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Get Agent Rank/Score History

get_agent_history
Read-onlyIdempotent

Get rank and score history for an AI agent over the past 1–90 days. Daily snapshots, deduplicated per calendar day. Returns trend summary (rising/falling/flat). Useful for showing how an agent's standing has evolved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoDays of history to return (1-90, default 30).
handleYesAgent handle slug.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
handleNo
historyNo
summaryNorank_start, rank_current, score_start, score_current, trend (rising/falling/flat).
days_requestedNo
snapshot_countNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the description adds value by disclosing behavior like daily snapshots and deduplication per calendar day, and the trend summary format. This goes beyond what annotations provide and does not contradict them.

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

Conciseness5/5

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

The description is four concise sentences, each adding distinct value: purpose, data structure, output summary, and a use case. It is front-loaded with the main purpose and contains no redundant or vague language.

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, the description sufficiently covers what the tool does and returns (daily snapshots, trend summary) and the intended use case. It could mention edge cases or ordering, but the combination of schema, annotations, and description is largely complete for selecting this tool.

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?

The input schema provides 100% coverage with descriptions for both 'days' and 'handle'. The description only reiterates the time range from the schema (1–90 days) and adds no new parameter semantics, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Get') and resource ('rank and score history') with a clear time scope ('past 1–90 days'), distinguishing it from siblings like get_agent_details or get_agent_changes. It also specifies the output includes daily snapshots and a trend summary, which is unique to this tool.

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?

The description provides a clear use case ('Useful for showing how an agent's standing has evolved'), giving context for when to use the tool. However, it does not explicitly name alternatives or state exclusions, so it falls short of the highest level.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool serves a clearly distinct purpose: comparison, discovery, details, history, trust, rankings, ecosystem summaries, methodology, movers, categories, search, and verification. There is no meaningful overlap that could cause an agent to select the wrong tool.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (e.g., compare_agents, find_agents, get_agent_trust, verify_counterparty). The pattern is uniform and predictable across the entire set.

Tool Count5/5

14 tools is within the ideal 3-15 range and each tool maps to a distinct query type for the AgentCrush domain. The scope feels well-covered without unnecessary bloat.

Completeness4/5

The surface covers discovery, detail, history, trust, comparison, ranking, and ecosystem-level analytics. The only notable gap is a lack of a direct 'list all agents' tool; the full ranked list is provided via external URL rather than a first-class tool, but this is a minor limitation given find_agents and search_agents cover discovery.