AgentFeed

Tokenized stock rankings

get_peg_universe

Read-onlyIdempotent

Tokenized stocks ranked by off-hours peg risk: every tokenized US equity we track, with p95 and max deviation bps, market-open deviation as the control, and median liquidity. Dead pools are excluded rather than reported as perfect pegs. Costs 0.05 USDC per call (x402, USDC on Solana, Base, Polygon, Arbitrum or Arc).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	lookback 1-30, default 7
`min_liquidity_usd`	No	filter out thinner pools

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	Yes	the get_peg_universe payload; a real captured example is free at https://x402.ochinimus.app/api/sample/get_peg_universe
`tool`	Yes	the tool that produced this payload

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 25, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "data": {
+      "additionalProperties": {},
+      "description": "the get_peg_universe payload; a real captured example is free at https://x402.ochinimus.app/api/sample/get_peg_universe",
+      "propertyNames": {
+        "type": "string"
+      },
+      "type": "object"
+    },
+    "tool": {
+      "const": "get_peg_universe",
+      "description": "the tool that produced this payload",
+      "type": "string"
+    }
+  },
+  "required": [
+    "tool",
+    "data"
+  ],
+  "type": "object"
+}

First observedSep 6, 2026

TDQS

A3.9/5.0

Behavior5/5

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

Annotations cover the safety profile (readOnly, idempotent, open world), and the description adds genuinely new operational context: a per-call cost of 0.05 USDC, the x402 payment mechanism, and the accepted chains. It also discloses a behavioral quirk of the result set (dead pools dropped instead of shown at parity), which would otherwise mislead an agent into thinking coverage is complete.

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 the resource and ranking basis, then the payment cost. Efficient overall, though the chain list is verbose and the opening sentence is a dense run-on that mixes scope, metrics, and exclusion policy.

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?

With an output schema present, return values need not be explained, and the description covers scope, metric definitions, exclusions, and cost. It omits pagination/row-count limits for a universe-wide listing and does not say how fresh the data is relative to the days lookback.

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 both parameters (days, min_liquidity_usd) are already documented, and the description adds no syntax, units, or default detail beyond them — 'median liquidity' refers to an output metric, not the filter. Baseline 3 is appropriate when the schema carries the parameter burden.

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?

States a specific verb (ranks/lists), a precise resource (every tokenized US equity tracked), and the metric basis (off-hours peg risk, p95/max deviation bps, market-open control, median liquidity). An agent can tell this is the universe-level peg ranking, but the description never names the closely-related siblings get_peg_deviation or get_peg_sessions, so differentiation relies on inference.

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?

The exclusion policy ('dead pools are excluded rather than reported as perfect pegs') implies when this list is meaningful, but there is no explicit when-to-use or when-not-to-use statement and no routing to get_peg_deviation for single-token pegs. Usage is implied rather than stated.

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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