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BotSpot: AI Trading, Backtests and Bots

by io.github.grzesir
OverviewDiscussionsAdmin

Discover or describe market research data

search_data_catalog
Read-onlyIdempotent

Find BotSpot-provided public research datasets the agent can query. Pass datasetId alone to inspect a selected dataset contract, schemas, units, coverage and historical guarantees. Catalog metadata is not observations or a permission grant. Use this for versioned symbol-membership universes, public news discovery, or when unsure whether SEC, Treasury, BLS, BEA, OFR, or CFTC is the right source. Universes contain membership only. This tool does not provide real-time prices, broker data, or premium news.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

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

Input Schema

NameRequiredDescriptionDefault
limitNo
queryNoNatural-language search text, such as SEC filings, GDP, CPI, Treasury debt, repo, or COT.
cursorNoOpaque nextCursor from the same owner and catalog search. Restart discovery if expired.
sourceNoOptional source filter.
datasetIdNoDescribe this registered dataset instead of listing matches. Use datasetId alone to retrieve query/result schemas, units, operations, coverage and historical guarantees.
operationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, yet the description still adds genuinely non-obvious context: catalog metadata is not observations and not a permission grant, and universes contain membership only. It does not cover pagination behavior or limits, but the added semantic guardrails are substantial for a read-only discovery tool.

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 purpose and the describe-mode instruction are front-loaded, and most sentences carry distinct information. There is minor restatement (datasetId-alone appears in both the description and the schema) and the negative-scoping list at the end is a touch listy, but no sentence is wasted.

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 no output schema, the description usefully previews what a datasetId lookup returns (contract, schemas, units, coverage, historical guarantees) and what a search returns (membership universes). Six optional parameters and cursor-expiry semantics are left to the schema, which is acceptable, though the limit cap and operations filter go unmentioned.

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 67%: query, cursor, source and datasetId are already documented in the schema, and the description mainly restates the datasetId-alone contract. The limit and operations parameters get no clarification in either place, so the description neither fully compensates nor adds much 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?

Opens with a specific verb and resource ('Find BotSpot-provided public research datasets the agent can query') and covers both modes: listing matches and describing one dataset via datasetId. It separates catalog metadata from actual observations, which implicitly distinguishes it from query_data, but never names a sibling tool explicitly, so differentiation is inferred rather than stated.

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

Usage Guidelines5/5

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

Gives concrete when-to-use triggers (versioned symbol-membership universes, public news discovery, or when unsure whether SEC/Treasury/BLS/BEA/OFR/CFTC is the right source) and explicit when-not boundaries (no real-time prices, broker data, or premium news). It also states the invocation pattern for the describe mode: pass datasetId alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All BotSpot: AI Trading, Backtests and Bots toolsInspect
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