Santismm Knowledge — Harness Engineering, Agentic AI & Governance

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

• Conexión: Se ejecuta en la nube
• Transporte: Streamable HTTP
• Categoría: Búsqueda y memoria de IA

Instalación

Elige la app de IA que usas y copia la configuración.

Necesitas

• Se ejecuta en la nubeNo hay nada que instalar; se conecta por internet

¿Qué app usas?

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

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Nombres y descripciones de las herramientas tal como los informó el servidor el 7 nov 2026. Los escribió el creador, no nosotros; los mostramos como texto sin formato.

• 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

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026

• ✓ Superada

  El creador coincide con el repo

  El nombre en el registro se verificó con el dominio santismm.com (el registro comprueba quién es dueño del dominio)

• … Aún sin revisar

  Sigue en mantenimiento

  Todavía no hemos leído este repo en GitHub

• … Aún sin revisar

  Tiene licencia

  Todavía no hemos leído este repo en GitHub

• ✓ Superada

  El endpoint responde

  Respondió en 410 ms (HTTP 200)

• – No aplica

  Inicio de sesión con OAuth

  No hace falta iniciar sesión

• ✓ Superada

  Sin instrucciones ocultas en las descripciones de herramientas

  Leímos 41 descripciones de herramientas y no encontramos instrucciones ocultas para la IA

Comparado con otros en Búsqueda y memoria de IAi

• Más estrellas que el 0% de los otros 5653 servidores de esta categoría
• Publicado en el registro más recientemente que el 100% de los otros 5653 servidores de esta categoría
• Revisiones básicas iguales o mejores que el 100% de los otros 5653 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

README del creador

{ "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"] } } }