Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Descripción del creador (original en inglés)
Trazum as an MCP server: let an agent price and budget its own prompts before it sends them.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json
{
"mcpServers": {
"trazum": {
"command": "npx",
"args": [
"-y",
"@trazum/mcp"
]
}
}
}Ejecuta esto en tu terminal
claude mcp add trazum -- npx -y @trazum/mcpPégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json
{
"mcpServers": {
"trazum": {
"command": "npx",
"args": [
"-y",
"@trazum/mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"servers": {
"trazum": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@trazum/mcp"
]
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
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.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 7 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub Davmunrey, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (2.7.7) llegó al registro ayer.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.
En la misma categoría: Spec Driven Development, Remote Desktop Commander, FlyDocs
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Spec-driven development workflow: your agent works issues, sessions and the status lifecycle.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
Find where your AI usage costs money, choose a change, and check the result. Trazum analyses local usage logs and prompts, ranks opportunities such as caching and model changes, and keeps estimates separate from measured outcomes.
Open source · Local analysis · No provider key needed to get started
Requires Node.js 20 or newer. npx may download Trazum from npm.
npx @trazum/cli bill
*Illustrative data from examples/claude-projects, not a customer result. Generated from the CLI output.*
With no path, bill reads the local sessions of Claude Code, Codex and Gemini CLI, names each folder it reads, and splits the result by agent, project and model, with each session's subagents and workflows under the project they ran for; --by day|week|month adds the period you ask for. On a subscription, this is API-equivalent cost, not what you paid. Unpriced models are reported as gaps. Local analysis does not upload your files.
Have a supported usage export instead? Pass its path:
npx @trazum/cli bill ./usage-export.jsonl
Supported inputs · Try sample data in your browser
For teams paying for API usage, the useful question is what to change next. Start with your largest workload, review the assumptions behind one finding, and evaluate quality before changing models or prompts.
bill shows the recorded cost and coverage gaps.plan ranks opportunities from a normalized usage log.verify compares the saved plan with later usage, including cases where the evidence cannot settle the result.Follow the first-report-to-verification walkthrough. A projected saving is not a guaranteed reduction in your invoice.
A smaller API-equivalent estimate does not reduce a fixed subscription fee. Use Trazum to inspect conversation growth, review redundant prompt text and set token budgets for instructions you control. Check task quality before keeping a shorter prompt. This does not automatically manage your agent's context or guarantee more messages under your provider's limits.
Subscription efficiency workflow.
In the [Tasks dashboard](https://trazum.vercel.app/?tab=tasks) and CLI/MCP 2.6.0: task efficiency connects Claude Code/Codex task usage, repeated-tool evidence, a reversible instruction policy and an explicit acceptance decision. Compare observed usage before and after a change without inferring success or guaranteed savings.
npx @trazum/cli bill ./usage-export.jsonl --html-out bill.html
PDF, CSV and Markdown exports are also available. Review the report before sharing it: it contains usage and cost information.
Claude Code is the first native integration, not a provider lock-in. Codex session imports, normalized usage logs and provider exports feed the same core. MCP clients can query Trazum without having a native transcript adapter.
| You need | Start here |
|---|---|
| A terminal report | Quick start |
| Native agent session import | Claude Code or Codex |
| A task-level efficiency experiment | Analyze, apply a policy and compare, since 2.6.0 |
| An agent integration | CLI, MCP and skill workflows |
| An API contract or enforced call path | Local HTTP API or gateway |
| The full documentation | Task-based index |
Harness, model and payment mode are separate facts. An API-equivalent estimate is not a subscription invoice; a model without a price remains a gap.
Explore advanced workflows, commands, architecture and limitations
One agent costs what it costs. A fleet of them spends in a loop nobody is watching per iteration, and the bill arrives a month later as one number with no per-decision detail inside it.
Trazum installs into that loop. The MCP server's first tool is spend_guard, and it is the only one here whose trigger is not a sentence somebody types:
"May I spend this?" -> yes, no, or cannot-tell.
A refusal carries the cheaper ways to make the same call, each priced for that call and each naming what it assumes. The ceilings come from your trazum.config.json; the spend so far comes from the usage log your host already writes. Nothing is called and nothing is spent to answer.
claude plugin marketplace add Davmunrey/Trazum
claude plugin install trazum@trazum
That one line brings the skill and the MCP server. An agent that reads Agent Skills from the repository instead gets the same skill with npx skills add Davmunrey/Trazum. Every other MCP client reads the same mcpServers JSON, so Cursor (.cursor/mcp.json), Windsurf, Claude Desktop and the rest are one paste:
{ "mcpServers": { "trazum": { "command": "npx", "args": ["-y", "@trazum/mcp"] } } }
The server is listed on the official MCP registry as io.github.Davmunrey/trazum, and every release updates the listing.
[Or run it right now, without installing anything: the Playground](https://trazum.vercel.app/?tab=playground)
That link opens the CLI's pure subset running in the page, against sample files already loaded, through the same @trazum/core functions the terminal runs. Nothing you paste leaves your browser.
*Real output, transcribed. Read the last two lines: the rules recovered $24.00 a month, and the advisory above them is worth $528.40, 22 times more. That gap is the entire argument for this tool.*
*Generated by npm run draw:architecture, not drawn. architecture-image.test.js fails the build if a package exists that the picture does not show, if the core takes a dependency while the picture says it has none, or if a third module is allowed to reach a network without the picture saying so.*
The prompt is the part everyone looks at, and usually the cheap part. In the run above, forty percent of the text came out and it moved 3.5% of the bill. What moved the rest was a question nobody was asking: does this task need the model it is running on?
Every figure has a receipt. Sixteen advisories, each priced per month and reproducible on a single file: caching you are not getting, work that could go through the Batch API, a schema costing tokens on every call to describe a shape the request could carry as a parameter. Underneath them, twelve deterministic rules that shorten the text itself: same input, same output, free, offline, and never touching code, URLs, email addresses or placeholders. On top, an optional LLM pass for the compression rules cannot do, through whichever provider you configure, which never runs unless you ask.
┌──────────────┐
│ @trazum/core │ the library: rules, tokens, pricing └──────┬───────┘ zero dependencies, browser-safe ┌────────────┬─────────────┼─────────────┬─────────────┐ @trazum/cli @trazum/mcp trazum-vscode @trazum/web action/ 55 commands MCP server the editor Next.js comments on for your agents status bar pull requests
@trazum/tokenizer-openai optional: exact counts for OpenAI install it or do not, nothing else changes
| I want to… | Go to |
|---|---|
| Get a first result | Quick start |
| Understand recorded spend | Usage inputs and bill/profile |
| Set budgets | CI gates or gateway decisions |
| Integrate Trazum | CLI, web, library and agents |
| Find a command or a data contract | Command reference and documentation index |
Limitations · Privacy · Pricing · Contributing
The analyser reports sixteen findings, with assumptions attached to each estimate.
1. Tells you where the money actually is. This is the part worth reading first, because it is where the numbers are. Every advisory is priced per month against your own call volume, and none of them is about making the text shorter:
| Advisory | Why it matters |
|---|---|
| Prompt caching | Reading from cache costs 10% of input. The saving is computed over the real stable prefix: in a template with {{placeholders}}, only what precedes the first one is cached — not the whole prompt. |
| Reorder the template | Stable instructions sitting *after* the first variable placeholder never cache today. Trazum prices moving them in front — and with --reorder, does it. |
| Batch API | 50% off input and output when the work tolerates latency. |
| Cheaper model | Complexity heuristic: if the task looks simple, what dropping a tier would save. |
| Output-dominated cost | If you pay more for the answer than for the prompt, shortening the prompt has a ceiling. |
| Promotional pricing | Warns when you are budgeting with an introductory price that expires. |
| Context window | If the prompt does not fit, the call is going to fail. |
| Contradictory instructions | "Answer in English" three paragraphs above "reply in the customer's own language". The model has to pick one, and which one can change between calls — a correctness problem that also costs tokens twice. |
| Redundant examples | Few-shot examples that are near-copies of an earlier one, and what they cost per month. |
| Output format stated twice | A schema shown in a code block and then walked again in prose. The block is the version worth keeping.
Solo se muestra el principio; lee el resto en el repo