Gemral Edge

Top Convergence Signals

get_top_signals

Multi-stream algorithmic convergence surfacing when independent federal contracts, insider buying, and congressional trade vectors align on a single company. Current top-ranked signals with composite score and bias.

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": "get_top_signals"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No

Output Schema

Name	Required	Description
`notice`	No	Free-tier upgrade notice; only when results are masked/delayed.
`results`	Yes	Ranked convergence signals (public columns only; scoring weights never exposed).
`history_limit`	No	Present when the license limits history depth: earliest_allowed date, history_days and how many older items were removed.
`deprecated_fields`	No	Present only while a renamed key is still returned next to its replacement: field (old key), replacement (new key, same value), removal (ISO date after which the old key is gone).

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 26, 2026

addedOutput schema / properties / deprecated_fields

Added value: +{
+  "description": "Present only while a renamed key is still returned next to its replacement: field (old key), replacement (new key, same value), removal (ISO date after which the old key is gone).",
+  "items": {
+    "additionalProperties": true,
+    "type": "object"
+  },
+  "type": "array"
+}

First observedOct 23, 2026

TDQS

C2.4/5.0

Behavior2/5

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

The annotations are actively unhelpful here: readOnlyHint=false on a tool named get_top_signals that only returns 'current top-ranked signals' is confusing, and the description does nothing to reconcile it. Beyond that, it omits freshness/refresh cadence, ranking methodology, and whether the list is cached or computed on demand — the details that would actually govern correct invocation.

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?

Two sentences, reasonably compact, but the first is dense with marketing-style jargon ('multi-stream algorithmic convergence', 'vectors align') that costs decoding effort without adding precision. What is useful (current top-ranked, composite score, bias) is buried in the second sentence.

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?

An output schema exists, so return-value detail need not be explained, and the description covers the conceptual content of the list. Still missing: the meaning of the one input parameter, what 'top-ranked' is ranked by, and how this differs from adjacent screening tools.

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

Parameters2/5

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

Schema description coverage is 0%, and the sole parameter (limit, default 50, max 200) is never mentioned in the description. The agent gets bounds from the schema but no guidance on what the limit applies to or sensible values, so the description fails to compensate for the coverage gap.

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

Purpose3/5

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

The description does convey the resource: ranked convergence signals across federal contracts, insider buying, and congressional trades, with a composite score and bias. However, the phrasing ('algorithmic convergence surfacing when ... vectors align') is jargon-heavy rather than a crisp verb+resource statement, and it gives no signal to distinguish it from siblings like screen_convergence, get_precursor_signals, or explain_score.

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 guidance and no alternatives are named. The agent must infer that this returns the current top-N list while screen_convergence presumably does custom filtering, but that distinction is never stated.

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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