Agenda Intelligence MD

Name: Agenda Intelligence
Author: vassiliylakhonin

Product entry point and evidence-discipline layer for strategic intelligence agents.

PyPI version License: MIT

Agenda Intelligence is a trusted geopolitical intelligence layer for agentic workflows. Auditable, structured, decision-grade risk memos with evidence discipline that generic LLMs lack. One MCP server. Structured input/output. Built-in validation.

This repository hosts the product entry point: JSON schemas defining the request/response contract, the stdio MCP server exposing analyze, validate_memo, list_signals, get_signal, and deep_dive, plus the original validation surface (briefs, evidence packs, audits, lenses, source plans). Reasoning content is bundled as in-repo references derived from sibling repositories: Global Think Tank Analyst (method), Central Asia + Caspian and Gulf + Middle East (regional specialists routed by query geography).

What this is

MCP product shell — analyze accepts a structured request (agenda-request.schema.json), routes geography to the relevant regional specialist, assembles a system prompt, and returns a memo validated against agenda-memo.schema.json
Markdown protocol — structured reasoning workflow for agents (Agenda-Intelligence.md)
JSON schemas — request/memo product contract plus validators for briefs, evidence packs, audits, signals, memory cards, lenses
CLI — validate-brief, validate-evidence, source-categories, source-coverage, audit-claims, score, bench, doctor (30+ commands)
MCP server — stdio server exposing 16 tools across the validation and product layers
Eval kit — rubric, LLM-judge prompt, human checklist, benchmark harness
Source policy — per-claim provenance tags (Axis A/B), source requirements for 12 categories

What this is not

Not a factuality verifier — checks structure, not truth
Not an autonomous news agent or source retriever
Not a source reputation scorer or live news gatherer
Not a replacement for analyst judgment
Not a compliance, legal, or financial advisory product

Quickstart

pip install agenda-intelligence-md
# Add the optional [llm] extra to let the MCP `analyze` tool call the
# Anthropic API directly (otherwise the host model completes from the
# returned system_prompt):
#   pip install "agenda-intelligence-md[llm]"
#   export ANTHROPIC_API_KEY=...
#
# Or pinned wheel:
# pip install https://github.com/vassiliylakhonin/agenda-intelligence-md/releases/download/v0.8.2/agenda_intelligence_md-0.8.2-py3-none-any.whl

agenda-intelligence validate-brief examples/agenda-brief.json
agenda-intelligence score examples/agenda-brief.json --evidence examples/source/evidence-pack.json
agenda-intelligence bench examples/source-backed --strict --min-score 80
agenda-intelligence doctor
agenda-intelligence mcp-config --client cursor

Benchmark baseline

20 source-backed cases, reproduced with agenda-intelligence bench examples/source-backed/:

Metric	Value
Cases	20
Mean score	87.6 / 100
Min / max	84 / 91
Schema-valid	100%
With evidence pack	100%
With claim-level audit	100%
With source category	100%
Mean source coverage	14.8%
Source coverage gap cases	20
Orphan evidence refs	0

Heuristic scores are uncalibrated and not validated against expert judgment. They evaluate structure, evidence labeling, source-coverage diagnostics, and decision-readiness — not factual truth.

Flagship example: examples/source-backed/eu-ai-act.md — brief + evidence pack + claim-level audit using illustrative sources. Before / after pairs: examples/before-after/.

Verification Contract

verify-quotes checks whether a cited quote or excerpt appears in supplied local text, or in text fetched from an already-specified URL when --fetch is used. It does not discover sources, score source reputation, gather live news, or decide whether a claim is true in the world.

Schemas

Schema	Purpose
`agenda-brief.schema.json`	Brief structure
`evidence-pack.schema.json`	Evidence pack
`evidence-audit.schema.json`	Claim-level audit
`signal-tracker.schema.json`	Signal lifecycle
`memory-card.schema.json`	AnalysisBank cards
`lens-manifest.schema.json`	Lens manifest
`signal-classification.schema.json`	Signal taxonomy

MCP

Stdio MCP server with 16 tools. Full docs and wire-protocol verification: MCP.md. Client setup: docs/integrations/mcp.md.

Tool	What it does
`validate_brief`	Validate a brief dict against `agenda-brief.schema.json`
`validate_evidence`	Validate an evidence-pack dict against `evidence-pack.schema.json`
`audit_claims`	Check claim-level audit: support distribution, orphan refs, unsupported claims
`score_output`	Heuristic score for structure, evidence labeling, decision-readiness
`get_protocol`	Return the full Agenda-Intelligence.md reasoning protocol
`list_source_categories`	List source requirement categories before calling `source_plan`
`source_plan`	Generate a source plan for a given topic
`source_coverage`	Diagnose evidence-pack coverage against category source requirements
`verify_quotes`	Check cited quote fragments in caller-provided text
`list_lenses`	List available lens packs
`get_lens`	Return a specific lens pack by name
`analyze`	Product-shell pipeline: validate request, route modules, assemble prompt, optionally call LLM, validate memo
`validate_memo`	Validate an Agenda memo against `agenda-memo.schema.json`
`list_signals`	List vendored signal archive entries
`get_signal`	Return a vendored signal markdown file by id
`deep_dive`	Planned v2 placeholder directing callers to `analyze` depth modes

Status

Component	Status
Markdown protocol, JSON schemas	Stable
CLI (validate, score, bench, audit, doctor)	Stable
MCP stdio server	Stable
Evidence-audit schema (claim-level)	Stable
Signal-tracker schema (lifecycle)	Stable
Heuristic scoring	Stable (uncalibrated)
Live source retrieval	Not implemented
Factual-truth verification	Not in scope

Documentation

Resource	Link
Quickstart	`docs/quickstart.md`
Tutorial	`docs/tutorial.md`
Evaluation layers	`docs/evaluation.md`
Factual verification boundary	`docs/factual-verification.md`
Source plan coverage boundary	`docs/source-plan-coverage.md`
Evidence audit	`docs/evidence-audit.md`
Threat model	`docs/threat-model.md`
Integrations	`docs/integrations/`
Use-cases	`docs/use-cases/`
Agent contract	`AGENTS.md`
Adoption guide	`ADOPTION.md`
Changelog	`CHANGELOG.md`
Roadmap	`ROADMAP.md`

Repository layout

agenda-intelligence-md/
├─ src/agenda_intelligence/   # Python package (CLI + MCP server)
├─ schemas/                   # JSON schemas
├─ examples/                  # briefs, evidence packs, before/after
├─ skills/                    # OpenClaw skill wrappers
├─ evals/                     # rubric, judge prompt, benchmark
├─ analysis-bank/             # agent persistent memory (memory-card schema, see schemas/memory-card.schema.json)
├─ docs/                      # guides, integrations, use-cases
├─ scripts/                   # dev and CI helpers
└─ tests/                     # pytest suite

Contact

Vassiliy Lakhonin — Almaty, Kazakhstan (UTC+5)

Portfolio · For analysts · Email · LinkedIn · GitHub

Issues, PRs, and eval-case contributions are welcome.

License

MIT.

Disclaimer. This toolkit is for informational and educational purposes only. It does not constitute investment, financial, legal, compliance, or trading advice. It does not verify factual truth, predict outcomes, or replace professional judgment. Use at your own risk.

mcp-name: io.github.vassiliylakhonin/agenda-intelligence-md

Agenda Intelligence