inferenceindexer-mcp

recommend_models

Recommend the best-value AI models for given constraints, ranked with receipts.

The core answer endpoint: give it constraints and it returns the top models
ranked by Cost/IQ (quality-adjusted price, lower is better), each with a
plain-English 'why', a hot-swap endpoint_config (provider base_url + native
model id, ready to call), as-of timestamps, and runner-ups.

Args:
    budget_max_usd_per_m: Max blended price $/M (optional).
    context_min: Minimum context window in tokens (optional).
    modality: 'text' (default), 'vision', or 'any'.
    zdr: Require zero-data-retention providers (optional).
    eu_sovereign: Require EU-sovereign providers (optional).
    reasoning: Filter reasoning models (null = any, true/false).
    aa_min: Hard floor on the AA Intelligence Index score (quality gate).
    use_case: Task profile: support | volume | extraction | summarization | coding | research.
    prefer_callable: Demote models without a verified call recipe below callable ones.
    limit: Max recommendations (1-20, default 5).
Returns: ranked recommendations with endpoint_config and ranking evidence.

Input Schema

Name	Required	Default
`zdr`	No
`limit`	No
`aa_min`	No
`modality`	No	text
`use_case`	No
`reasoning`	No
`context_min`	No
`eu_sovereign`	No
`prefer_callable`	No
`budget_max_usd_per_m`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedOct 20, 2026

addedInput schema / properties / aa_min

Added value: +{
+  "anyOf": [
+    {
+      "type": "number"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Aa Min"
+}

addedInput schema / properties / prefer_callable

Added value: +{
+  "default": false,
+  "title": "Prefer Callable",
+  "type": "boolean"
+}

addedInput schema / properties / use_case

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "title": "Use Case"
+}

AddedSep 2, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations present, the description carries the full burden and does well: it discloses the ranking metric (Cost/IQ, lower is better), that each result includes a plain-English 'why', a ready-to-call endpoint_config, as-of timestamps, and runner-ups. Gaps remain around read-only nature, rate limits, or cost of the call itself.

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 summary sentence followed by an organized Args block; the structured layout is appropriate for 10 parameters. The parameter block largely restates schema field names, which would be redundant at higher coverage, but here it is the only source of parameter meaning, so the length is justified.

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?

An output schema exists, so return values need not be explained in depth, and the description nonetheless summarizes the payload (endpoint_config, evidence, runner-ups). Combined with full parameter documentation, the only missing piece is explicit sibling disambiguation, which keeps it short of a 5.

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

Parameters5/5

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

Schema description coverage is 0%, yet the description documents every one of the 10 parameters with meaning beyond the schema: modality's allowed values ('text' default, 'vision', 'any'), use_case's task profiles, aa_min as an AA Intelligence Index quality gate, and limit's range and default (1-20, default 5). This fully compensates for the empty schema.

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-value AI models') and the ranking criterion (Cost/IQ, quality-adjusted price). It distinguishes itself from siblings like search_models and compare_providers by positioning itself as 'the core answer endpoint' that returns ranked recommendations with evidence, not raw listings.

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?

Calling itself 'the core answer endpoint' implies this is the default recommendation path, which is useful positioning. However, it never states when NOT to use it or names an alternative (e.g. search_models for browsing or compare_providers for head-to-head), so the routing decision is left to inference.

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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