OpenWarrant — Reference Verification
Server Details
Verify a document's citations resolve and match, recheck its math, flag unsupported claims.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.7/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools, and the tool's purpose is clearly defined.
The single tool name "verify_references" follows a clear verb_noun pattern, consistent with good naming conventions.
One tool is borderline thin for a verification service, but the tool itself is feature-rich and covers multiple verification modes, which mitigates the low count.
The tool covers provenance, citation resolution, math checking, and optional deep support analysis, leaving few obvious gaps for its stated reference-verification domain.
Available Tools
1 toolverify_referencesAInspect
Fact-check a document's REFERENCES and CLAIMS — built for AI-generated reports whose citations must be checked before they're trusted.
USE THIS WHEN someone shares a report, article, whitepaper, or deep-research export (or a
link to one) and asks: is this accurate / legit? are these citations real? fact-check this.
did the AI make this up? Also use it proactively before relying on any AI-written document.
Provide the document ONE way: `url` (a public http(s) link to a PDF or web page — fetched
server-side, the cheapest call: no need to download or encode anything), `text` (pasted
markdown/plain prose), OR `bytes_b64` (a base64 PDF; URLs are read from the PDF's link
annotations, so they're exact). Default (fast): provenance (is it a ChatGPT deep-research
export?), citation resolution (live / archived / dead, papers matched against arXiv/Crossref
to catch 'real ID, wrong paper'), and internal MATH (recompute the doc's own arithmetic).
Set `deep=true` to also fetch each cited source and judge whether it SUPPORTS or CONTRADICTS
the claim (slower, ~a minute).
Returns a trust summary, per-item tables, and a shareable `permalink` to the public
fact-check record. HONEST BOUNDARY: this reports verification COVERAGE, not truth —
'supported' means evidence-backed (not necessarily true) and 'unsupported' means no evidence
found (not necessarily false). It tells a reviewer WHERE to look; it does not bless the
document, and it never affects the fraud risk band.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| deep | No | ||
| text | No | ||
| filename | No | document.pdf | |
| bytes_b64 | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully carries the behavioral burden. It details default checks (provenance, citation resolution, math) and deep mode (fetching sources, judging support/contradiction). The 'HONEST BOUNDARY' section clarifies what the results mean and what they don't, adding transparency.
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 concise yet comprehensive. It front-loads the purpose, then explains input options, default behavior, deep mode, and limitations in a logical flow. Every sentence adds value.
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?
Given the output schema exists, return values are covered structurally. The description compensates for the 0% schema coverage by explaining all key parameters except ‘filename’. It addresses the tool's complexity and provides necessary context for correct usage.
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 0%, so the description must explain parameters. It explains ‘text’ and ‘bytes_b64’ as input options, and ‘deep’ as a boolean controlling depth. However, the ‘filename’ parameter is not mentioned, leaving a gap.
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 'Verify a document's REFERENCES and CLAIMS' and specifies it's for AI-generated reports. It lists specific verification tasks (provenance, citation resolution, math) and distinguishes between default and deep modes.
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 explains when to use the tool (to check citations before trusting) and contrasts the default fast mode with the slower deep mode. However, it does not explicitly state when not to use it or mention alternatives (though there are no sibling tools).
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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