front-asset-intel-mcp

Lightweight local TypeScript MCP server exposing precomputed asset research and rubric summaries for analyst agents.

This repo is intentionally runtime-small: the MCP server does not call an LLM, crawl the web, or regenerate research. It only serves validated local files.

Research workflow for new assets

The copied research workflow lives in docs/asset-investment-diligence/. Start with docs/asset-investment-diligence/RUN-RESEARCH.md when producing a new token or PT package.

New research is generated as an offline artifact first, normally under research-runs/<run-slug>/, not directly under data/assets/. For MCP-quality packages, run S1/S2 token diligence, include S4/S5 X/social research and S6 quantitative return context, and run S3 only when the asset is a Pendle PT / PT market. After review, import the curated result into data/assets/<asset-slug>/{manifest.json,summary.asset_risk_v1.json,research.md} and run npm test.

Related MCP server: OpenFin MCP

Why this exists

Long Markdown research reports are useful for diligence, but they are not a stable decision interface for an analyst agent. The server exposes a small discovery surface plus two data layers:

• list_available_assets — discovery JSON listing every static asset this MCP can answer about, accepted lookup values, and exact examples for calling summary/research when the agent already knows a symbol or address.

• get_asset_summary — compact rubric JSON with uniform questions, fixed scoring buckets, table-facing agent_display fields, per-rubric score/status/evidence-state fields, evidence snippets, blocking unknowns, per-topic comparable grade anchors, and a normalized return_context that carries the full local return/social/quant overlay.

• get_asset_research — full Markdown report for source review when the summary needs expansion, with the same return_context inline so the research view and JSON view use the same ROI layer.

Tools

list_available_assets

No input. Returns a static asset-capability map with:

• available asset count;

• each asset's canonical asset_id, slug, symbol, chain, addresses, aliases, and accepted lookup values;

• recommended get_asset_summary / get_asset_research examples for symbol-first, token-address-first, Pendle-market-address-first, and PT-address-first callers.

Use this when an agent is unsure which identifiers the MCP recognizes or needs a deterministic way to call the summary/research tools without web discovery.

get_asset_summary

Input accepts any one of:

• asset_id

• symbol

• slug / alias / token address / Pendle market address / PT address / chain-prefixed address

Returns precomputed JSON enriched with static rubric grade anchors from data/rubrics/asset_risk_v1.json and a runtime-built return_context from the same local summary/report package.

For table/ranking UIs, use the agent_display block first:

• agent_display.score_display — explains whether the table score is direct asset-quality evidence or fixed-return PT economics with inherited underlying risk shown separately.

• agent_display.decision_label — human-usable action label such as "Block Preview/Execute", "Conditional PT candidate", or "Do not underwrite" instead of the legacy coarse review_required bucket.

• agent_display.underwriting_status and agent_display.execution_automation_status — separate research/underwriting readiness from automation safety.

• agent_display.primary_blockers and agent_display.next_action — the concrete reason the row is not executable and what input is needed next.

rubric.score / rubric.decision_class remain for backward compatibility and deterministic score validation. Do not use those two fields alone as the table decision: PT rows expose a separate fixed-return table score, and review_required is only a legacy score-bucket class.

For analyst-agent scoring helper use, read dimensions[] as the main surface. Each dimension includes the current asset answer/score plus possible_grades: every fixed rubric bucket with condition, score range, score band, default status/evidence state, is_selected, and relation_to_selected. This lets an agent see, for example, that daily NAV attestations without granular reconciliation sit between weak monthly/no-granularity evidence and strong realtime/daily granular backing portfolio reporting.

For ROI/ranking use, read return_context as the normalized layer shared by both tools:

• kind: direct_or_variable_token_return for non-PT/direct token rows. It carries token_return_estimate with organic/variable ROI, fresh-farming points ROI where quantitatively modeled, expected-loss scenarios, exit cost, and risk-adjusted ROI/annualized return.

• kind: fixed_maturity_pt_return for Pendle PT rows. It carries pt_return_profile with gross ROI, APY/APR, expected-loss and exit-cost assumptions, break-even drawdown, liquidity, and risk-adjusted ROI/annualized return. Variable yield/incentive economics are explicitly separated into YT and are not counted in the PT-holder base ROI.

• Both kinds carry social_research_layer and quantitative_risk_return_layer when available, including X/social evidence and quantitative decision triggers.

For non-PT/simple-token rows, summaries also expose simple_token_return_estimate at the top level and mirrored in agent_display.simple_token_return_estimate, plus a compact agent_display.simple_token_return_display. These estimates separate: organic return from holding the token (organic_yield_apy_estimate, organic_roi_over_horizon), quantitatively backed fresh-farming points value (estimated_points_roi_over_horizon, estimated_points_annualized_return, points_roi_scenarios_over_horizon, points_farming_thesis), expected-loss scenarios (expected_loss_prior, expected_loss_prior_scenarios), exit cost, and risk-adjusted ROI before/after the base points case.

The normalized base formula is risk_adjusted_roi_after_base_points = organic_roi_over_horizon + estimated_points_roi_over_horizon - expected_loss_prior - exit_cost_assumption, then annualized linearly over horizon_days for table comparability. expected_loss_prior_scenarios keeps low/base/high loss cases so a conservative stress haircut is not mistaken for the only average estimate; risk_adjusted_roi_scenarios_after_base_points recomputes the same formula under the low-loss/base/high-loss loss cases.

Points are not shown as cheap safe floors. For points programs, points_farming_thesis.kind = fresh_quant_farming_thesis and the estimate must name program allocation, season timing, route multiplier, FDV scenarios, raw TVL proxy, weighted points-denominator scenarios, formula, freshness limits, and evidence. Where no confirmed points program exists, points_thesis_kind = no_confirmed_points_program and points ROI is 0, not omitted.

Example asset lookups:

• apxUSD

• apyUSD

• PRIME

• deSPXA

• USDC

• USDat

• sUSDat

• PT-apxUSD

• PT-apyUSD

• PT-USDat

• PT-sUSDat

• ethereum:0x98a878b1cd98131b271883b390f68d2c90674665

• 0xaf0349fb9b1ba07d34381870c59b560b31412660

• 0x30bb9ee8dc6aab322dc3a0d36063cbf06a9e5952

• 0x9afe7a057a09cf5da748d952078c9c99938b4329

• 0x91bc86899c8391b6caaf26535b9cd82efe49a189

get_asset_research

Same lookup input. Returns the full Markdown research report with the same normalized return_context used by get_asset_summary carried inline for auditability. This is not a separate shortcut estimate: both tools expose the same local return layer derived from the saved summary/report package.

For non-PT/direct token rows, the context is direct_or_variable_token_return and includes organic/variable ROI, fresh-farming points ROI when quantitatively modeled, expected-loss low/base/high bands, exit cost, risk-adjusted ROI, and social/X plus quantitative overlays. For PT rows, the context is fixed_maturity_pt_return and includes the PT fixed-return return_profile; PT research keeps simple_token_return_estimate unset and does not add token-points assumptions because PT variable yield/incentive economics are separated into YT.

Seed assets

• ethereum-apxusd — Apyx apxUSD token-level research and asset_risk_v1 summary, refreshed from the public rich report package.

• ethereum-pendle-pt-apxusd-2026-11-05 — Pendle PT apxUSD 05 Nov 2026 research and summary with fixed-return risk-adjusted APY / hurdle overlay.

• ethereum-apyusd — Apyx apyUSD public research package, including X/social and quantitative risk/return layers.

• ethereum-pendle-pt-apyusd-2026-08-27 — Pendle PT apyUSD 27 Aug 2026 report and summary, including the 83-day fixed-return recovery trade overlay.

• ethereum-prime — Hastra PRIME rich public report package normalized into the asset-quality rubric.

• base-despxa — Centrifuge deSPXA rich public report package normalized into the asset-quality rubric.

• ethereum-usdc — Circle USDC known-asset package with top-tier stablecoin/backing, tested-peg, issuer-control, and no-points direct-token return context.

• ethereum-usdat — Saturn USDat collateral package with Gearbox feed/oracle context, X/social layer, and public asset reports.

• ethereum-susdat — Saturn sUSDat collateral package with ERC-4626/feed context, X/social layer, and public asset reports.

• ethereum-pendle-pt-usdat-2026-08-27 — Pendle PT USDat 27 Aug 2026 PT market dossier plus quantitative fixed-return hurdle overlay.

• ethereum-pendle-pt-susdat-2026-08-27 — Pendle PT sUSDat 27 Aug 2026 PT market dossier plus quantitative fixed-return hurdle overlay.

PT markets reuse the underlying asset-risk rubric for inherited issuer/backing/control context, but table ranking uses PT-specific fixed-return economics. The PT adds a return_profile block plus optional social_research_layer and quantitative_risk_return_layer blocks: maturity, PT price, accounting-asset price, gross ROI, annualized return, expected-loss prior, risk-adjusted return after expected loss/exit cost, break-even drawdown, and liquidity snapshot. PT holders underwrite the fixed discount-to-maturity; variable yield is separated into YT and not part of the PT-holder return.

Data layout

data/
  rubrics/
    asset_risk_v1.json
  assets/
    <asset-slug>/
      manifest.json
      summary.asset_risk_v1.json
      research.md
src/
  server.ts
  registry.ts
  validate-data.ts
  smoke.ts

Rubric model

asset_risk_v1 totals 100 points:

• Backing / NAV evidence: 18

• Redemption and holder eligibility: 18

• Market liquidity and peg behavior: 18

• Issuer controls and governance: 14

• Oracle / accounting alignment: 10

• Audits and security review: 12

• Incidents and social stress: 10

Each dimension has fixed answer buckets and score ranges. Summaries store the selected bucket, score, score_band, dimension-level action status, evidence_state, evidence, confidence, blocking unknowns, and — where available — social_research_layer / quantitative_risk_return_layer overlays that expose fixed-return APY, hurdle, expected-loss, and risk-adjusted-return information without folding those economics into the 100-point asset-quality score.

Dimension status values are action-oriented:

• usable_for_review — this dimension has usable evidence and does not itself force a review gate.

• review_required — evidence is partial, stale, or size/holder-specific enough that an analyst must review it.

• block_automation — the dimension can be discussed, but automated Preview/Execute should not proceed until the missing input is resolved.

• cannot_underwrite — the dimension contains a material valuation/risk gap or negative evidence that prevents underwriting under current assumptions.

score_band is the scoring bucket quality (strong, partial, weak). evidence_state preserves whether the status came from verified support, partial support, source inconclusiveness, missing/unknown evidence, or negative evidence.

Install and verify

npm install
npm test

npm test runs:

1. TypeScript build.

2. Data validation against manifests/rubric schema.

3. Registry smoke lookups for list_available_assets, apxUSD, apyUSD, PRIME, deSPXA, USDat, sUSDat, and PT assets, including a chain-prefixed market-address lookup.

4. Real MCP stdio smoke test:

   • initializes the MCP server;

   • checks tools/list exposes list_available_assets, get_asset_summary, and get_asset_research;

   • calls list_available_assets and verifies symbol/address call guidance plus accepted lookup values;

   • calls get_asset_summary for APYx, Saturn, PRIME, and deSPXA assets;

   • calls get_asset_summary by a chain-prefixed PT market address to verify address-first callers can use the guidance;

   • verifies PT fixed-return table scores and risk-adjusted APY values for PT-apxUSD, PT-apyUSD, PT-USDat, and PT-sUSDat;

   • calls get_asset_research for all direct/variable-token reports and verifies the inline normalized return context mirrors the summary ROI basis (organic/variable ROI, fresh-farming points ROI, risk-adjusted ROI, and social/X context);

   • calls get_asset_research for PT reports and verifies the fixed-return risk-adjusted conclusions are present without simple-token points assumptions.

For only the MCP protocol smoke after a build:

npm run build
npm run smoke:mcp

Run as local MCP server

npm run build
node dist/server.js

Example MCP client command config:

{
  "mcpServers": {
    "front-asset-intel": {
      "command": "node",
      "args": ["/absolute/path/to/front-asset-intel-mcp/dist/server.js"]
    }
  }
}

From this workspace, the absolute command target is:

/Users/ilya/ai-assistant/projects/front-asset-intel-mcp/dist/server.js

Source lineage

Seed reports were copied or condensed from the public Front KB rich-report branch (de-snake/front-knowledge-base, commit b954049). The MCP evidence surface is local: data/assets/<asset-slug>/research.md plus summary.asset_risk_v1.json.

Summaries preserve coarse generated_from provenance, but manifest and summary evidence pointers intentionally point to local files or public URLs, not invisible upstream run paths.