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BotSpot: AI Trading, Backtests and Bots

List artifacts for a deployment run

list_deployment_artifacts
Read-only

Returns the manifest for the latest or specified deployment run, including artifact kinds, keys, sizes, row counts, timestamps, symbols, queryability, visibility, and upload status. Use this to find the canonical agent_detail and memory artifacts before asking why a live or scheduled bot made a decision. For scheduled AI-agent strategies, verify agent_detail plus memory_events, memory_retrievals, and memory_state are present to confirm tool usage, thinking/token telemetry, and memory persistence between runs.

Input Schema

NameRequiredDescriptionDefault
runIdNo
deploymentIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

readOnlyHint already covers the safety profile, and the description adds substantial context beyond annotations: what the response manifest contains, and how to interpret presence/absence of agent_detail, memory_events, memory_retrievals, and memory_state for scheduled strategies. It does not describe pagination or behavior when no runs exist, but for a read-only listing tool this is rich disclosure.

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 leading sentence front-loads the return value and its fields, and the guidance follows in priority order. The second sentence is dense with multi-item lists but every clause is actionable, so there is little waste.

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?

There is no output schema, so the description carries the return-value burden and does so by enumerating manifest fields and the specific artifact keys to look for. The main residual gap is the relationship between the two parameters and what happens when the deployment has no runs.

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 0% and neither parameter is documented in the schema. The description compensates for runId by implying that omitting it yields the latest run, which is real added meaning. However, deploymentId's role (scope to a deployment, with runId selecting within it) is never made explicit, so the coverage gap is only partially closed.

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 verb and resource: it returns the artifact manifest for the latest or a specified deployment run, and enumerates the manifest fields (kinds, keys, sizes, row counts, timestamps, symbols, queryability, visibility, upload status). It never names the closest siblings (get_deployment_artifact, query_deployment_artifact), so the agent must infer the list-vs-single-vs-query distinction, which keeps it below 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 Guidelines4/5

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

It gives a concrete use case: find the canonical agent_detail and memory artifacts before investigating why a live or scheduled bot made a decision, plus a verification checklist for scheduled AI-agent strategies. It stops short of stating when to prefer query_deployment_artifact or get_deployment_artifact instead, so no explicit exclusions or alternatives are provided.

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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