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ssm82

syntx-ai-mcp

by ssm82
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

list-models

Lists AI models with upload constraints, supported media types, and features; filter by scope, provider, active status, search term, or catalog source.

Instructions

List AI models with upload constraints, supported media types, and features. Filters (all optional, combined with AND): scope (text|image|video|audio|upscale), ai_name (exact match, e.g. "chatgpt"), active_only (default true), search (case-insensitive substring against the model id / value and label), source (which upstream catalog to read: "ai" | "llm" | "both"). The two catalogs are not in sync — use source: "both" to see every model. value is always the model identifier; text-catalog rows additionally carry an llm object with the context window, capabilities and subscription gate.

Input Schema

NameRequiredDescriptionDefault
scopeNoCapability bucket, resolved against the live provider catalog. Omit to receive models from every bucket (including providers whose scope the API does not report).
searchNoCase-insensitive substring matched against the model id and `label`.
sourceNoWhich catalog to read. "ai" (default) = the platform catalog: the general catalog merged with the text catalog, because upstream keeps the two in sync only partially — "gpt-6.1-sol" exists only in the text catalog. "llm" = the text catalog alone (the view the SPA text-flow picker uses). "both" = an explicit spelling of "ai".
ai_nameNoExact syntx.ai provider name, e.g. "chatgpt", "claude", "midjourney".
active_onlyNoWhen true (default), drop inactive models. Set false to include them.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.4.0
    • changedInput schema / properties / scope / description
      Previous value: -"Capability bucket inferred from the syntx.ai provider. Omit to receive models from every bucket (including providers that don't match any known bucket)."New value: +"Capability bucket, resolved against the live provider catalog. Omit to receive models from every bucket (including providers whose scope the API does not report)."
    • changedInput schema / properties / search / description
      Previous value: -"Case-insensitive substring matched against the model `value` and `label`."New value: +"Case-insensitive substring matched against the model id and `label`."
    • addedInput schema / properties / source
      Added value: +{
+  "description": "Which catalog to read. \"ai\" (default) = the platform catalog: the general catalog merged with the text catalog, because upstream keeps the two in sync only partially — \"gpt-6.1-sol\" exists only in the text catalog. \"llm\" = the text catalog alone (the view the SPA text-flow picker uses). \"both\" = an explicit spelling of \"ai\".",
+  "enum": [
+    "ai",
+    "llm",
+    "both"
+  ],
+  "type": "string"
+}
  2. First observedv0.3.0

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden and does useful work: it discloses the default active_only=true, explains that the two catalogs are partially out of sync, and notes that text-catalog rows carry an additional llm object. It does not discuss permissions or rate limits, but those are not central for a listing tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and then moves directly into filter semantics. It is dense but largely earns its length; a few details, such as repeating the exact-match example, could be tightened, but there is no major waste.

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

Completeness4/5

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

There is no output schema, but the description explains the important return distinction: value is always the model identifier, and text-catalog rows additionally carry an llm object with context window, capabilities, and subscription gate. For a zero-required-parameter listing tool, this is sufficiently complete.

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

Parameters4/5

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

Schema coverage is already 100%, so the baseline is 3. The description still adds meaning beyond the schema: it says filters are combined with AND, clarifies that ai_name is exact match, explains the practical difference between 'ai', 'llm', and 'both', and notes that value is always the model identifier.

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 uses a specific verb and resource: 'List AI models with upload constraints, supported media types, and features.' It is clear what the tool returns, but it does not explicitly distinguish this tool from nearby siblings like list-ai-services or get-llm-limits.

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

Usage Guidelines3/5

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

It explains that filters are optional and combined with AND, and gives a concrete rule for catalog selection: use source: 'both' to see every model because the catalogs are not in sync. However, it gives no explicit guidance on when to choose this tool over sibling tools that also expose model or service information.

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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