Tokenomics Overview MCP server

Read from the package source without running it. The installed server may list more.

• calculate_cache_metrics

  Applies the cache explainer's published formulas to YOUR token counts. Cache hit rate = cache read / (cache read + cache write + uncached input). Cache cost efficiency = 1 − actual prompt cost / uncached equivalent cost, where uncached equivalent cost = (cache read + cache write + uncached input) × base input price. Give token counts; add `base_input_price_per_mtok` plus either `actual_prompt_cost

• define_term

  Glossary lookup across the Foundation's definition page and the cache explainer (e.g. 'KV cache', 'prompt cache', 'tokenmaxing', 'Jevons paradox'). Returns every matching definition with its source.

• get_bigt_class

  One rung of the Big-T ladder: what puts a workload in it, the published fix, the stack layers whose levers move it, and the levers tagged with it. `class` accepts 'T(n·k)', 'n*k', 'nk', 'multiplicative', or a slug like 't-n-k'.

• get_bigt_notation

  Big-T notation, 'Big-O for AI consumption': T(n · k · a) = requests × model calls per request × agent depth, and the six-class complexity ladder from T(1) to T(∞), each with its published fix. Use it to classify how a workload's token cost grows before optimizing constants.

• get_crosslinks

  Stated links from tokenomics entities to the other two servers: each carries a quote from the Foundation page that names the target, and a finops://framework/… or focus://spec/… URI to read with those servers (null for FOCUS working-draft identifiers). Filter by `entity_type` + `slug` (e.g. metric cache-hit-rate) or by target `server`.

• get_crosswalk

  EXPERIMENTAL, UNOFFICIAL: this server's curated mappings from tokenomics entities to FinOps Framework capabilities, KPIs and personas (e.g. cache-hit-rate → framework KPI cache-hit-rate, personas by name). No Foundation page states these; each carries a rationale. Prefer get_crosslinks for stated links.

• get_curriculum_module

  EXPERIMENTAL, UNOFFICIAL: one training module's objectives, agenda, anchors to sources, and (with include_slides) each slide's title, body, source line, and facilitator notes. Module-original formulas are the module's own method, not Foundation guidance.

• get_document

  Canonical markdown of one Tokenomics Foundation document, prefixed with its publication status and followed by CC BY attribution. Pass `section` (an id from list_documents) to get one section instead of the whole document — prefer this for long papers and podcast transcripts.

• get_focus_ai_tracker

  The Foundation's page on what FOCUS 1.5 does for AI cost, one card per capability: in the working draft (e.g. ModelId and other model properties on SKU Price Details, TokenCacheAction / TokenDirection, PrincipalId / CredentialId), in final Member review (SKU Price dataset, AI billing worked examples), and not in 1.5. Filter by `bucket` (done|review|out) or an `identifier` such as 'TokenCacheAction

• get_layer

  One layer of the Seven-Layer Stack: description, components, published tooling table, and the levers that sit at it. `layer` accepts 'l5', '5', 'L5', the layer name ('ai access and control'), or its first word ('energy', 'demand'). L5 is the proxy / router / gateway layer.

• get_lever

  Full record for one optimization lever, with provenance and status. Look up by slug or name from list_levers.

• get_metric

  One metric's published formula, definition, inputs, 'what good looks like' targets, and its cross-links to the FinOps Framework / FOCUS servers. E.g. 'cache-hit-rate', 'cache-cost-efficiency'.

• get_numbers_register

  EXPERIMENTAL, UNOFFICIAL: the curriculum's register of every figure a module may use, with its wording and its only allowed citation (e.g. 8–27% token share of agent run cost, ~34× Big-T worked example). Optional `query` filter.

• get_persona

  One persona's responsibilities, key decisions, signals and metrics, and stated links to FinOps Framework capabilities.

• get_provider_cache_snapshot

  The cache explainer's dated snapshot of frontier providers' prompt-cache pricing and behavior (write/read charges as multiples of base input, minimum cacheable prefix, lifetime). A snapshot, not a spec — verify against current provider docs. Optional `provider` filter (anthropic, openai, google).

• get_tokenomics_info

  Start here. Data version, entity counts, and every source document with its publication status (Working Draft, Release Candidate, …). No parameters.

• list_curriculum_modules

  EXPERIMENTAL, UNOFFICIAL: the maintainer's local AI Tokenomics training modules (TK-2xx/3xx) from the curriculum overlay — ids, titles, levels, personas, objectives. Not Tokenomics Foundation content.

• list_documents

  Every ingested tokeneconomics.com document with kind (docs/project/paper/insight), publication status, and section ids for get_document. Optional `kind` filter.

• list_layers

  The seven layers (L0 Energy … L5 AI access and control, the routing/gateway layer … L6 Demand and entry points) with the published summary: whether each moves the Big-T multiplier, its key metric, and its primary levers. The lower three set what a token costs; the middle three move a workload between Big-T classes; the top layer sets demand.

• list_levers

  Optimization levers as the Foundation publishes them: the stack's primary levers per layer (caching, batching, right-sizing, quantization, complexity routing, budgets, agent caps…), Big-T's five levers (routing, serialization, caching, abstraction transparency, governance), architectural approaches with published token reductions, and the 34× worked-example steps. Filter by `layer` (0-6), `bigt_cl

• list_metrics

  The Foundation's reference metrics with their published formulas: Cache Hit Rate, Cache Cost Efficiency and uncached equivalent cost (cache explainer), net benefit and risk expected loss (value classification), cost per token (definition), and AI unit economics (TCA over realized value, a proposal).

• list_personas

  The eight core personas (Engineering, FinOps Practitioner, AI End User, Finance, Product, Procurement, Leadership, Capacity) and four allied ones, with the stages (Production, Consumption, Value) each spans. Filter by `stage` or `core`.

• list_value_categories

  The ten outcome categories (direct dollar wins, labor, intangible) with 'estimate as' units, and the five destinations Finance books realized value to. From 'Classifying and Measuring Realized Value from AI'.

• search_tokenomics

  Ranked keyword search across layers, Big-T classes, levers, metrics, personas, value categories, glossary terms, FOCUS tracker items, and every document section. Use when you don't know the slug; follow up with the matching get_* tool or get_document(section).

• Code scan66 source files scanned25/25
• –Live reliabilityno gateway calls yet and no remote to proben/a
• –Tool poisoningtools not inspected (local package is not executed); not countedn/a
• Auth qualitylocal package, no credentials required12/15
• Maintenancelast push 6 days ago15/15
• Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10

Safety score

92/100

higher than 95% of 48,255 servers

Tools

24

the typical server has 7

Tools that change things

0%

typical: 0%. Fewer write tools means less an agent can break

Tokenomics Overview: common questions

Is Tokenomics Overview MCP server safe?

Yes, by our scan: it is graded A (92/100). Read the Tokenomics Overview safety report

How do I install Tokenomics Overview?

It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.

Does Tokenomics Overview need an API key?

No secret keys are declared. It reads 1 setting from the environment.

Is Tokenomics Overview maintained?

The last commit was 15 days ago (2026-10-20). The latest release is v0.2.1.

How does Tokenomics Overview compare with other MCP servers?

safety score 92/100 (higher than 95% of 48,255 servers); tools 24 (the typical server has 7); tools that change things 0% (typical: 0%. Fewer write tools means less an agent can break). Full comparison.