ToolPlan MCP
The ToolPlan MCP server provides a single tool, plan_project, that transforms a raw project idea into a structured, cost-aware, markdown-formatted prompt for AI coding agents.
What it does:
Takes a raw project idea and quality grade to produce an enriched prompt containing a project brief, recommended tech stack, high-leverage open-source tools/MCPs/skills that AI models typically miss, cost-saving execution directives (e.g., subagent delegation, plan-first execution), and source references.
Leverages a bundled, curated knowledge base (
kb/*.yaml) covering stacks, tools, MCPs, skills, and directives — no live network calls at runtime.Designed to make projects more "one-shotable" by producing a superior first prompt, even on non-frontier models.
Input Parameters:
idea(required, string, min 10 chars) — Your raw project descriptiongrade(required:"industry"or"personal") — The quality/professionalism targettags(optional, array of strings) — Additional tags to guide knowledge base matching
Key Characteristics:
Runs entirely locally; no external data is sent (privacy-preserving)
Single-shot execution — no long-running or interactive tasks
Optional local logging for usage analysis
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ToolPlan MCPplan a personal habit tracker app"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ToolPlan MCP
Turn a raw project idea into a polished, cost-aware first prompt for any AI coding agent.
Why
Models pick tech stacks decently — but they:
Under-recommend high-leverage open-source tools. Niche skills, scrapers, and community tools from GitHub/Reddit (Agent-Reach, caveman, karpathy-guidelines, ...) save hours, and models rarely surface them unprompted. Programmers who don't track this ecosystem lose that time.
Never apply cost-saving techniques on their own. Subagent delegation, plan-first execution, task-by-task verification — models don't do these unless told, and casual users don't know to ask. Result: millions of wasted tokens.
Do better with a structured first prompt. A polished brief with stack, constraints, and done-criteria makes a project far more one-shotable — even on non-frontier models.
ToolPlan packages all three into one MCP tool call.
Related MCP server: PromptArchitect MCP
How it works
your raw idea ──▶ plan_project(idea, grade) ──▶ enriched prompt
│
reads curated KB (kb/*.yaml):
stacks · tools · MCPs · skills · directives
each with why_models_miss_it + cost_profileNo live scraping at runtime — a weekly offline pipeline proposes KB updates as human-reviewed diffs, so advice stays current without hype pollution.
Quick start (Claude Code)
claude mcp add toolplan -- npx -y toolplan-mcpThen either:
/toolplan <your idea>— copycommands/toolplan.md(shipped in the npm package) to~/.claude/commands/first. The agent calls the tool, shows you the refined prompt verbatim, and waits for you to proceed, edit, or regenerate — it never starts building on its own./mcp__toolplan__plan— zero-install; Claude Code auto-exposes the server's built-inplanprompt as a slash command.Or just ask in chat: "Use plan_project with my idea: an app that tracks freelance invoices, grade personal."
Other hosts (Cursor, Codex CLI, any stdio MCP host): see docs/HOST_SETUP.md.
Tool API
plan_project(idea: string, grade: "industry" | "personal", tags?: string[])
→ markdown enriched prompt: project brief, recommended stack, tools you'd
likely miss, execution directives, quality bar, sources.
Knowledge base
One YAML file per entry under kb/<category>/. Format: docs/KB_SCHEMA.md.
Contributions welcome — PRs must pass the eval regression suite.
Privacy note: running the tool never phones home. The KB is read-only at
runtime and bundled with the package; nobody's usage updates it. Optional
TOOLPLAN_LOG writes usage lines to a local file you control.
Improving the KB
Three ways, smallest first:
Add one entry by hand. Copy an existing YAML in
kb/<category>/, fill the fields honestly (especiallywhy_models_miss_it), runnpm test && npm run eval, open a PR.Mine your own usage. Set
TOOLPLAN_LOG=toolplan.jsonlin the server env, use the tool for a while, thennpm run log-to-case toolplan.jsonl— real ideas become eval-case skeletons; weak matches show you exactly which keywords the KB is missing.Run the weekly refresh. Point a Claude agent at pipeline/REFRESH.md; it researches new tools and writes proposals to
pipeline/proposals/<date>/with evidence. You reviewPROPOSAL.md, move accepted files intokb/, runnpm test && npm run eval, commit.
Staleness check anytime: npm run stale.
Development
npm install
npm run build
npm test
npm run smoke # end-to-end stdio call against the built serverStatus
v1: web-application scope only. See PLAN.md for roadmap (eval harness, refresh pipeline, host adapters).
Available Tools
1 toolplan_projectPlan ProjectB
Turns a raw project idea into an enriched, polished prompt using a curated knowledge base of stacks, tools, MCPs, skills, and execution directives.
| Name | Required | Description | Default |
|---|---|---|---|
| idea | Yes | ||
| tags | No | ||
| grade | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It indicates a transformation (reads input, produces prompt), which suggests a non-destructive read-like operation. However, it doesn't explicitly state idempotency, permissions, or side effects. The description is adequate but not detailed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 20 words, front-loaded with the key action ('Turns a raw project idea'). It is concise and contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description hints at the output ('enriched, polished prompt') but does not cover error conditions, prerequisites (e.g., idea length requirements), or behavior of optional parameters. Given the low complexity (3 params, no output schema), it is minimally complete but could be improved.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no explanation of the parameters 'idea', 'tags', or 'grade'. The parameter names are somewhat self-explanatory, but the description fails to clarify their roles (e.g., what 'grade' means, how 'tags' are used). This significantly reduces the tool's usability for an AI agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'turns' and the resource 'raw project idea' into 'enriched, polished prompt'. It specifies the use of a curated knowledge base. It is specific enough to distinguish from generic tools, though it could be more precise about the output structure.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidelines are provided about when to use this tool versus alternatives. Since no sibling tools are listed, the description misses an opportunity to specify prerequisites or context (e.g., 'Use this for initial project planning before executing tasks').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
plan_project
TDQS
With only one tool, there is no possibility of confusion between tools.
Single tool named with clear verb_noun pattern, consistent within the set.
A single tool feels thin for a server that references a knowledge base, though it may be sufficient for a focused planning utility.
The tool covers the core planning action but lacks additional operations for managing or querying the underlying knowledge base.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
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