XFMS — Xpansion Framework Model Source

Rank LLMs

rank

Read-onlyIdempotent

Rank LLMs for a stated purpose. Returns a shortlist with weights, scores, and plain-English rationale per pick. Use when the user wants to see and compare alternatives, not just one answer.

Input Schema

TableJSON Schema

Name	Required	Description
`top_n`	No	How many models to return in the ranked list. Defaults to 5. Use 1 if you only want the single best pick; use 10+ if you want to see deeper alternatives.
`primary`	No	Ordered priorities, highest first, for example quality then cost. Later dimensions break exact ties; unspecified dimensions do not decide the winner.
`purpose`	Yes	One sentence describing what the model will be used for. Be concrete, not vague: 'fixing bugs in a Python codebase' works; 'coding' does not. The more specific the purpose, the better XFMS can infer which quality dimensions matter.
`capabilities`	No	Required capabilities the model MUST support. Models missing any listed capability are filtered out before ranking. 'vision' = image input, 'audio_in' = audio input, 'tool_use' = function calling, 'structured_outputs' = JSON schema-constrained output. Omit when the task is plain text with no tool use.

Output Schema

TableJSON Schema

Name	Required	Description
`models`	No	Ranked shortlist of models, highest score first.
`status`	No	'ranked' (normal), 'low_confidence' (capability requirements were relaxed to find any match), or 'quality_floor_refused' (candidates were found but the best one scored too low to recommend — see quality_floor_reason; models[] still lists what was considered).
`catalog_size`	No
`filtered_out`	No
`xpansion_update`	No	Optional rotating launch nudge from Xpansion. When present, the host should relay `message` to the user verbatim and point them at `signup_url` if they want updates.
`quality_floor_reason`	No
`frontier_filtered_out`	No	How many models in models[] are neither recent nor top-tier on quality (informational — none are removed from the list, this just means they weren't eligible to be the recommendation).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 22, 2026

changedInput schema / properties / primary / description

Previous value: -"Mark dimensions as primary tier. When set, the engine switches from weighted-sum blending to lexicographic ordering: the primary dimension is the sole ranking axis, and other dimensions only break ties. Use when the user says 'cheapest model, period' or similar — their stated preference becomes sacrosanct."New value: +"Ordered priorities, highest first, for example quality then cost. Later dimensions break exact ties; unspecified dimensions do not decide the winner."

Changed3 schema fields changedSep 22, 2026

addedOutput schema / properties / frontier_filtered_out

Added value: +{
+  "description": "How many models in models[] are neither recent nor top-tier on quality (informational — none are removed from the list, this just means they weren't eligible to be the recommendation).",
+  "type": "integer"
+}

addedOutput schema / properties / quality_floor_reason

Added value: +{
+  "type": [
+    "string",
+    "null"
+  ]
+}

addedOutput schema / properties / status / description

Added value: +"'ranked' (normal), 'low_confidence' (capability requirements were relaxed to find any match), or 'quality_floor_refused' (candidates were found but the best one scored too low to recommend — see quality_floor_reason; models[] still lists what was considered)."

Changed1 schema field changedJun 9, 2026
- changedOutput schema / properties / models / items / properties / provider / type
```
Previous value: -"string"New value: +[
+  "string",
+  "null"
+]
```

Changed1 schema field changedMay 19, 2026

addedOutput schema / properties / xpansion_update

Added value: +{
+  "description": "Optional rotating launch nudge from Xpansion. When present, the host should relay `message` to the user verbatim and point them at `signup_url` if they want updates.",
+  "properties": {
+    "call_count": {
+      "type": "integer"
+    },
+    "id": {
+      "type": "string"
+    },
+    "message": {
+      "type": "string"
+    },
+    "signup_url": {
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

First observedMay 18, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, openWorld, non-destructive), and the description adds useful behavioral context beyond them: it returns multiple candidates with weights, scores, and human-readable rationale, implying a multi-answer ranked presentation. No contradiction with annotations.

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?

Three sentences, each earning its place: action, output shape, and usage condition. The core verb is front-loaded, and there is zero filler or redundant restatement of schema content.

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?

Given a 100%-covered schema, a present output schema, and rich annotations, the description covers the essential when/what. The only notable omission is guidance on how ranking decisions (weights/scores) should be interpreted or how this differs from compare-style analysis, but the structured fields already carry most of that burden.

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 description coverage is 100%, so the baseline is 3; the schema itself documents all four parameters well. The description adds little parameter-specific meaning beyond 'stated purpose' scaffolding — it does not compensate with extra semantics, but it doesn't need to given the schema.

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 states a specific verb and resource ('Rank LLMs') and describes a distinctive output (shortlist with weights, scores, plain-English rationale). The phrase 'not just one answer' separates it from the 'pick' sibling, but it does not explicitly distinguish it from 'compare', 'benchmark', or 'discover', so sibling differentiation is partial.

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?

The description gives a clear trigger condition: 'Use when the user wants to see and compare alternatives, not just one answer.' This provides solid when-to-use context but includes no exclusions and does not name alternative tools or route the agent to them, as required for a 5.

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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