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Coverage of a market

cover_status
Read-onlyIdempotent

Whether the signed-in reader can cover a market now (so DataSnack builds or keeps working its model every night) and, if not, why; with the reader's allowance and credits.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "id": "<id>"
    },
    "name": "cover_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
idYesA DataSnack model id (ch:…), a datasnack.ai/m/… URL, or a market id from search_models

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, non-destructive, closed-world behavior, and the description adds real context on top: the result is per signed-in reader (auth-sensitive), it can explain a negative outcome, and it surfaces allowance and credits. It stops short of describing response shape or what happens when a market is covered versus uncovered.

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?

The core condition is front-loaded in a single dense sentence with no boilerplate. The nested parenthetical about nightly model building is useful context but makes the sentence harder to parse than a two-clause split would.

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?

For a one-parameter read tool with rich annotations and no output schema, the description covers purpose, the auth-scoped subject, the negative-result explanation, and the accompanying allowance/credit data. An agent has enough to call it correctly; only the exact result structure is unstated.

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 coverage is 100% and the single 'id' parameter already documents the accepted id, URL, and market-id forms. The description's use of 'market' is consistent with that but adds no syntax or format detail beyond the schema, so baseline 3 applies.

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 states a specific check — whether the signed-in reader can cover a market — and what the answer includes (reason if not, allowance and credits). It is clearly a distinct read from siblings like cover_market, though it never names that sibling, so distinguishing it relies on inference rather than explicit contrast.

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?

Usage is only implied: an agent can infer this is a pre-check or status query tied to coverage, and the parenthetical about nightly model building hints at why it matters. There is no explicit when-to-use, no prerequisite statement, and no named alternative such as cover_market, so guidance remains inferential.

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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