Skip to main content
Glama

Agent Attention Signal

consensus_signal

What tokens or wallets are being queried by other agents right now. Proprietary data derived from SolEnrich's own query stream. Pass address for that entity's rank/percentile/trend; omit it for top-N. Windows: 1h, 6h, 24h.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoEntity type to querytoken
limitNoTop-N size when address is omitted
windowNoLookback window1h
addressNoOptional Solana address — single-entity report when provided

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries full responsibility for behavioral disclosure. It reveals the proprietary SolEnrich query stream, real-time nature, and the two distinct output modes, which is useful context. It lacks details on rate limits, data freshness guarantees, or how 'trend' is computed, but overall it is fairly transparent.

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?

Three sentences with no redundancy: the first states the core function, the second adds provenance, and the third gives usage modes and windows. Every sentence earns its place, making it highly concise and well-structured.

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?

Without an output schema, the description reasonably covers return expectations by mentioning top-N, rank/percentile/trend, and windows. It could provide an example or specify the response format, but it is sufficient for the tool's moderate complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% parameter coverage with descriptions, so the baseline is 3. The description adds value by clarifying that passing `address` yields rank/percentile/trend and that omitting it gives top-N, which goes beyond the schema's 'single-entity report' phrasing.

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 clearly identifies the tool as surfacing current query activity from other agents, which distinguishes it from price or money-flow tools. It also specifies two modes (address vs. top-N) and names the proprietary data source, making its role unambiguous among the sibling tools.

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?

The description explains how to use the tool (omit address for top-N, pass address for a single-entity report) and the available time windows, but it does not explicitly contrast it with sibling tools or state when not to use it. While the context is clear, there are no named alternatives or exclusions.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation3/5

With 32 tools, several have overlapping purposes, such as wallet_history vs portfolio_history (both track wallet portfolio over time) and smart_money_flow vs smart_money_trenches (both follow smart money movements). However, most tools have clearly distinct scopes, and detailed descriptions help differentiate them.

Naming Consistency5/5

All tool names follow snake_case with a predictable verb_noun or noun phrase pattern (e.g., enrich_token, compare_wallets, perps_market_trend). The consistent structure makes the set easy to navigate, even the 'perps_' prefix group is uniform.

Tool Count2/5

32 tools is well above the 25-tool threshold, making the surface feel heavy. While the breadth reflects the wide domain of Solana analytics, the sheer number can overwhelm agents and increase the chance of selecting the wrong tool.

Completeness4/5

The tool set covers most aspects of Solana token/wallet/perp analysis, including enrichment, comparison, trend tracking, smart money flows, and perp market structure. Minor gaps exist, such as no direct historical OHLCV endpoint, but the existing tools handle core workflows well.