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LLMSlim
Tool-aware context, contract-safe.
v0.4.0 adds contract-safe tooling for MCP, OpenAI function, Anthropic, and generic tool schemas: provider-aware adapters, deterministic canonical JSON, SHA-256 fingerprints, exact-equivalence checks, and schema inspection. Raw execution schemas remain authoritative. Tool retrieval is research-only and is never used for authorization or execution.
Top comment
Tool definitions are context—and unlike ordinary prompt text, they are execution contracts. LLMSlim v0.4.0 makes tool-schema handling easier to inspect and reason about: preserve raw definitions, create deterministic canonical views, fingerprint complete contracts, and verify exact equivalence before measuring catalog representations. We also published the uneventful result: across 375 schemas in 18 catalogs, lossless measurement saved 0 tokens because the baseline was already compact canonical JSON. That’s useful evidence, not a hidden failure. Experimental retrieval remains research-only and is never an authorization or execution decision. I’d love feedback from builders using MCP or multi-provider tools: where do adapters break fidelity, and how are you tracking contract changes across catalogs? Install: `pip install llmslim` Release notes: https://github.com/Thanatos9404/...
About LLMSlim on Product Hunt
“Tool-aware context, contract-safe.”
LLMSlim was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #86 on the daily leaderboard. v0.4.0 adds contract-safe tooling for MCP, OpenAI function, Anthropic, and generic tool schemas: provider-aware adapters, deterministic canonical JSON, SHA-256 fingerprints, exact-equivalence checks, and schema inspection. Raw execution schemas remain authoritative. Tool retrieval is research-only and is never used for authorization or execution.
On the analytics side, LLMSlim competes within Developer Tools — topics that collectively have 518.4k followers on Product Hunt. The dashboard above tracks how LLMSlim performed against the three products that launched closest to it on the same day.
Who hunted LLMSlim?
LLMSlim was hunted by Yashvardhan Thanvi. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of LLMSlim including community comment highlights and product details, visit the product overview.