Every MCP server injects its full tool schemas into context on every turn — 30 tools costs ~3,600 tokens/turn whether the model uses them or not. Over 25 turns with 120 tools, that's 362,000 tokens just for schemas.mcp2cli turns any MCP server or OpenAPI spec into a CLI at runtime. The LLM discovers tools on demand: mcp2cli --mcp https://mcp.example.com/sse --list # ~16 tokens/tool mcp2cli --mcp https://mcp.example.com/sse create-task --help # ~120 tokens, once mcp2cli --mcp https://mcp.example.com/sse create-task --title "Fix bug" No codegen, no rebuild when the server changes. Works with any LLM — it's just a CLI the model shells out to. Also handles OpenAPI specs (JSON/YAML, local or remote) with the same interface.Token savings are real, measured with cl100k_base: 96% for 30 tools over 15 turns, 99% for 120 tools over 25 turns.It also ships as an installable skill for AI coding agents (Claude Code, Cursor, Codex): `npx skills add knowsuchagency/mcp2cli --skill mcp2cli`Inspired by Kagan Yilmaz's CLI vs MCP analysis and CLIHub.https://github.com/knowsuchagency/mcp2cli
FL score
out of 100
Verdict
high confidence
Competition
10
competitors found, emerging market, funded players
Trend
No signal yet
A CLI tool that significantly reduces LLM token consumption by dynamically loading API tool schemas on demand, specifically for agentic workflows.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Mcp2cli – One CLI for every API, 96-99% fewer tokens than native MCP”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
This idea targets a specific, measurable pain point in LLM agent development (token waste from tool schemas) with a novel CLI-based approach, showing good market potential despite general competition in token optimization. Buildability for a solo builder to create a robust, universally compatible solution is a moderate challenge.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
High market demand and a clear value proposition with significant token savings, but faces challenges in execution complexity and creating a strong competitive moat against broader solutions.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A highly specific and leveraged solution for a clear problem in the LLM agent space, but faces challenges in monetization as an open-source project and complexity for solo development.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong value proposition for a specific audience, but needs a clear monetization strategy beyond open-source and careful management of adoption friction.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a clear and growing pain for LLM agent developers with a specific, elegant solution that is likely to become more essential over time.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
A Python package that uses a smaller language model to compress prompts by removing redundant tokens while preserving semantic meaning.
Pricing: Open-source (free)
Provides infrastructure for semantic caching, vector search, and session management to optimize LLM token usage by retrieving cached answers for semantically similar queries.
Pricing: Offers a free tier, then scales based on usage and features (e.g., Redis Cloud pricing). Actual numbers vary greatly based on deployment.
Provides a practical framework to make token usage traceable and governable across workloads, teams, and providers.
Pricing: Not explicitly stated, but offers open-sourced LLM pricing database.
Helps implement dynamic pricing strategies for AI-powered features based on the cost of underlying LLM tokens used, with a focus on metered billing.
Pricing: Not explicitly stated on their pricing page, but focuses on metered billing for AI features.
Provides access to various LLM APIs (Claude Sonnet, GPT Codex, Doubao Seedream) with competitive pricing, aiming for cost-effectiveness for startups and enterprises.
Pricing: Not explicitly stated with actual numbers on their general information pages, but highlights 'affordable pricing'.
Offers a platform to test, compare, and orchestrate models faster, aiding in cost management through strategies like multi-model experimentation and hybrid inference.
Pricing: Does not directly track or manage billing, but helps reduce costs indirectly. No explicit pricing on general information pages.
An AI proxy that sits between users and LLM providers, enabling centralized control, observability, and governance, including token-based rate limiting and usage quotas.
Pricing: Offers various editions, including a free open-source version of Kong Gateway. Enterprise pricing is custom.
An inference platform designed to help AI-native companies optimize inference performance and economics.
Pricing: Not publicly available; offered as part of an 'Enterprise Readiness Initiative' with custom engagements.
A command-line tool that reads an OpenAPI spec, flattens parameter schemas, generates MCP tools, and handles unflattening for API calls, specifically to address LLM's poor handling of nested JSON.
Pricing: Likely open-source/free as a command-line tool, given the context.
A framework for developing applications powered by language models, enabling orchestration, evaluation, and multi-step agent deployment, which can incorporate various token optimization strategies.
Pricing: Open-source (free) for the core framework; offers LangChain Plus for managed services and observability (pricing not publicly disclosed).
What they charge
Recent news
Stop wasting money on AI: 10 ways to cut token usage
LogRocket Blog, March 17 2026
Nebius gives VC-backed growth-stage companies a fast track to enterprise adoption in collaboration with NVIDIA
Nebius, March 17 2026
LLM API Cost Comparison 2026: 300+ Models Analyzed
Unnamed Source (analysis mentioned in a search result), March 16 2026
The best llms in 2026
Product Hunt, March 16 2026
Top OpenAI API Alternatives for Fast and Flexible AI Integration
Built This Week, March 16 2026
Market signals
The market for optimizing LLM API token usage is significant and rapidly growing. As AI adoption scales, especially in enterprise settings with complex agentic workflows, the high cost and latency associated with excessive token consumption make optimization a critical business requirement. Recent funding rounds and numerous articles highlight the increasing focus on token efficiency and cost management in AI applications.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI