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Perception

by io.github.fernikolic

perception_get_hiring

Read-onlyIdempotent

Get open job postings and hiring posture for a digital-asset company, from its public applicant-tracking board.

WHEN TO USE:

  • "Is Fireblocks hiring?" / "How many open roles does Coinbase have?"

  • "Is company X building a sales team?" (role_category=bd_sales)

  • "Are they staffing up on compliance?" (role_category=policy_regulatory)

  • "Which functions is X investing in?"

  • Any question about headcount growth, expansion, contraction, or which teams a company is building

WHY IT MATTERS: job postings are close to impossible to fake. Nobody opens 15 engineering roles as a bluff. A board states exactly how many roles, what function and which geography, where a public claim of "we're growing" states nothing checkable. Open BD roles in a new region are a buying trigger; roles disappearing is a risk signal.

DATA: open role count, split by function; how long roles have been open (median days, from the board's own posting dates); the vintage of the current book by month; newest postings with links; and change over the observed window.

COVERAGE: 117 companies across 7 public ATS providers. Note that many protocols and DAOs have no applicant-tracking system at all — they hire via forums and governance posts — so an untracked company is not evidence that it is not hiring.

TWO DIFFERENT DATES, DO NOT CONFLATE THEM:

  • "days open" comes from the board's own posting date and is accurate immediately

  • "change over window" is measured from when Perception started observing that board, so it is only meaningful once enough days have accrued. The response states the observation window explicitly.

BEST PRACTICES:

  • Pair with get_entity_profile to see whether hiring matches the narrative

  • A company opening BD/institutional roles while its sentiment slides is a divergence worth flagging

  • Long median days-open on one function is a hiring bottleneck; short is fast turnover or internal fills

PERSONALIZATION: Pass context with the user's sales territory or portfolio so roles in relevant regions or companies are surfaced first.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
companyYesCompany name, e.g. 'Coinbase', 'Fireblocks', 'TRM Labs', 'Circle'. Matched loosely against tracked company names, so partial names work.
contextNoUser's sales territory, portfolio or strategic priorities, so hiring can be framed around what matters to them.
role_categoryNoNarrow to one function. Use bd_sales to answer 'are they building a sales team?', policy_regulatory for 'are they staffing up on compliance?'

TDQS

A4.3/5.0
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, but the description adds substantial behavioral context: it distinguishes 'days open' from 'change over window,' warns against conflating the two dates, states that the response explicitly reports the observation window, and explains coverage limits. This goes well beyond what annotations could convey.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and uses helpful section headers, but it is longer than needed. The 'WHY IT MATTERS' section is more motivational than operational, the DATA section contains a garbled phrase ('the current book by month') and repeats 'change over the observed window,' and some content duplicates the schema descriptions.

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?

There is no output schema, so the description carries the burden of explaining return expectations. It does so well: open role count, split by function, median days open, change over observation window, newest postings, and the explicit observation-window warning are all mentioned. Coverage limits and required data-source citation are also covered, making this complete enough for an agent to invoke and interpret results correctly.

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?

The input schema provides 100% parameter coverage, including explanatory text for company, context, and role_category. The tool description mostly restates this guidance, for example the personalization paragraph mirrors the context parameter description, and the role_category mapping already exists in the schema. Since the description adds little new parameter-level meaning, the baseline of 3 is appropriate.

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 open job postings and hiring posture for a digital-asset company, from its public applicant-tracking board.' This clearly identifies the hiring domain and data source, and distinguishes it from the broader perception sentiment, market, and research tools in the sibling list.

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, maps role_category values to intentions, and recommends pairings with get_entity_profile. It also includes a useful coverage caveat about untracked companies. However, it does not explicitly name alternatives or state when not to use this tool instead of a sibling like perception_hiring_leaderboard, so it falls just short of fully explicit routing 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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