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JavaScript
Hybrid

Recommend a node and model

recommend
Read-onlyIdempotent

Identify a suitable AI node and model for a task by extracting features locally, returning ranked options with cost estimates and reasons.

Instructions

Recommend the best AI node + model for a task, with an enforceable cost estimate and human-readable reasons. Use this first when you know the task but not the model. Features (task class, size, language, needs) are extracted LOCALLY from task and only those features are sent to the registry — the prompt never leaves this machine. Returns ranked candidates with score components, a cost estimate pinned to the node's card revision, an optional cheaper two-step scenario, and MCP tool categories the task likely needs. To browse instead, use search_offerings; to price a specific chain, use estimate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesThe task description or the prompt itself, e.g. "Translate this Polish contract to English and list the risks". Analysed locally; not sent.
policyNoOptional constraints and preferences for the recommendation.
est_output_tokensNoExpected answer length in tokens, if known. Improves the cost estimate; otherwise inferred from the task.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed9 schema fields changedv0.1.1
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / est_output_tokens / description
      Added value: +"Expected answer length in tokens, if known. Improves the cost estimate; otherwise inferred from the task."
    • addedInput schema / properties / est_output_tokens / minimum
      Added value: +1
    • changedInput schema / properties / est_output_tokens / type
      Previous value: -"number"New value: +"integer"
    • addedInput schema / properties / policy / additionalProperties
      Added value: +false
    • addedInput schema / properties / policy / description
      Added value: +"Optional constraints and preferences for the recommendation."
    • addedInput schema / properties / policy / properties
      Added value: +{
+  "limit": {
+    "default": 3,
+    "description": "Number of candidates to return.",
+    "maximum": 20,
+    "minimum": 1,
+    "type": "integer"
+  },
+  "max_input_per_mtok": {
+    "description": "Maximum input price in USD per million tokens.",
+    "minimum": 0,
+    "type": "number"
+  },
+  "max_total_usd": {
+    "description": "Maximum estimated total cost of the call in USD.",
+    "minimum": 0,
+    "type": "number"
+  },
+  "min_tier": {
+    "description": "Exclude offerings below this trust tier.",
+    "enum": [
+      "unverified",
+      "community",
+      "verified",
+      "attested"
+    ],
+    "type": "string"
+  },
+  "prefer_local": {
+    "description": "Give a bonus to nodes on this machine or LAN.",
+    "type": "boolean"
+  },
+  "preset": {
+    "description": "Weighting preset: cheap favours price, fast favours latency/throughput, quality favours model strength, private favours trust tier.",
+    "enum": [
+      "cheap",
+      "fast",
+      "quality",
+      "private"
+    ],
+    "type": "string"
+  },
+  "region": {
+    "description": "Required data region, e.g. \"eu\". Offerings pinned to other regions are excluded.",
+    "type": "string"
+  }
+}
    • addedInput schema / properties / task / description
      Added value: +"The task description or the prompt itself, e.g. \"Translate this Polish contract to English and list the risks\". Analysed locally; not sent."
    • addedInput schema / properties / task / minLength
      Added value: +1
  2. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations only cover readOnly/idempotent/openWorld, but the description adds substantial beyond-schema behavior: features are extracted LOCALLY and the prompt never leaves the machine; results are pinned to the node's card revision; returns ranked candidates with score components and an optional cheaper two-step scenario. This privacy and determinism context goes well past what annotations declare.

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?

Front-loaded with purpose, then when-to-use, then privacy mechanics, return shape, and sibling routing in five efficient sentences. Slightly dense with the returns enumeration, but every sentence earns its place and none is filler.

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 3-param tool with a nested policy object, no output schema, and read-only annotations, the description covers purpose, routing, privacy behavior, and return contents well. It omits error/failure modes (e.g. what happens when no candidates pass the filters) and turnaround/latency behavior, which a full 5 would address.

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 100%, so the baseline is 3. The description adds meaning beyond the schema by clarifying that `task` is analysed locally and never sent, and that est_output_tokens improves cost estimation (matching the schema's hint). It doesn't extend the policy object semantics, which the schema already covers, so it lands just above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Recommend the best AI node + model for a task') with the value proposition (enforceable cost estimate, human-readable reasons). It explicitly distinguishes itself from siblings search_offerings (browse) and estimate (price a specific chain), so an agent can route correctly without opening schemas.

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

Usage Guidelines5/5

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

Explicit when-to-use: 'Use this first when you know the task but not the model.' It also names both alternatives and the condition that selects each (browse vs price a chain). This is textbook when/when-not/alternatives coverage.

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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