseline 3 is appropriate.
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 clearly states that it fetches a single record by ID, provides concrete examples (FIPS 140 certificate, boiler rules, loan limits), and specifies that the returned record includes a source URL and verbatim quote. This distinguishes it from siblings like search_records (search) and list_datasets (listing datasets).
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?
The description explicitly says the record ID comes from search_records results or the record's URL, guiding the agent on where to find the ID. However, it does not mention when to prefer this over verify_quote or explicitly exclude alternative tools, leaving a minor gap in usage guidance.
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.
Each tool has a unique role: get_record retrieves by ID, list_datasets enumerates available datasets, search_records performs substring search within a dataset, and verify_quote checks claims against published records. There is no overlap or ambiguity between them.
All tool names follow the same verb_noun snake_case pattern (get_record, list_datasets, search_records, verify_quote), making the action and target clear and predictable.
Four tools is an appropriate size for a focused reference/verification server. Each tool serves a distinct step in the workflow, and none are redundant or missing.
The tool set covers the core flow of discovering datasets, searching records, retrieving specific records, and verifying claims. Minor gaps exist (e.g., no explicit dataset detail endpoint), but the tools are sufficient for the server's stated purpose.