XFMS — Model Source
Server Details
Pick the right LLM for any task. Ranked shortlist with rationale across 8 evaluators.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- VisionAIrySE/XFMS
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- 0
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Tool Definition Quality
Average 4.5/5 across 5 of 5 tools scored.
Each tool has a clearly distinct purpose: discover for understanding criteria, pick for single best, rank for shortlist, compare for user-specified models, and benchmark for engine-chosen candidates. The descriptions explicitly state when to use each, leaving no ambiguity.
All tool names are single, lowercase imperative verbs (discover, pick, rank, compare, benchmark), following a consistent pattern with no mixing of styles or conventions.
With 5 tools, the set is well-scoped for LLM selection. Each tool serves a necessary and distinct function without redundancy or gaps, making it easy for an agent to navigate.
The tool set covers the full lifecycle of model selection: understanding criteria, getting a single answer, comparing alternatives, and testing with live data. No obvious gaps exist for the stated domain.
Available Tools
5 toolsbenchmarkBenchmark the engine's top picks with real test queriesARead-onlyIdempotentInspect
Run a live A/B test against the engine's TOP 3 PICKS for a stated purpose — the engine chooses the candidates from the full catalog. Generates 5 representative test queries (auto-expands to 10 or 15 if results are too close to call), runs them through the picked models in parallel, and returns real cost, latency, and plain-English commentary on who won what. Use AFTER pick or rank when the user wants the engine's own picks stress-tested with live data. DO NOT use this when the user has already named specific candidate models — the engine will ignore the names and test its own picks. Use compare instead in that case. Costs more than rank (15+ live LLM calls).
| Name | Required | Description | Default |
|---|---|---|---|
| purpose | Yes | One sentence describing what the model will be used for. The benchmark generates representative test queries from this — so be concrete, not vague. |
Output Schema
| Name | Required | Description |
|---|---|---|
| models | No | Ranked shortlist of models, highest score first. |
| status | No | |
| ab_result | No | |
| 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits: generates 5 test queries (auto-expands to 10/15), runs top 3 picks in parallel, returns cost/latency/commentary. This adds significant context beyond annotations, which already indicate read-only, open-world, idempotent, non-destructive behavior. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core action, followed by details and usage guidance. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's single parameter, rich annotations, and output schema, the description fully explains what it does, how it works, when to use it, and what results to expect. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The sole parameter 'purpose' has a schema description urging concreteness and explaining its role in query generation. The description reinforces this: 'Generates 5 representative test queries from the purpose.' Together, they provide complete semantic clarity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action: 'Run a live A/B test against the top model picks for a stated purpose.' It specifies the verb (run/test), resource (top model picks), and action (A/B test), and distinguishes from siblings by positioning after 'pick' or 'rank' and highlighting its unique value.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance is provided: 'Use AFTER pick or rank when the user has asked for real evidence before committing — this is opt-in, not automatic.' It also contrasts with 'rank' by noting higher cost but ground-truth data, enabling correct selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compareCompare specific models head-to-head with real test queriesARead-onlyIdempotentInspect
Run a live A/B test between 2–5 user-specified models for a stated purpose. NO ranking step — the supplied model_ids ARE the candidate set. Generates 5 representative test queries from the purpose, runs them through every named model in parallel, and returns real cost, latency, and plain-English commentary on who won what. Unknown IDs are dropped with a note; if fewer than 2 IDs resolve, the call refuses. Use this whenever the user names specific models to compare (e.g. 'A/B test X and Y'). For engine-chosen candidates, use benchmark instead. Costs more than rank (10+ live LLM calls). Free-tier note: when any candidate ends in ':free', the probe is capped at 3 queries (no adaptive expansion) because free-tier rate limits often push longer probes past the deploy's 5-minute ceiling — evidence will be shallower. The commentary surfaces this when it happens.
| Name | Required | Description | Default |
|---|---|---|---|
| primary | No | Optional. Only affects the plain-English commentary at the end — does not change which models are tested. Marks the dimension the user cares most about so the commentary calls out that winner first. | |
| purpose | Yes | One sentence describing what the models will be used for. Used ONLY to generate representative test queries for the head-to-head — not to rank the catalog. Be concrete, not vague. | |
| model_ids | Yes | Exact model IDs to test head-to-head, in caller-chosen order. 2–5 IDs. Examples: 'nvidia/nemotron-3-super-120b-a12b:free', 'openai/gpt-oss-120b:free'. Unknown IDs are dropped with a note; if fewer than 2 resolve, the call is refused. Use this whenever the user has already named candidates — do NOT call `benchmark` in that case. |
Output Schema
| Name | Required | Description |
|---|---|---|
| status | No | |
| purpose | No | |
| ab_result | No | |
| refusal_reason | 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. |
| model_ids_tested | No | |
| invalid_model_ids | No | |
| model_ids_requested | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds significant behavioral context: it generates 5 representative test queries, runs them in parallel, returns cost/latency/commentary, drops unknown IDs with a note, refuses if fewer than 2 IDs resolve, and includes a free-tier note about capping at 3 queries with shallower evidence. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is detailed but not overly long. It front-loads the core action and then provides necessary usage rules and behavioral notes. Each sentence serves a purpose, though some minor redundancy (e.g., repeating 'head-to-head') could be trimmed. Overall well-structured and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, output schema present, annotations rich), the description covers all essential aspects: purpose, when to use, parameter roles, behavioral details (refusal, dropping, free-tier cap), and return values (cost, latency, commentary). It is fully complete for an AI agent to select and invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. Description adds meaningful context beyond schema: explains that 'primary' only affects commentary order, 'purpose' is solely for generating test queries (not for ranking catalog), and 'model_ids' must be exact IDs with min/max constraints and examples. This justifies a score of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description explicitly states 'Run a live A/B test between 2–5 user-specified models for a stated purpose.' It clearly identifies the verb 'compare', the resource 'models', and the scope 'head-to-head'. It also distinguishes from sibling tool 'benchmark' by noting that for engine-chosen candidates, one should use benchmark instead.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description provides explicit when-to-use guidance: 'Use this whenever the user names specific models to compare.' It also gives a clear when-not-to-use: 'For engine-chosen candidates, use `benchmark` instead.' Additionally, it contrasts cost with 'rank' tool: 'Costs more than `rank`.'
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discoverDiscover quality dimensionsARead-onlyIdempotentInspect
Show which quality dimensions matter for a stated purpose, WITHOUT ranking any models. Returns the inferred weights and the discovery-walk trace. Useful for understanding how XFMS interprets the purpose before committing to a pick.
| Name | Required | Description | Default |
|---|---|---|---|
| purpose | Yes | One sentence describing the task. The tool returns which quality dimensions XFMS would weigh for this purpose, without actually ranking any models. Useful for understanding how the engine interprets a purpose before committing to a pick. |
Output Schema
| Name | Required | Description |
|---|---|---|
| events | No | Trace of the discovery walk. |
| weights | No | Per-dimension weights inferred for this purpose. |
| derived_purpose | 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and not destructive. The description adds that it returns 'inferred weights and the discovery-walk trace,' which is valuable behavioral detail beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, each serving a distinct purpose: first states core function, second explains return value and use case. No wasted words, front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one parameter, full schema coverage, and presence of output schema, the description covers purpose, behavior, return type, and usage context. No gaps identified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema includes a description for the only parameter 'purpose', covering 100% of parameters. The tool description does not add substantially new information for the parameter beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it shows quality dimensions for a purpose, explicitly mentions 'WITHOUT ranking any models,' which distinguishes it from sibling tools pick and rank.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says 'Useful for understanding how XFMS interprets the purpose before committing to a pick,' providing clear context for when to use it, but does not explicitly discuss when not to use or alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pickPick the best LLMARead-onlyIdempotentInspect
Return the single best LLM for a stated purpose. Concise output, no list. Use when the user has settled on the criteria and just wants one answer.
| Name | Required | Description | Default |
|---|---|---|---|
| purpose | Yes | One sentence describing what the model will be used for. Be concrete, not vague: 'summarizing 50-page commercial leases' works; 'summarization' does not. |
Output Schema
| Name | Required | Description |
|---|---|---|
| name | No | |
| model_id | No | |
| provider | No | |
| rationale | No | |
| total_score | 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, openWorld, idempotent, and non-destructive. Description adds 'concise output, no list' but does not deepen behavioral transparency beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two succinct sentences front-load purpose and usage. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has a single parameter with full schema description, output schema exists, and description covers when to use. Complete for its complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a rich description for 'purpose' parameter. The tool description adds no further parameter meaning, meeting baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Return', resource 'single best LLM', and scope 'stated purpose', distinguishing from siblings 'discover' and 'rank' which return lists or rankings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use when the user has settled on the criteria and just wants one answer', providing clear context for when to invoke this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rankRank LLMsARead-onlyIdempotentInspect
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 | 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. | |
| 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
| Name | Required | Description |
|---|---|---|
| models | No | Ranked shortlist of models, highest score first. |
| status | No | |
| 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructiveness. The description adds value by explaining the output format (weights, scores, rationale), which helps the agent understand what to expect beyond the structured data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no extraneous words. It front-loads the core purpose and output, then adds usage guidance. Every sentence serves a clear function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity with 4 parameters (all well-documented in schema), the description adequately covers return values and usage context. An output schema exists, so return details are partially covered. It lacks edge case or error handling notes, but overall it's sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents parameters thoroughly. The description does not add meaningful parameter-level information beyond what's in the schema, earning the baseline score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Rank' and the resource 'LLMs', and specifies the output (shortlist with weights, scores, rationale). It also distinguishes from siblings by noting it's for comparing alternatives, not single answers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises when to use: 'when the user wants to see and compare alternatives, not just one answer.' This implies a contrast with siblings like 'pick' for single answers, though it doesn't name them explicitly. It provides clear context but could be more specific about exclusions.
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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