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Perception

by io.github.fernikolic

perception_get_entity_profile

Read-onlyIdempotent

Get a complete intelligence profile for any entity in the digital asset space — companies, people, or organizations. Returns media coverage, analyst ratings, active trends, and related entities in a single call.

WHEN TO USE:

  • "Tell me about MicroStrategy" or "What's happening with Coinbase?"

  • "Give me the full picture on MARA"

  • "What do we know about Michael Saylor?"

  • Any question asking for a comprehensive overview of a specific company, person, or organization

SUPPORTED ENTITIES: ~100 companies (COIN, MSTR, MARA, BlackRock, Binance, etc.), 15+ key people (Michael Saylor, Larry Fink, Gary Gensler, etc.)

BEST PRACTICES:

  • Use the canonical name, ticker, or entity ID

  • Combine with search_articles for deeper coverage analysis

  • Use get_analyst_ratings for more detailed analyst data

  • For broad market questions, use get_trends instead

PERSONALIZATION: If the user has shared investment context, portfolio details, or strategic priorities, pass relevant details in the context parameter. Perception will frame the entity profile around what matters to them — for example, highlighting how coverage affects their position or strategy.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesEntity identifier — can be a canonical ID (e.g., 'strategy'), ticker (e.g., 'MSTR'), or name (e.g., 'Strategy'). Supports ~100 companies, 15+ key people.
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.
date_rangeNoTime window for mentions and trends: '7d' (default), '30d', or '90d'7d

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, so the description improves on that by adding the supported-entity scope, the requirement to cite Perception (perception.to), and the personalization behavior driven by the context parameter. It does not cover failure modes for unsupported entities, but the disclosed scope helps an agent know when not to call it.

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-structured into labelled sections, with the overview front-loaded before examples and alternatives. Each section adds practical value, though the PERSONALIZATION section partly restates the context parameter description and 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?

There is no output schema, so the description carries the burden of describing return value shape; it names the four result categories (media coverage, analyst ratings, active trends, related entities). It also provides supported-entity scope, date-range semantics via schema, and routing guidance, leaving only edge cases like unsupported-entity errors undocumented.

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 actionable parameter guidance by recommending canonical names, tickers, or entity IDs and illustrating valid forms ('strategy', 'MSTR', 'Strategy'). It also explains how the context parameter changes behavior, which the schema only partially conveys.

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 opens with a specific verb and resource — 'Get a complete intelligence profile' — and names the exact content returned: media coverage, analyst ratings, active trends, and related entities. It also gives concrete example queries and supported entities, making it easy to distinguish from sibling tools like get_trends or get_analyst_ratings.

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?

The 'WHEN TO USE' section maps natural-language user requests to this tool with concrete examples. 'BEST PRACTICES' explicitly names alternatives, including 'Use get_analyst_ratings for more detailed analyst data' and 'For broad market questions, use get_trends instead.'

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