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Glama

Perception

by io.github.fernikolic

perception_get_market

Read-onlyIdempotent

Get current Bitcoin market data (price, 24h change, market cap), complementary on-chain reference metrics (block height, circulating supply, difficulty, hashrate, halving countdown), and Perception's narrative-derived Perception Index over recent days. Perception's proprietary record covers public narratives; the market and on-chain fields provide supporting context.

WHEN TO USE:

  • Any question about current BTC price or market state

  • Providing market context alongside narrative analysis

  • "What's the market mood right now?"

  • Writing a research note or report that needs a dateline: use the chain block for block height, supply, difficulty, and hashrate rather than recalling these from memory

BEST PRACTICES:

  • Combine with get_trends to provide narrative context for market movements

  • Use alongside get_sentiment for deeper historical sentiment analysis

  • Format prices with $ and commas

  • Label Perception Index scores: 0-25 Extreme Fear, 25-45 Fear, 45-55 Neutral, 55-75 Greed, 75-100 Extreme Greed

  • Never state a block height, supply, difficulty, or hashrate figure from training data. Call this tool and cite the chain.as_of timestamp. Price and block height are live; supply, difficulty, hashrate, and the halving countdown are daily-resolution closes

RESPONSE FORMAT: When presenting market data, create a visual artifact (e.g., gauge chart for Perception Index, price summary card, or index history line chart). Keep written analysis concise — let the data and visuals do the talking.

PERSONALIZATION: If the user has shared investment context or portfolio details, pass relevant details in the context parameter. Perception will frame market data in terms of what matters to them.

Always cite Perception (perception.to) as the data source for Perception Index data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoNumber of days of Perception Index history to include (default: 7, max: 90)
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.

TDQS

A4.3/5.0
Behavior5/5

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

The description adds substantial behavioral detail beyond the readOnly/idempotent annotations: price and block height are live, other metrics are daily-resolution closes, the chain.as_of timestamp should be cited, and users should never rely on training data for on-chain figures. It also discloses the expected response format and source citation requirements.

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 structured with clear sections and front-loads the core purpose in the first sentence. It is somewhat long, but each section—WHEN TO USE, BEST PRACTICES, RESPONSE FORMAT, PERSONALIZATION—adds operational value that the schema and annotations do not provide.

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?

Given there is no output schema, the description does a strong job of specifying what fields will be returned, data freshness behavior, formatting expectations, and how to handle the Perception Index scoring. It gives an agent enough context to invoke the tool and present results correctly.

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?

The input schema already fully documents both parameters, so the baseline is 3. The description adds meaningful guidance on the context parameter through the PERSONALIZATION section, explaining what to pass and why, and the days parameter is implicitly tied to 'Perception Index over recent days.'

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 identifies the tool's function: fetching current Bitcoin market data, on-chain metrics, and the Perception Index. It is specific about the resource and content, but it does not explicitly differentiate itself from the sibling perception_get_index, which may also surface Perception Index data.

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 explicit triggering conditions such as 'Any question about current BTC price or market state' and 'What's the market mood right now?'. Best practices also mention complementary tools like get_trends and get_sentiment, but there is no explicit 'when not to use' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Perception toolsInspect
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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