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Perception

by io.github.fernikolic

perception_hiring_leaderboard

Read-onlyIdempotent

Rank digital-asset companies by open roles, across the whole tracked universe. Filter by sector and by function.

WHEN TO USE:

  • "Who is hiring hardest in custody right now?"

  • "Which crypto companies are building sales teams?" (role_category=bd_sales)

  • "Who is staffing up on compliance?" (role_category=policy_regulatory)

  • "Which exchanges are growing?" (sector=exchange)

  • Building a prospect list, sizing a market, or finding expansion signals across companies rather than for one name

WHY IT MATTERS: this is the cross-company view that no public source assembles. Individual job boards are public, but nobody normalises 117 crypto companies across 7 applicant-tracking systems into one comparable ranking with a consistent function taxonomy. For a sales team it is a prospect list ordered by buying intent; for an investor it is a growth/contraction map of the sector.

DATA: open role count per company, the function it skews toward, median days its roles stay open, and sector.

COVERAGE: 117 companies, 7 ATS providers. Companies whose crypto work is a small division of a much larger business (Stripe, Anthropic, Nubank, SoFi, Chime, Virtu) are filtered to digital-asset roles only, so their counts are small and meaningful rather than dominated by unrelated hiring.

CAVEAT WORTH PASSING ON: absence is not evidence. Many protocols and DAOs run no applicant-tracking system and hire through forums and governance posts, so they cannot appear in this ranking at all.

BEST PRACTICES:

  • role_category=bd_sales is the single strongest buying-intent filter

  • Cross-reference the leaders with get_entity_profile or search_companies: a company hiring aggressively into falling sentiment is the interesting case

  • Median days open separates fast-moving teams from stalled requisitions

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many companies to return (default 20, max 50).
sectorNoRestrict to one sector, e.g. 'exchange', 'custody', 'miner', 'defi', 'protocol', 'payments', 'infra', 'analytics', 'stablecoin', 'market-maker'.
contextNoUser's territory, ICP or portfolio, so the ranking can be framed around it.
role_categoryNoRank by hiring in one function only. bd_sales answers 'who is building a sales team right now?', policy_regulatory answers 'who is staffing up on compliance?'

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and idempotent, and the description adds substantial behavioral context beyond that: it explains that companies with small crypto divisions are filtered to digital-asset roles only, that absence is not evidence because many protocols/DAOs use no ATS, and that median days open is a meaningful signal. These caveats materially shape how an agent interprets results and avoid overclaiming.

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 longer than typical, but every section earns its place: purpose, when to use, why it matters, data caveats, best practices, and attribution. The key message is front-loaded in the first sentence, and headers make the content easy to scan. No filler or repetition is present.

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 no-output-schema tool with four optional parameters, the description is remarkably complete: it explains what the ranking represents, the universe covered, filtering limits, data normalization, caveats about missing companies, how to interpret metrics like median days open, and how to combine this tool with siblings. An agent has enough context to invoke it correctly and interpret results cautiously.

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 schema already provides 100% parameter coverage, so the baseline is 3. The description adds operational meaning beyond the schema, particularly for role_category: it identifies bd_sales as the strongest buying-intent filter and connects policy_regulatory to compliance staffing. It also explains how sector/function filters relate to the ranking's purpose, which helps an agent choose parameters more intelligently.

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 opening sentence states a specific verb and resource: rank digital-asset companies by open roles across the whole tracked universe, with filtering by sector and function. This clearly distinguishes it from single-company tools like perception_get_entity_profile or perception_get_hiring by emphasizing the cross-company view. The scope is explicit and actionable.

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 provides concrete example queries and conditions, such as 'Who is hiring hardest in custody right now?' and building prospect lists. It also explicitly contrasts this cross-company ranking with per-company views and recommends cross-referencing with get_entity_profile or search_companies, giving clear routing and alternative 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.

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