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 4.6/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of confusion between tools. The single tool has a clearly distinct purpose.
The tool name 'verify_references' follows a clear verb_noun pattern. With only one tool, naming consistency is perfect.
The server has only one tool, which is minimal but still reasonable given its focused scope of reference verification. The tool is comprehensive, covering multiple input methods and verification modes.
The tool appears to cover all necessary aspects of reference verification: supporting multiple input formats (URL, text, bytes), citation resolution, math checking, and optional deep source verification. No obvious gaps in the 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?
Despite no annotations, the description thoroughly discloses behavioral traits: how input (url/text/bytes_b64) is processed, default vs. deep mode behavior, and importantly the HONEST BOUNDARY section explaining what the output represents and doesn't represent. No contradictions.
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?
Description is somewhat long but well-structured: starts with purpose, then usage, then input methods, then behavior and boundaries. Each sentence adds value, no redundancy.
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 presence of an output schema (as per context signals), the description doesn't need to detail return values, but it does mention key output features (trust summary, per-item tables, permalink). Covers input, processing modes, and limitations comprehensively.
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?
With 0% schema description coverage, the description compensates well by explaining 'url', 'text', 'bytes_b64' parameters and their usage semantics. 'deep' parameter is described. Only 'filename' is not explained, but it's minor.
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?
Description clearly identifies the verb 'fact-check' and resource 'document's REFERENCES and CLAIMS', explicitly targeting AI-generated reports. It distinguishes itself by stating its specific use case: verifying citations and claims in AI-written documents.
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?
Provides explicit guidance on when to use the tool ('USE THIS WHEN someone shares a report...') with concrete examples. Also tells what to expect regarding boundaries ('it does not bless the document'), though it doesn't explicitly state when not to use it.
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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