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kairos-signal

by io.github.OV3RK177
OverviewDiscussionsAdmin

Fetch raw dataset rows

fetch_dataset
Read-onlyIdempotent

Query records from a dataset with limit/offset. Free tier: 10 records per query. For depin_onchain, raw archived observations, including rejected rows, are retained. observation_quality reports limited PKT airdrop checks; not_checked means not validated. This read-time annotation is not covered by original proof hashes.

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": {
      "dataset": "<dataset>"
    },
    "name": "fetch_dataset"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax records (max 10 for free tier)
offsetNoRecord offset
datasetYesDataset name (e.g., depin_onchain)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / dataset / description
      Previous value: -"Dataset name (e.g., depin_stats)"New value: +"Dataset name (e.g., depin_onchain)"
  2. Changed1 schema field changed
    • changedInput schema / properties / dataset / description
      Previous value: -"Dataset name (e.g., master_leads)"New value: +"Dataset name (e.g., depin_stats)"
  3. First observed

TDQS

B3.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description adds real behavioral context beyond them: the free-tier 10-record cap, that rejected rows are retained for depin_onchain, and that observation_quality read-time annotations are not covered by original proof hashes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core query sentence is front-loaded, but the remainder pivots into dataset-specific caveats about observation_quality and proof hashes that only apply to one dataset, diluting focus for general callers. Some sentences earn their place; others are only conditionally relevant.

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

Completeness3/5

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

For a read-only query tool with an annotation-covered safety profile and full schema coverage, the essentials are present. However, with no output schema, gaps remain around default limit, ordering, and pagination behavior that the description could reasonably address.

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%, so the schema already documents dataset, limit, and offset, establishing the baseline of 3. The description restates limit/offset and echoes the tier cap ('10 records per query') but adds no format, default, or ordering detail beyond the schema.

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 verb and resource: 'Query records from a dataset with limit/offset,' and the title reinforces 'Fetch raw dataset rows.' It is unambiguous what the tool does, though it does not explicitly distinguish itself from siblings like list_datasets or get_data_dictionary.

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

Usage Guidelines2/5

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

There is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named. It gives context (free tier cap, depin_onchain retention) but never tells the agent when this tool is the right choice versus list_datasets, get_data_dictionary, or purchase_data.

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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