Skip to main content
Glama

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:

  1. 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.

  2. 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.

  3. 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_profile

No 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-mcp

Then either:

  • /toolplan <your idea> — copy commands/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-in plan prompt 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:

  1. Add one entry by hand. Copy an existing YAML in kb/<category>/, fill the fields honestly (especially why_models_miss_it), run npm test && npm run eval, open a PR.

  2. Mine your own usage. Set TOOLPLAN_LOG=toolplan.jsonl in the server env, use the tool for a while, then npm run log-to-case toolplan.jsonl — real ideas become eval-case skeletons; weak matches show you exactly which keywords the KB is missing.

  3. 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 review PROPOSAL.md, move accepted files into kb/, run npm 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 server

Status

v1: web-application scope only. See PLAN.md for roadmap (eval harness, refresh pipeline, host adapters).

Available Tools

1 tool
plan_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.

ParametersJSON Schema
NameRequiredDescriptionDefault
ideaYes
tagsNo
gradeYes

TDQS

B3.1/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness3/5

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.

Parameters2/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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. 1 tool updatev0.1.0
    • First observedplan_project

TDQS

B3.4/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion between tools.

Naming Consistency5/5

Single tool named with clear verb_noun pattern, consistent within the set.

Tool Count3/5

A single tool feels thin for a server that references a knowledge base, though it may be sufficient for a focused planning utility.

Completeness3/5

The tool covers the core planning action but lacks additional operations for managing or querying the underlying knowledge base.

Maintenance

ActivitySlowing
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • A
    license
    A
    quality
    D
    maintenance
    Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.
    5
    22
    62
    TypeScript
    MIT
  • F
    license
    A
    quality
    C
    maintenance
    Refines and improves AI prompts using workspace-aware context from your project's tech stack, structure, and dependencies. Includes tools to analyze prompt quality and generate well-structured prompts from raw ideas.
    4
    209
    5
    -
  • A
    license
    A
    quality
    D
    maintenance
    Classifies development task complexity (LIGHT/MEDIUM/HEAVY) and recommends the most cost-efficient AI model per provider, enabling optimized model selection for coding tasks.
    3
    41
    MIT

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/aaditya0602/toolplan-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server