FreeTier Agent API

llm-pricing

llm_pricing

Read-only

3 free MCP calls per day (all tools together), then $0.005 USDC via x402 or card credits (paid in-band: params._meta["x402/payment"]). LLM pricing: AI model list prices (USD per 1M input/output/cached tokens, context window, vision/tools/reasoning flags) for ~3,000 models across OpenAI, Anthropic, Google, Mistral, Groq, Bedrock, Azure, Together, Fireworks and more, plus verified free tiers/rate limits of AI APIs. Filters: q, provider, free=yes, max_input_per_m, min_context, vision=yes, tools=yes, sort=input|output|context, limit<=100.

Input Schema

Name	Required	Description	Default
`q`	No
`free`	No
`sort`	No
`limit`	No
`tools`	No
`vision`	No
`provider`	No
`min_context`	No
`max_input_per_m`	No

Output Schema

Name	Required	Description	Default
`count`	No
`models`	No
`sources`	No
`returned`	No
`free_tiers`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 26, 2026

changedOutput schema / description

Previous value: -"Inferred from a real response captured on 2026-10-24; extra fields may appear, null-valued fields are untyped."New value: +"Inferred from a real response captured on 2026-10-26; extra fields may appear, null-valued fields are untyped."

Changed1 schema field changedOct 24, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "description": "Inferred from a real response captured on 2026-10-24; extra fields may appear, null-valued fields are untyped.",
+  "properties": {
+    "count": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "free_tiers": {
+      "items": {
+        "properties": {
+          "checked_at": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "free_tier": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "provider_model": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "source_url": {
+            "type": [
+              "string",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "models": {
+      "items": {
+        "properties": {
+          "cached_input_per_m_usd": {},
+          "context_length": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "free": {
+            "type": [
+              "boolean",
+              "null"
+            ]
+          },
+          "function_calling": {
+            "type": [
+              "boolean",
+              "null"
+            ]
+          },
+          "id": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "input_per_m_usd": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "max_input_tokens": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "max_output_tokens": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "output_per_m_usd": {
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "provider": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "reasoning": {
+            "type": [
+              "boolean",
+              "null"
+            ]
+          },
+          "rpm": {
+            "type": [
+              "null",
+              "number"
+            ]
+          },
+          "tpm": {},
+          "vision": {
+            "type": [
+              "boolean",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "returned": {
+      "type": [
+        "number",
+        "null"
+      ]
+    },
+    "sources": {
+      "items": {
+        "properties": {
+          "license": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "name": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "note": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "snapshot": {
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "url": {
+            "type": [
+              "string",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}

First observedOct 18, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations only declare readOnlyHint and openWorldHint; the description adds substantial context the annotations cannot convey: a metered quota (3 free MCP calls/day across all tools), a $0.005 USDC charge via x402 or card credits, and that payment is supplied in-band at params._meta["x402/payment"]. This is exactly the kind of billing/authorization behavior an agent must know before calling.

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?

Two dense sentences with no filler; the billing terms lead (arguably the most decision-critical fact) followed by the data scope and filter list. Slightly crowded but every clause carries information, and nothing is repeated from structured fields needlessly.

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

Completeness5/5

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

For a read-only filterable data tool with an output schema already defining return shape, the description covers cost model, dataset breadth, provider coverage, all filter dimensions and the result cap. An agent has everything needed to decide and invoke correctly.

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% across 9 parameters, so the description carries the full burden and does: it names every parameter (q, provider, free, max_input_per_m, min_context, vision, tools, sort, limit), gives the enum values for the flag/sort params, and adds the constraint limit<=100 that the schema does not express.

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 names a specific resource (AI model list prices: input/output/cached token pricing, context window, capability flags, plus free tiers/rate limits) and its scale (~3,000 models, named providers). It does not explicitly distinguish itself from close siblings such as llm_pricing_sample, dump_llm_pricing or llm_cost, which is the only thing keeping it from a 5.

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 enumerates the available filtering modes (free=yes, vision=yes, tools=yes, sort, limit) which implies how to narrow results, but never states when to choose this tool over llm_cost, llm_pricing_sample or dump_llm_pricing, nor any prerequisites beyond the payment note.

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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