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blockchainacademics

@blockchainacademics/mcp

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get_sentiment

Retrieve editorial sentiment buckets (bullish, bearish, neutral, mixed) for a given entity, including drivers behind each sentiment classification.

Instructions

BCA editorial sentiment bucket (bullish/bearish/neutral/mixed) for an entity with bucket drivers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
windowNoRolling window.
entity_slugYesEntity slug.
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions 'with bucket drivers' but does not state that the tool is read-only, whether authentication is needed, what happens if the entity is not found, or any rate limits. The description is insufficient for an agent to understand the tool's impact and side effects.

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 a single sentence that efficiently conveys the tool's core function without redundancy. Every word is necessary and relevant.

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?

The tool has two parameters, no output schema, and moderate complexity. The description provides the output type (sentiment bucket) but omits the structure (e.g., whether it returns a string or an object with drivers). Given the lack of output schema, additional detail on the return format would improve completeness.

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 both parameters are documented. The description adds value by explaining that the output includes 'bucket drivers' and that the tool is for an 'entity,' but it does not elaborate on the 'window' parameter (e.g., allowed values, meaning). The baseline is 3, and the description offers marginal additional context.

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 specifically states it returns a 'BCA editorial sentiment bucket (bullish/bearish/neutral/mixed) for an entity with bucket drivers,' clearly identifying the verb (get), resource (entity), and the nature of the output (sentiment bucket). This distinguishes it from sibling tools like 'get_fear_greed' (market-wide) and 'get_sentiment_velocity' (change in sentiment).

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?

No explicit guidance on when to use this tool versus alternatives. For example, it does not mention that 'get_fear_greed' is for market-level sentiment or that 'get_sentiment_velocity' is for trend analysis. The description implies it's for entity-specific sentiment but fails to clarify exclusions or preferred use cases.

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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