A
licenseNot graded
qualityF
maintenanceVerifies claims with verdicts (supported/disputed/unverifiable), confidence scores, and cited sources by cross-referencing FoundryNet Data Network and web search.
MIT
Stipple — Reference Verification
by sh.stipple
Server Details
Fact-check citations: resolve, match, support claims. Arithmetic rechecked. Free to start.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- Sketchjar/stipple-mcp
- GitHub Stars
- 0
- Server Listing
- stipple-mcp
Available Tools
1 toolverify_referencesVerify a report's citationsARead-onlyInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| deep | No | ||
| text | No | ||
| filename | No | document.pdf | |
| bytes_b64 | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
A4.8/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation is present, and the description adds substantial behavior beyond it: server-side fetching, the three default checks (provenance, citation resolution against arXiv/Crossref, internal math recomputation), the deep=true mode with a time estimate (~a minute), the return shape (trust summary, per-item tables, permalink), and an explicit 'HONEST BOUNDARY' disclaiming that it reports coverage, not truth. This is model-level transparency.
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?
Despite its length, every sentence earns its place: purpose, usage triggers, input modes with tradeoffs, default-vs-deep behavior, returns, and limitations are each in their own paragraph with helpful flags like 'USE THIS WHEN' and 'HONEST BOUNDARY'. The description is front-loaded with purpose and contains no filler or repetition of the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (five parameters, three mutually exclusive input modes, no required params, zero schema coverage), the description is remarkably complete: it explains the mutual-exclusivity constraint, the deep-mode toggle, time cost, outputs, and interpretatin caveats. The output schema exists, so return values need not be spelled out. The only substantive gap is the undocumented filename parameter and the absence of any error-mode notes (e.g., failed URL fetch or malformed PDF).
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?
Schema description coverage is 0%, so the description carries the full burden, and it compensates well: url (public HTTP(S), text (pasted markdown/plain prose), bytes_b64 (base64 PDF with exact link-annotation URLs), and deep (default fast vs fetch-each-source mode) are all given meaningful semantics, including cost tradeoffs. However, the filename parameter is never mentioned anywhere in the description or schema, leaving one of five parameters undocumented.
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?
The opening line, 'Fact-check a document's REFERENCES and CLAIMS — built for AI-generated reports whose citations must be checked before they're trusted,' states a specific verb (fact-check), a specific resource (references and claims), and the target use case. There are no sibling tools to differentiate from, and the title 'Verify a report's citations' is reinforced rather than merely restated.
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?
The description includes an explicit 'USE THIS WHEN' block with concrete trigger scenarios and example user questions ('is this accurate / legit?', 'did the AI make this up?'), plus proactive guidance ('use it proactively before relying on any AI-written document'). It also routes input selection by explaining when to choose url vs text vs bytes_b64, so an agent knows how to invoke it correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as
io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon the same origin as the connector, then choose Check HTTP challenge.DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Connectors
Free mechanical checks for AI text: unnamed counts, dangling references, bad arithmetic, misquotes.
Independent fact-checking for AI answers: a verdict and sources for every claim.
Fact-check claims against independent sources — fast verdict or deep multi-step investigation.
Cross-check a factual claim against a verified knowledge graph before you assert it. Never guesses.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceVerifies citations in reference lists by checking DOIs against public registries to catch AI-hallucinated or mismatched citations.MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to verify claims deterministically by computing arithmetic, ratios, and dates and matching statements against provided sources, returning a confidence ladder of certain, source-backed, or unverifiable.MIT
- AlicenseAqualityBmaintenanceValidate AI claims against live data: check endpoints, count competitors, and test hypotheses. Includes free and paid tools via x402.16MIT
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4.8/5.0
Disambiguation5/5
Only one tool exists, so there is no possibility of ambiguity. The tool's purpose is clearly defined.
Naming Consistency5/5
With a single tool, naming is consistent by default. The name 'verify_references' is clear and follows a verb_noun pattern.
Tool Count4/5
One tool for a narrow domain is borderline thin, but the tool is comprehensive and well-scoped for reference verification, so it is reasonable.
Completeness5/5
The tool covers all aspects of reference verification: input methods (URL, text, base64), citation resolution, math checking, deep evidence fetching, and provides a shareable report. No obvious gaps for its stated purpose.