Santismm Knowledge — Harness Engineering, Agentic AI & Governance

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

• Connection: Runs in the cloud
• Transport: Streamable HTTP
• Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

You need

• Runs in the cloudNothing to install; it connects over the internet

Which app do you use?

https://santismm.com/mcp

claude mcp add --transport http knowledge https://santismm.com/mcp

{
  "mcpServers": {
    "knowledge": {
      "url": "https://santismm.com/mcp"
    }
  }
}

{
  "servers": {
    "knowledge": {
      "type": "http",
      "url": "https://santismm.com/mcp"
    }
  }
}

https://santismm.com/mcp

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Tool names and descriptions exactly as the server reported them on Nov 7, 2026. The maker wrote them, not us; we show them as plain text.

• get_author_profile

  Get the author's professional profile as structured facts: name and the other forms it takes, current role with its start date, previous organisations, the frameworks he is the author of with their canonical URLs, how to cite this work, the licence, and the date these facts were

• get_overview

  Get the complete MCP map — start here. Returns the five-domain core plus the separate Article, Labs, Homeric Atlas, claim-registry, AI-benchmark and Physical AI surfaces, with their tools, identifiers, citation rules, languages, licence and bulk-ingest URLs. Federated surfaces th

• search_all

  Search the core corpus, first-party essays, executable Labs, epistemic claims, the Homeric Atlas, the AI benchmarks (professions and benchmark catalogue) and Physical AI (robot platforms, documented deployments, deployment guide) in one call. Use this first when a natural-languag

• list_knowledge

  List all knowledge units (concepts on agentic & enterprise AI) with slug, category, title, summary and Evidence-First provenance. Use this to browse the domain; use `search` when you have a question rather than a slug.

• get_knowledge

  Get one knowledge unit by slug. Returns the full entry, or a single-locale body if locale is given. Use this once `search` or `list_knowledge` has given you a slug.

• list_patterns

  List all Enterprise AI patterns (reusable agentic design patterns) with slug, category, name, summary and provenance. Use this to browse the catalogue; use `search` when you are looking for a pattern that solves a problem.

• get_pattern

  Get one Enterprise AI pattern by slug (includes problem, solution, KPIs, failure modes, lessons). Use this once `search` or `list_patterns` has given you a slug.

• list_architectures

  List all reference architectures (end-to-end enterprise agentic blueprints) with id, slug, category, name, summary and provenance. Use this to browse the blueprints; use `search` when you have a use case rather than a name.

• get_architecture

  Get one reference architecture by slug (includes the request flow, reference scenario, KPIs, cost & scaling, and the patterns/knowledge it composes). Use this once `search` or `list_architectures` has given you a slug.

• list_governance

  List all AI governance units (regulations, standards, frameworks, playbooks — EU AI Act, ISO 42001, NIST AI RMF, agentic checklist) with id, slug, category, name and summary. Use this to browse the regulations and standards; use `search` for an obligation or control.

• get_governance

  Get one AI governance unit by slug (includes scope, key requirements, implementable controls, a checklist and common pitfalls). Use this once `search` or `list_governance` has given you a slug.

• list_handbook

  List the Harness Engineering Handbook chapters (HRN-001…): the canonical long-form corpus on harness engineering — definition, history, taxonomy, principles, memory, observability, evaluation, governance, planning, orchestration, security, case studies, glossary and bibliography.

• get_handbook

  Get one Harness Engineering Handbook chapter, by id (e.g. 'HRN-001') or slug. Returns the full Markdown body plus its provenance and related ids. Use this once `search` or `list_handbook` has given you an id.

• search

  Ranked keyword search across the whole corpus (knowledge, patterns, architectures, governance and the handbook). Matches every language and ignores accents, so query in the user's own words. Each hit carries a relevance score and the fields it matched; follow up with the matching

• list_articles

  List every long-form essay published on articles.santismm.com, with language, dates, topics and citable canonical URLs. Use this to browse the essay catalogue; use `search_articles` when you have a topic rather than a slug.

• get_article

  Get one complete essay by slug, including its clean Markdown-like body, metadata, licence context and canonical URL. Use this after `list_articles` or `search_articles` has returned the slug you need.

• search_articles

  Ranked, accent-insensitive full-text search over every first-party essay, including titles, summaries, topics and bodies. Use this when you need long-form analysis about a topic; follow with `get_article` for the complete essay.

• list_professions

  List the professions that have a ranking of frontier AI models built from independent benchmark results (lawyer, accountant, developer, salesperson, translator, engineer…), each marked as a direct or an indirect evaluation, with the model that ranks first today. Use this to find

• recommend_model_for_profession

  Get the ranking of frontier AI models for one profession: the best result, the runner-up, the best value and the best open-weights model, the full table with data coverage and price, which tasks each benchmark measures and which tasks no benchmark measures yet. Use this when some

• compare_models

  Put two or more frontier models side by side on the benchmark results recorded for them (score, unit, source table and evidence grade per benchmark), optionally with their rows in one profession's ranking. Use this when the user is choosing between specific models; use `recommend

• list_benchmarks

  List every benchmark in the AI Agent Benchmarks catalogue (agentic benchmarks with SOTA, saturation and headroom) and in the radar's complementary list (vision, long context, factuality, instruction following, language). Use this to find a benchmark slug; then call `get_benchmark

• get_benchmark

  Get one benchmark by slug or name: what it measures, state of the art, saturation, headroom, human baseline, source and notes, the radar axis it informs, the model scores recorded on it, and which profession rankings use it. Use this after `list_benchmarks` or `search_all`, or to

• list_labs

  List every SANTISMM Lab with its inputs, outputs, assumptions, formulas and citation URL. Use this to discover interactive and machine-readable tools; filter by kind when the user specifically asks for a calculator, converter, experiment or educational game.

• get_lab

  Get one Lab by slug, including formulas, assumptions, related SANTISMM content and its executable endpoint when one exists. Use this after list_labs or search_all; use the named calculate_* tool rather than reimplementing a published formula.

• calculate_agent_economics

  Calculate monthly operating cost, cost per verified outcome, manual baseline, savings, ROI and break-even success rate from explicit assumptions. Use this for an agent business case or scenario comparison; keep every monetary input in the same currency and cite the returned canon

• calculate_evaluation_sample_size

  Calculate two different samples: how many independent evaluations are needed to detect at least one failure, and how many are needed to estimate its rate at a chosen margin. Use this when a user asks how many tests are enough; do not interpret zero observed failures as proof of z

• calculate_human_supervision_capacity

  Calculate review and escalation workload, required FTE, available headroom or backlog, monthly labour cost and sustainable case volume. Use this before production rollout to test whether the stated human-oversight model is operationally credible; the result uses averages and is n

• search_physical_ai

  Search the Physical AI dataset — real robot platforms (humanoids, quadrupeds, mobile manipulators, UGVs, drones), documented deployments with sources, the enterprise deployment guide, the autonomy taxonomy and the evidence methodology. Filter by type and taxonomy ids. Each result

• get_physical_ai_item

  Read one record by type and slug: a platform sheet (specs with test conditions, provenance and sources, autonomy by task, evidence stage, access, lifecycle), a documented case study (sourced facts, published KPIs, lessons), a guide, a cited source, or the taxonomy and methodology

• compare_physical_ai_platforms

  Compare 2–5 platforms side by side on published fields only — payload, runtime, weight, speed, IP rating, temperature range, price, autonomy, coordination, control, perception, environments, sectors, evidence stage, access and lifecycle. Every cell says whether the value is publi

• calculate_physical_ai_roi

  Run the Physical AI calculator's own 36-month model: manual vs robotic TCO, annual savings, upfront investment, payback month, ROI, coverage, supervision and incidents, plus payback sensitivity to hourly cost, capex, coverage, integration, maintenance and uptime. Every input is o

• assess_physical_ai_fit

  Run the deployment guide's readiness assessment: score per area and overall, the band and its guidance, the weakest area, blockers (items answered 'no') and the questions still unanswered, plus the platforms and documented deployments that match the environment, sector and family

• get_related

  Traverse the knowledge graph: given a unit, return its neighbours — the units it links to (outgoing) and the units that reference it (incoming), each with the relationship type. Use this after a `get_*` call to widen an answer with adjacent units.

• list_homeric_places

  List every place in the Homeric Atlas with its identification class, its confidence score and how many competing identifications it carries. Use this to browse the atlas; use `get_homeric_place` once you have a slug. Identification classes: accepted (an excavated site with consen

• get_homeric_place

  Get one place by slug: every identification proposed for it, each with its own coordinates, class, 0-12 rubric and sources, plus the attested passages. Use this when you need to weigh the evidence for a location, or to cite it; a place the poem does not locate carries no coordina

• list_homeric_episodes

  List every episode of the Iliad and the Odyssey held in the atlas, in reading order, with its passage, the places it involves and how firmly it can be located. Use this to find the episode you want; use `get_homeric_episode` for its theories and sources.

• get_homeric_episode

  Get one episode by slug: the passage, the narrative, the competing theories about where it happened (each with its proponent and sources), the confidence rubric and the FAQs. Use this when the question is where an episode took place and who argued for it.

• list_homeric_routes

  List the reconstructed itineraries (the nostos of Odysseus and the others), each with its rival reconstructions scored separately. Use this to see which voyages the atlas reconstructs before fetching one.

• get_homeric_route

  Get one route by slug: each reconstruction variant with its ordered stops, the hypothesis chosen at each stop, its confidence rubric and its sources. Use this to compare rival reconstructions of a voyage: they are returned side by side rather than merged.

• list_claims

  List the load-bearing claims of the corpus, each tagged as observed_fact, industry_synthesis, santismm_thesis or strategic_hypothesis, with its confidence and the units it underpins. Use this before quoting the handbook to know whether a statement is evidence, a reading of the in

• get_claim

  Get one claim by id (HE-CLAIM-001) or slug: the statement, what it rests on, its structured sources, and — always present — what it does NOT establish and the observation that would retire it. Use this to cite the corpus honestly, or to check whether a result you have just measur

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

• ✓ Pass

  Maker matches the repo

  The registry name was verified against the domain santismm.com (the registry checks domain ownership)

• … Not checked yet

  Still maintained

  We have not read this repo from GitHub yet

• … Not checked yet

  Has a licence

  We have not read this repo from GitHub yet

• ✓ Pass

  Endpoint responds

  Responded in 410 ms (HTTP 200)

• – Not applicable

  Sign-in with OAuth

  No sign-in needed

• ✓ Pass

  No hidden instructions in tool descriptions

  Read 41 tool descriptions and found no hidden instructions to the AI

Compared with others in Search & AI memoryi

• More stars than 0% of the other 5666 servers in this category
• Published to the registry more recently than 99% of the other 5666 servers in this category
• Basic checks equal to or better than 100% of the other 5666 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

README from the maker

{ "mcpServers": { "santismm-knowledge": { "type": "http", "url": "https://santismm.com/mcp" } } }

npm ci && npm run build && npm start

npx santismm-knowledge-mcp

{ "mcpServers": { "santismm-knowledge": { "command": "npx", "args": ["santismm-knowledge-mcp"] } } }

uvx santismm-knowledge-mcp

{ "mcpServers": { "santismm-knowledge": { "command": "uvx", "args": ["santismm-knowledge-mcp"] } } }