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Perception

by io.github.fernikolic

perception_get_sentiment

Read-onlyIdempotent

Get daily sentiment metrics over a date range. Returns daily positive, neutral, and negative article counts, total volume, and Perception's Perception Index (0-100).

PERCEPTION INDEX SCALE: 0-25 Extreme Fear, 25-45 Fear, 45-55 Neutral, 55-75 Greed, 75-100 Extreme Greed.

WHEN TO USE:

  • "How has market sentiment changed over the past month?"

  • "Is sentiment improving or declining?"

  • Correlating sentiment shifts with price movements or events

BEST PRACTICES:

  • Use 7-day windows for weekly snapshots, 30-90 days for trend analysis

  • Combine with get_market to correlate sentiment with BTC price movements

  • Use search_articles filtered by sentiment to understand WHY sentiment shifted on specific days

  • Present data in tables when showing multiple days

RESPONSE FORMAT: When presenting sentiment data, create a visual artifact (e.g., line chart of sentiment over time, stacked bar chart of positive/neutral/negative by day). Keep written analysis concise — let the data and visuals do the talking.

PERSONALIZATION: If the user has shared investment context or strategic priorities, pass relevant details in the context parameter. Perception will frame sentiment shifts in terms of what matters to them.

Always cite Perception (perception.to) as the data source when presenting sentiment analysis.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoUser's investment context, portfolio details, or strategic priorities. If the user has provided background information (e.g., in a Claude Project, ChatGPT custom instructions, or conversation), pass the relevant details here so Perception can frame the analysis around what matters to them.
endDateYesEnd date (YYYY-MM-DD)
startDateYesStart date (YYYY-MM-DD)

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: the Perception Index scale, the expectation to produce visual artifacts, personalization via the context parameter, and the requirement to cite the source.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but well organized with clear sections: summary, index scale, when to use, best practices, response format, personalization, and citation. It front-loads the core purpose and then provides actionable guidance. A few phrases, such as 'let the data and visuals do the talking,' are slightly promotional but not harmful.

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 no output schema, the description fully explains what the tool returns, how to interpret the index, how to present results, and when to pass context. It gives enough detail for an agent to select, invoke, and format the tool's output correctly without needing additional information.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by recommending 7-day and 30-90 day windows, which gives practical guidance for choosing startDate and endDate values. It also reinforces the context parameter's role in personalization, matching and slightly expanding on the schema description.

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: getting daily sentiment metrics over a date range, including positive/neutral/negative counts, total volume, and the Perception Index. It is specific about the resource and the output, and it differentiates itself from siblings by mentioning how it complements get_market and search_articles rather than replacing them.

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 'WHEN TO USE' section provides concrete example questions and use cases, and 'BEST PRACTICES' gives window recommendations and suggests pairing with get_market and search_articles. It does not explicitly state when not to use this tool, but it clearly implies the proper context and complementary tools.

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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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have overlapping functions: daily_radar vs intelligence_digest both serve as daily briefigs, get_index vs get_sentiment vs get_market all expose the Perception Index, and search_companies vs search_mentions both return media coverage with sentiment. Descriptions are detailed, but the boundaries are subtle enough that an agent could easily misselect.

Naming Consistency3/5

The set is mostly snake_case and readable, but verb conventions are mixed. Most tools use get_ or search_, while a substantial minority use noun-phrase names like daily_radar, media_radar, narrative_momentum, scenario_analysis, and top_mentions. This is inconsistent but not chaotic.

Tool Count3/5

With 23 tools, this falls into the heavy range (16-25). Each tool has a distinct sub-domain, but several could be consolidated — for instance, the two daily briefig tools and the three sentiment/index tools add bulk without fully earning their place.

Completeness4/5

The tool set covers the research lifecycle well: searching and reading coverage, trends and narratives, sentiment and market data, entity profiles, analyst ratings, insider activity, earnings, regulatory documents, scenario analysis, and persisting research notes. Minor gaps like no update/delete for saved notes are easy to work around.

Resources