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Replicate-MCP-Server

by sena-labs
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TypeScript
Hybrid

Discover New Popular Replicate Models

replicate_refresh_models
Read-only

Search Replicate for popular models not yet in the curated registry and return suggestions only, without modifying code.

Instructions

Search Replicate for popular models NOT yet in the curated registry. Returns suggestions only — does not modify code.

Use this to find new models worth adding. Then ask Claude to edit src/models.ts with the ones you want.

Args:

  • categories (string[], optional): Which categories to check. Default: all 15 (image, video, audio, tts, llm, vision, upscale, bg, stt, inpaint, segment, embed, voiceclone, threed, lipsync).

  • min_run_count (integer, optional): Minimum run_count threshold. Default: 1000.

  • limit_per_category (integer, optional): Max suggestions per category (1-20). Default: 5.

Returns structuredContent: { "checked_at": string, "categories_checked": string[], "suggestions": [{ category, owner, name, model_id, run_count, description, replicate_url }], "already_curated": number, "total_suggestions": number }

Examples:

  • "Check for new popular models" → all categories, min 1000 runs

  • categories=["image","video"], min_run_count=10000 → only top-tier image/video models

Input Schema

NameRequiredDescriptionDefault
categoriesNoCategories to check. Default: all 15 (image, video, audio, tts, llm, vision, upscale, bg, stt, inpaint, segment, embed, voiceclone, threed, lipsync).
min_run_countNoMinimum run_count to surface a model. Default: 1000.
limit_per_categoryNoMax suggestions per category (1–20). Default: 5.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv3.3.1
    • addedInput schema / properties / min_run_count / maximum
      Added value: +9007199254740991
  2. First observedv3.2.3

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/openWorld/non-idempotent/non-destructive, so the safety bar is lower; the description still adds real value by stressing "does not modify code" and "returns suggestions only," which tells the agent about the human-in-the-loop follow-up step. It does not mention that results reflect live Replicate popularity data that changes between calls, which matters given idempotentHint=false.

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?

Front-loaded scoping sentence, then usage, then Args, then Returns, then Examples — each section earns its place. The Returns block is essential since no output schema exists; the Args section is mildly duplicative but aids scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description fully documents the structuredContent shape including suggestion fields and counters, and the workflow note tells the agent what to do with results. Nothing needed to call this correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the Args block largely restates the same defaults and the 15-category list already in the schema. The examples do add a little semantic texture (min_run_count=10000 = "top-tier" models), but nothing about category validity or how threshold affect result volume.

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?

States a specific verb and scope: search Replicate for popular models NOT yet in the curated registry, and explicitly says it returns suggestions only. This differentiates it functionally from the sibling replicate_search_models, though it never names that sibling, so the agent must infer the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Use this to find new models worth adding" plus the follow-up workflow (ask Claude to edit src/models.ts) gives clear context, and the Examples block shows two concrete invocation scenarios. Missing is an explicit when-NOT or a named alternative (e.g. vs replicate_search_models).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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