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Perception

by io.github.fernikolic

perception_get_analyst_ratings

Read-onlyIdempotent

Get Wall Street analyst ratings, price targets, and recent upgrades/downgrades for a crypto-related public company.

WHEN TO USE:

  • "What do analysts think about Coinbase?"

  • "What's the price target for MSTR?"

  • "Any recent upgrades or downgrades for MARA?"

  • "Show me the analyst consensus on RIOT"

  • Any question about sell-side research, analyst recommendations, or price targets for Bitcoin/crypto stocks

COVERAGE: 70 US-listed digital asset companies — miners (MARA, RIOT, CLSK, HUT), exchanges (COIN), Bitcoin treasury (MSTR, TSLA, GME), fintech (HOOD, XYZ, MELI), and more.

DATA: Consensus ratings (Strong Buy/Buy/Hold/Sell/Strong Sell counts), price targets (high/low/mean/median), and individual firm actions (Goldman Sachs, JP Morgan, etc.) with dates.

BEST PRACTICES:

  • Combine with search_companies to see how media coverage aligns with analyst sentiment

  • Use alongside get_market for full market context

  • Always mention the number of analysts covering the stock for credibility

  • Cite specific firms and their ratings when available

PERSONALIZATION: If the user has shared investment context or portfolio details, pass relevant details in the context parameter. Perception will frame analyst data in terms of what matters to them — for example, how ratings compare to their current positions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock ticker symbol to look up analyst ratings for (e.g., COIN, MSTR, MARA, TSLA, HOOD). Must be a US-listed ticker.
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.
include_actionsNoInclude recent individual analyst upgrades/downgrades/initiations (default: true)

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior, so the description's burden is lower. It adds useful context beyond annotations: the coverage universe of 70 digital asset companies, the data fields returned, and the personalization behavior of the context parameter. It does not mention rate limits or exact response format, but the tool is simple and no output schema exists.

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 well-organized into front-loaded sections: WHEN TO USE, COVERAGE, DATA, BEST PRACTICES, and PERSONALIZATION. Every section contributes actionable guidance, and the example queries make the tool's intent immediately understandable without padding.

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?

For a read-only tool with three parameters and no output schema, the description provides everything an agent needs: purpose, use cases, coverage, returned data, best practices, and personalization guidance. Nothing essential is missing for correct selection and invocation.

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?

All three parameters are fully documented in the input schema (100% coverage), so the baseline is a 3. The PERSONALIZATION section reinforces the purpose of the context parameter, but it does not add materially new parameter semantics beyond what the schema already explains.

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 names a specific operation — getting Wall Street analyst ratings, price targets, and upgrades/downgrades — and limits scope to crypto-related public companies. It is clearly distinct from sibling topics like sentiment, insider activity, and earnings, though it never explicitly names a sibling to differentiate against.

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 queries and the BEST PRACTICES section recommends pairing with search_companies and get_market. It gives clear context for when to use the tool, but it does not state exclusions or direct users to alternative tools for different scenarios.

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.

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