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by com.tipranks
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get_analysts_by_firm

Read-onlyIdempotent

List analysts at a given firm (case-insensitive substring match).

Each row: {name, firm, rank, avg_return_1y_pct, covered_tickers_sample,
total_covered}. Results are deduped by analyst name (keeping the best
rank) and sorted by rank ascending.

When `sector` is provided, the per-analyst coverage list is filtered
to tickers in that sector and the row shape becomes {name, firm,
rank, avg_return_1y_pct, sector, covered_tickers}.

Args:
    firm: Firm name or fragment (e.g. 'Goldman' matches 'Goldman Sachs').
    sector: Optional lowercase sector (e.g. 'technology', 'healthcare').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
firmYes
sectorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -{
-  "properties": {
-    "result": {
-      "title": "Result",
-      "type": "string"
-    }
-  },
-  "required": [
-    "result"
-  ],
-  "title": "get_analysts_by_firmOutput",
-  "type": "object"
-}New value: +null
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds valuable behavioral details: case-insensitive substring matching, deduplication by analyst name keeping best rank, sort order, and the output shape change when sector is provided. This significantly enriches the agent's understanding.

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 well-structured, beginning with the core action, then output format, behavioral rules, and parameter details. It is information-dense without redundancy—every sentence provides necessary context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only two simple parameters and no output schema, the description is complete: it explains return row structures for both with/without sector, matching behavior, deduplication, and sorting. An agent can confidently invoke this tool without further clarification.

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

Parameters5/5

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

The input schema only provides names and defaults, but the description adds substantive meaning: firm is a case-insensitive substring (with example matching 'Goldman' to 'Goldman Sachs'), sector is optional lowercase and affects the result row shape. This fully compensates for the 0% schema description coverage.

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 states the tool's function: 'List analysts at a given firm' with specific output rows. It distinguishes from sibling tools by focusing on firm-based analyst listing, which is unique among the many get_* tools.

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 clearly implies when to use it—when you need analysts for a firm. It does not explicitly compare to alternatives like get_recent_analyst_ratings, but provides clear context and parameter guidance, satisfying the 'clear context, no exclusions' 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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