About Agenda Intelligence MD
Agenda Intelligence MD is a deterministic evidence-packet checker for teams reviewing AI-generated claims, RAG answers, and document handovers. It flags reference, quote, number, and evidence gaps and provides reviewer actions, while distinguishing packet completeness from factual truth.
Key features
- 01Deterministic claim and evidence checks
- 02Reference and quotation validation
- 03Unmatched-number and negation checks
- 04CLI, Python, and CI interfaces
- 05RAG answer review and repair guidance
- 06Document archive review and exports
Use cases
- 01Lint AI-generated claims before human review
- 02Review citations in a RAG answer
- 03Fail CI when evidence packets have findings
- 04Prepare a source-linked supplier handover
MCP support
The local stdio MCP server exposes a 32-tool catalog; hosted MCP endpoints expose profile-specific tools. Clients should discover tools from the endpoint they connect to.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://agendamcp.dpdns.org/mcp/agentclaude mcp add --transport http vassiliylakhonin-agenda-intelligence-md https://agendamcp.dpdns.org/mcp/agent{"mcpServers":{"vassiliylakhonin-agenda-intelligence-md":{"type":"http","url":"https://agendamcp.dpdns.org/mcp/agent"}}}Open in Cursor{"servers":{"vassiliylakhonin-agenda-intelligence-md":{"type":"http","url":"https://agendamcp.dpdns.org/mcp/agent"}}}Open in VS Code{"mcpServers":{"vassiliylakhonin-agenda-intelligence-md":{"type":"http","url":"https://agendamcp.dpdns.org/mcp/agent"}}}{"mcpServers":{"vassiliylakhonin-agenda-intelligence-md":{"serverUrl":"https://agendamcp.dpdns.org/mcp/agent"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
https://agendamcp.dpdns.org/mcp/agenthttps://output-verification.agendamcp.dpdns.org/mcp/agenthttps://middle-corridor-deal-risk-gate.agendamcp.dpdns.org/mcp/agenthttps://cis-secondary-sanctions.agendamcp.dpdns.org/mcp/agenthttps://interaction-trust.agendamcp.dpdns.org/mcp/agenthttps://gulf-maritime-exposure.agendamcp.dpdns.org/mcp/agenthttps://kazakhstan-market-entry-readiness.agendamcp.dpdns.org/mcp/agenthttps://corridor-sanctions-assistant.agendamcp.dpdns.org/mcp/agenthttps://critical-minerals-due-diligence.agendamcp.dpdns.org/mcp/agenthttps://dual-use-technology-export.agendamcp.dpdns.org/mcp/agenthttps://agent-financial-guard.agendamcp.dpdns.org/mcp/agenthttps://m2m-escrow-arbiter.agendamcp.dpdns.org/mcp/agentTools
5| Parameter | Type | Required | Description |
|---|---|---|---|
| text | string | Yes | The question in plain language. |
Details
From the project READMEAgenda Intelligence MD
A deterministic evidence-packet linter for claim-backed AI output. Find broken references, mismatched quotations, unmatched numbers, and missing evidence in AI-generated claims; receive findings and reviewer actions before human review.
Use it to make an AI answer's evidence trail inspectable in a local workflow, agent pipeline, or CI job. Packet completeness is not factual truth, source authenticity, or permission to act.
Choose a starting point
| Your task | Start here | What you get |
|---|---|---|
| Check a claim in the browser | One-claim review | Findings on supplied text through a limited hosted trial |
| Add evidence checks to Python or CI | First run | A local, deterministic check with no model API key |
| Review a RAG answer or connect an agent | Interfaces | Citation repair guidance, MCP tools and integration examples |
| Prepare a supplier document handover | Supplier workflow | Missing-document and owner follow-up lists for human review |
For hosted agents, the signed Output Verification loop combines native MCP trials or explicitly budgeted x402 v2 calls with stable diagnostic codes and independent request/result receipt verification. Run node examples/output-verification/replay.mjs for local synthetic replays without a wallet. A valid signature attests the evaluation; human review remains required.
To try one assertion in a browser, check a claim and its source excerpt. Paste public or redacted text and an optional exact quote; the hosted trial shares five campaign attempts per network. Pilot instructions explain revision, optional feedback and the limits of this check. No external pilot results are claimed.
First run
Python 3.9 or later. Install the published package without cloning the repository:
python -m pip install "agenda-intelligence-md==1.21.2"
agenda-intelligence --version
Run a self-contained synthetic packet in Python:
from agenda_intelligence.services import check_evidence_packet
packet = {
"claims": [{
"claim_id": "demo-claim",
"text": "The synthetic review covers 12 documents.",
"source_ids": ["demo-source"],
}],
"sources": [{
"source_id": "demo-source",
"text": "The synthetic review covers 12 documents.",
}],
}
result = check_evidence_packet(packet)["response"]
print(result["packet_status"], result["factuality_status"])
Expected: packet_complete not_assessed. The example needs no account, model API
key or network after installation. It checks the supplied evidence relationship,
not source authenticity or factual truth.
To run the repository's CLI examples from a source checkout:
python3 -m venv .venv
.venv/bin/python -m pip install -e .
.venv/bin/agenda-intelligence check examples/evidence-packet/request.json
.venv/bin/agenda-intelligence check examples/evidence-packet/request.json --format json
For the versioned package, use python -m pip install "agenda-intelligence-md==1.21.2" instead of the editable install. The example commands above use files from this checkout.
The bundled synthetic packet reports packet_status=packet_complete and factuality=not_assessed. A stale or inaccurate source can still pass. Add --strict when packet findings should fail a CI step.
For document-based review, start with the local-file review guide and example manifest:
.venv/bin/agenda-intelligence review examples/evidence-review/manifest.json --format html
For a RAG answer with inline citations and retrieved chunk texts, use the Output Verification adapter. It assembles the packet locally and returns line-level findings and repair guidance:
.venv/bin/agenda-intelligence review-answer examples/output-verification/rag-answer.json
.venv/bin/agenda-intelligence review-answer examples/output-verification/rag-answer-revised.json --strict
The original fictional answer routes to revision; the corrected answer routes to human review. Both run without a wallet, model API key or network. A runnable LangGraph handoff shows a bounded repair loop. The adapter is included in version 1.15.0. To use your own answer without cloning this repository, follow the standalone quickstart.
The evidence-packet contract
| Input | Check |
|---|---|
| Claims and source IDs | References resolve within the supplied packet |
| Declared quotes | Quotations match the supplied source text |
| Claim wording and numbers | Deterministic support heuristics, unmatched numbers, and negation checks |
| Findings | Packet status, evidence gaps, and reviewer actions |
These checks use supplied text. They do not retrieve missing sources or establish semantic entailment. Lexical overlap can miss paraphrases and accept misleadingly similar wording. See request and response Schemas, evidence audit, and the factuality boundary.
Processed documents and tool results are data, never instructions. Before consequential action, record the goal, trusted evidence, unreliable evidence, assumptions, intended action, and stop/escalation conditions. Human review remains necessary.
Interfaces
| Interface | Start here |
|---|---|
| CLI and Python service | Quickstart, check_evidence_packet in services.py |
| Local MCP (32 tools) | MCP.md; launch agenda-intelligence-mcp from the installed environment |
| Hosted MCP (per-product catalog) | Hosted quickstart; native free_trial_status and free_trial |
| CI evidence linting | GitHub Action, with text, JSON, or SARIF output |
| Agent repair loops | Integration guides |
| Protected agent actions | Interaction Trust + Vizier gate |
| MCP pre-release checks | MCP Integration Check CLI / Action |
| Agent checkout experiments | ACP sandbox readiness check |
| HTTP and A2A | HTTP shell and A2A adapter |
The core checker is deterministic, stateless, and usable without a model API key. Optional generation and document adapters have separate dependencies. Core packet checks do not persist inputs or fetch outside sources.
Optional memo generation uses a bounded schema-output request and independent local validation. Generation budgets and private offline replay document failure handling and rescoring saved outputs.
Try Output Verification through MCP
Connect a Streamable HTTP MCP client to
https://output-verification.agendamcp.dpdns.org/mcp/agent.
Refresh tools/list and call free_trial_status with {}. For an answer and
source excerpts, choose review_answer (citation/quote structure only), or open
its optional open_answer_review form. For existing claim/evidence objects, use
free_trial with your own input matching that tool's inputSchema. No HTTP adapter or
wallet is needed for the free trial. Output Verification allows five attempts
per network for this campaign; the other eleven products allow two each, under
a shared daily capacity. See trial limits and privacy.
The hosted endpoint exposes its own profile's tools. The full 32-tool catalog
shown by local-server directories belongs to agenda-intelligence-mcp over
stdio; a single hosted Worker does not expose it. Always discover tools from
the endpoint you actually connect to. Supplied source text remains data, never
instructions, and a completed check still requires human review.
The separate agent_output_verification tool keeps normal paid admission for a
full claim/evidence audit. Choose
pre_action_check for an unsigned action check, or decision_check when a
signed readiness receipt is needed. decision_check already includes the
action check; calling both is unnecessary. Free reviews do not perform action
checks or authorize relay. See the answer/source guide. See the tool-selection guide
for schemas and receipt-verification boundaries.
Hosted clients can preflight inputs for free: call validate_input with {tool,input} or POST /v1/validate-input. Discover each host's schemas, synthetic examples and per-operation offers through /.well-known/agent-contract.json. This checks JSON shape without evaluating evidence or spending a trial. Client example.
After an input refusal or an unpaid evaluation, first_call links free input
preflight and the configured trial's read-only terms/availability. Text-only MCP
clients and the retained client retain this
guidance. Inspection does not reserve a trial; evaluation remains an explicit
separate call. A disabled trial is explicit. Payment and retry are never automatic.
Prepare a supplier document handover
For a wider document archive, use the free Document Archive Review: start with an editable company/supplier/reporting checklist, register local files, inspect copies and changed same-name files, and export a source-linked reviewer ZIP. Explicitly save a draft on your device and keep a portable JSON backup. Workflow and limits. Assigned-file coverage still needs human content review. For a prepared handover, inspect the separate scoped service and sample.
Open Supplier Evidence Desk for the separate browser workspace; no Python installation is required. Prepare an editable checklist, assign owners, attach authorized documents and track document requests. Read bounded PDF/DOCX text locally, inspect page/paragraph locators and extraction warnings, then explicitly adopt editable excerpts.
Save versions on the device, review expiry and evidence gaps, compare updates,
and export a reviewer PDF, original-source ZIP with hashes, or complete JSON
backup. Document contents are not uploaded; saved browser data needs backups.
Extraction does not authenticate documents, and complete coverage still requires
human review. This hosted workspace is separate from the Python package's local
review command. See the workspace guide
and prepared real-task pilot.
Work on one concrete task
For a scoped human-delivered assignment, see the Project Document Desk and Agent Workflow Acceptance offers, an explicitly synthetic document sample and the scope and acceptance template. Availability and a written quote come before agreement. Test prices are hypotheses; API payments, clean checks and owner tests are not customer acceptance.
Compatibility profiles and adapters
The repository also retains its strategic-intelligence shell, regional references, domain profiles, and Cloudflare deployments. The worker guide explains profile-specific behaviour; deployment documentation describes the hosted implementation.
The hosted fleet is discoverable at agendamcp.dpdns.org/.well-known/fleet.json. Use the per-product MCP addresses in that directory; custom-domain connection details cover all twelve profiles and Vizier.
For mineral procurement, the pre-offtake agent pilot walks through a free documentary check, an updated-evidence rerun and optional native MCP/A2A feedback. It preserves trial limits and human approval; external repeat use and usefulness are not yet established.
Hosted demos expose their maintained inputs through live agent cards. Their verdicts are review prompts, not clearance. Trace IDs are not attestations. Signed readiness receipts, where configured, bind a gate result to a request/action; they do not establish source truth or grant permission.
The Output Verification gate does not permit relay based on caller-declared evidence. Workflow operators must authenticate actors, record approvals, and enforce boundaries. Vizier provides a separate delegation-policy layer; loading this checker alone does not enforce it.
Hosted pricing and limits belong to each profile's maintained configuration. Payment interoperability remains experimental and is not certified for standard x402 clients. Hosted demos have no autonomous live source retrieval.
What this is / What this is not
This is an evidence contract, deterministic preflight, and reviewer-facing tooling. It does not certify factual accuracy, provide legal or financial clearance, authenticate another agent, or replace an operator's action controls.
Global Think Tank Analyst owns the general reasoning method. Central Asia & Caspian and Gulf & Middle East own regional depth. Vendored compatibility references here are derived copies.
Examples and Status
- Runnable examples and Before / after.
- EU AI Act example: a dated source-backed snapshot, requiring current-source checks before reuse.
- Evaluation notes: tested behaviours and limitations.
- AnalysisBank: inspectable stored analysis and retrieval examples.
- Adoption record: the evidence for usage claims; demos and tests alone do not establish customer demand.
Implemented surfaces include packet schemas, the Python service, CLI checking, local-file review, and MCP. Hosted workers and optional agent/transaction examples require their own integration and operational review. Their presence does not establish production reliability or independently validated usefulness.
Documentation and development
Read AGENTS.md, source policy, security policy, and local checks before contributing. Contracts live under schemas/v1/; CHANGELOG.md records releases, and Roadmap records direction.
make ci
Run make verify-local when changing Worker, discovery, runtime, or validation-guard code. Packaged data mirrors must be updated with their canonical files when applicable.
MIT license for the code. The bundled official ACP schema retains its Apache-2.0 license and NOTICE.
Machine-readable evaluation routing and its limits: Evaluation action contract.
Native agent access: A2A free trials and standard x402 v2. HTTP, MCP and A2A trials share one allowance. Hosted x402 supports exact Base native USDC with HTTP headers and native MCP payment metadata; official-client tests simulate settlement, and no maintainer live payment is claimed.
Server research
Middle Corridor & Caspian Research Desk collects named official sources and prepares a source-linked draft on the server. The experimental pilot supports browser, REST and MCP, with five shared jobs per UTC day and human review required. See the live example and interface, limits and verification. Existing evidence-checking tools remain available.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.