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CLI, MCP server, and JSON schemas for validating and auditing strategic-risk AI agent output

Project description

Agenda Intelligence MD

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

PyPI version License: MIT

Agenda Intelligence is an MCP product shell and evidence-discipline layer for strategic intelligence agents. Structured request/memo contract, geography-routed reasoning, schema validation, evidence audit, and scoring. One MCP server. Structured input/output. No live retrieval, no factual verification — those are explicit non-goals before v1.0.

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 shellanalyze 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
  • CLIvalidate-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, agent-eval methodology
  • 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/v0.9.0/agenda_intelligence_md-0.9.0-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
Agent-eval methodology docs/agent-eval-methodology.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

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