Mcp2cli – One CLI for every API, 96-99% fewer tokens than native MCP
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
- Addresses a specific, quantifiable, and severe pain point (token waste) in a rapidly growing market (LLM agents).
- Offers a highly specific, novel solution (CLI-based dynamic tool discovery) that provides significant, measured token savings (96-99%).
- The value proposition is clear and directly tackles user frustrations regarding cost and latency.
- The problem will only become more critical as LLM agents and tool usage scale.
Risks
- Building a universally robust and compatible CLI for all OpenAPI/MCP specs is technically challenging for a solo builder.
- As an open-source project, establishing a clear and sustainable monetization strategy for a solo builder is a risk.
- Competition from broader LLM optimization platforms (some funded) could make differentiation difficult in the long run.
- The friction of adopting a new tool-calling paradigm and ensuring LLM reliability with CLI output needs careful management.
Next steps
- Conduct in-depth user interviews with LLM agent developers and platform engineers to confirm specific integration challenges and willingness to pay for a hosted/premium version.
- Develop a clear pricing model for enterprise features (e.g., advanced monitoring, managed service, enhanced security) or premium support around the open-source core.
- Focus MVP on a specific, popular subset of OpenAPI/MCP configurations to reduce initial build complexity and get early validation.
- Actively engage with AI developer communities on GitHub, Reddit, and Discord to gather feedback and build a user base for validation.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 27
- Solution gap
- 21
- Willingness to pay
- 19
- Buildability
- 10
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
- Conduct in-depth user interviews with LLM agent developers and platform engineers to confirm specific integration challenges and willingness to pay for a hosted/premium version.
- Develop a clear pricing model for enterprise features (e.g., advanced monitoring, managed service, enhanced security) or premium support around the open-source core.
- Focus MVP on a specific, popular subset of OpenAPI/MCP configurations to reduce initial build complexity and get early validation.
- Actively engage with AI developer communities on GitHub, Reddit, and Discord to gather feedback and build a user base for validation.
Who is already there, emerging market
- LLMLinguaVisit
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.
- KindeVisit
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.
- CometAPIVisit
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'.
- Prem AIVisit
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.
- mcp-openapi (command-line tool)Visit
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
- High token usage and escalating API costs, especially with complex prompts and numerous tool definitions.
- Increased latency and degraded performance due to processing massive context windows and too many tools.
- Models struggling to select the correct tool from a large catalog, leading to 'tool confusion.'
- The need to resend entire conversation history with every request, leading to redundant tokens.
- LLMs being 'terrible at constructing nested JSON' for API calls, leading to malformed payloads and increased retries.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
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
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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”.