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Perception

by io.github.fernikolic

perception_get_intelligence_digest

Read-onlyIdempotent

Get the daily Intelligence Digest: a cross-signal briefing that fuses analyst actions, sentiment shifts, volume spikes, earnings events, regulatory mentions, and GitHub activity into one ranked summary.

WHEN TO USE:

  • "What's the intelligence digest for today?"

  • "What signals converged yesterday?"

  • "Which companies have the most activity right now?"

  • "Give me the daily cross-signal briefing"

  • Any request for a comprehensive daily summary that goes beyond just news or sentiment

HOW IT WORKS: Every day at 9:30 AM UTC, Perception scans 6 signal sources across all tracked entities and ranks them by signal convergence. Companies with 2+ simultaneous signals (e.g., analyst downgrade + sentiment drop + volume spike) surface to the top. The top 5 entities get an AI-synthesized narrative explaining why they matter today.

SIGNAL TYPES:

  • Analyst upgrades/downgrades (from Wall Street firms)

  • Sentiment shifts (sudden positive or negative swings vs 7-day baseline)

  • Volume spikes (3x+ normal mention volume)

  • Earnings events (recent transcript analysis available)

  • Regulatory mentions (SEC, CFTC, ECB, etc.)

  • GitHub activity spikes (major releases or PR activity)

BEST PRACTICES:

  • Use this as a starting point, then drill into specific entities with get_entity_profile or get_insider_activity

  • Compare with daily_radar (which focuses on narrative trends) for a complete picture

  • Signal convergence (multiple signals on one entity) is more meaningful than any single signal

PERSONALIZATION: Pass context parameter with portfolio details so Perception can highlight signals for companies the user holds or watches.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate to retrieve digest for (YYYY-MM-DD format). Defaults to today. Use yesterday's date for a complete digest (today's may still be generating).
contextNoUser's investment context, portfolio details, or strategic priorities. Pass relevant details so Perception can highlight the signals most relevant to them.

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark the tool as readOnly, idempotent, and non-destructive. The description adds substantive behavior: scans 6 signal sources daily at 9:30 AM UTC, ranks by signal convergence, and synthesizes narratives for top 5 entities. No contradiction with annotations.

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 long but well-organized with clear headers and front-loaded core definition. Example queries and signal lists add practical utility, though some redundancy could be trimmed.

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

Completeness4/5

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

For a tool with no output schema, the description adequately conveys what the result looks like: a ranked cross-signal summary with AI narratives for top entities. Date generation caveats and personalization behavior are covered. Slightly more detail on exact response fields would make it fully complete.

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?

Input-schema descriptions cover 100% of parameters with clear details about date format, default, and context semantics. The PERSONALIZATION paragraph reinforces the context parameter but adds no syntax or constraints beyond the schema, so the description does not need to compensate.

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?

States a specific verb and resource: 'Get the daily Intelligence Digest' and characterizes it as a cross-signal briefing fusing six signal types into one ranked summary. It also distinguishes itself from siblings by naming daily_radar and follow-up tools like get_entity_profile.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Includes a WHEN TO USE section with concrete example queries and explicit guidance to compare with daily_radar while drilling into specific entities with get_entity_profile or get_insider_activity. This makes the selection criteria clear.

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