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imbenrabi

Financial Modeling Prep MCP Server

getCOTAnalysis

Analyze Commitment of Traders (COT) reports to evaluate market sentiment, dynamics, and potential reversals across commodity sectors for specific date ranges.

Instructions

Gain in-depth insights into market sentiment with the FMP COT Report Analysis API. Analyze the Commitment of Traders (COT) reports for a specific date range to evaluate market dynamics, sentiment, and potential reversals across various sectors.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesCommodity symbol
fromNoOptional start date (YYYY-MM-DD)
toNoOptional end date (YYYY-MM-DD)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gains in-depth insights' and analyzes reports, implying a read-only operation, but doesn't clarify if it requires authentication, has rate limits, returns paginated data, or handles errors. For a tool with no annotation coverage, this is a significant gap in transparency.

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?

The description is two sentences but includes marketing fluff ('Gain in-depth insights into market sentiment') that doesn't add operational value. It could be more front-loaded with core functionality. While not verbose, some content is redundant or promotional rather than informative.

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

Completeness2/5

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

Given no annotations, no output schema, and a tool with three parameters, the description is incomplete. It lacks details on behavioral traits (e.g., authentication, rate limits), output format, and usage guidelines. For a financial data tool with potential complexity, this leaves significant gaps for an agent to operate effectively.

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 description coverage is 100%, so the schema already documents all parameters (symbol, from, to). The description adds minimal value beyond the schema by implying date-range analysis ('specific date range') but doesn't provide additional context like format examples or constraints. This meets the baseline for high schema coverage.

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 clearly states the tool's purpose: 'Analyze the Commitment of Traders (COT) reports for a specific date range to evaluate market dynamics, sentiment, and potential reversals across various sectors.' It specifies the verb ('analyze'), resource ('COT reports'), and scope ('specific date range'), though it doesn't explicitly differentiate from sibling tools like getCOTList or getCOTReports, which likely serve different purposes (e.g., listing vs. analyzing).

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?

The description provides no explicit guidance on when to use this tool versus alternatives. It mentions analyzing COT reports but doesn't specify prerequisites, constraints, or when to choose it over sibling tools like getCOTList or getCOTReports. The lack of usage context leaves the agent without clear direction for tool selection.

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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