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Browse the B4 Index

b4_browse
Read-only

For browsing, filtering, or finding the index's name for something. Example: {"query": "warehouse picking", "domain": "Supply Chain"}. Search and filter the B4 Index's 1,600+ independently scored software categories. Browse by keyword, domain, quadrant, or industry. When filtering by industry, returns all vertical categories for that industry PLUS all horizontal categories (which apply to every industry). Each row carries its banded verdict — primary, confidence word, and a near-call flag — and the quadrant filter matches the verdict at whichever lens you are reading. Verdicts are banded (B4 methodology v4.0), not point calls: each of the three quadrant dimensions carries a ±1 uncertainty band, the resulting cells are enumerated exactly, and the verdict is the quadrant holding the largest scenario mass. Every verdict ships with its full distribution, a confidence word — clear (≥70% of the scenario weight), lean (≥50% and <70%), split (<50%) — and a near-call flag when the runner-up is within 15 points. An axis counts as high only when it clears the 3.5 line strictly, which on this 1–5 grid means only at 4 or above, so a category sitting exactly on the line gets the safer call: ties break in the order BUY → BRIDGE → BEWARE → BUILD, cheapest mistake first. Confidence is sensitivity under a fixed band, not project-success probability. Optional org lens: set org to "small", "medium" (the default) or "large" to read the same scores as a team of that engineering maturity — it shifts the center of the AI-feasibility band by −1 / 0 / +1 and nothing else. The lens is a filter the caller looks through, never a stored profile. Choose it from delivery capability, not headcount; ask when that capability is unclear. The raw scores themselves never change. Omit it and you get the default-lens numbers, which are the ones published on logged-out surfaces. Routing: a vendor or product name → b4_audit (one or many; add a short description of what it does for anything the index may not know); a need or problem in words → b4_recommend; an exact category name or id → b4_score (one category) or b4_compare (build vs buy paths); browsing, filtering, or an unknown vocabulary → b4_browse. When an audit row returns clarification.needed, relay askTheUser to the user and re-run that row with the answer as its description. [Needs a B4 plan: browse and score come with B4 Web.]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orgNoOrg-maturity lens: "small" (no dedicated engineering), "medium" (default — some AI capability), "large" (AI-mature). Shifts the AI-feasibility band center by −1/0/+1 at read time. A filter the caller looks through, never a stored profile.medium
limitNoMax results to return (default 20, max 100)
queryNoSearch term to match against category names, vendors, domains, and rationales
cursorNoOpaque continuation from the previous page. Keep filters and lens unchanged; restart on SNAPSHOT_EXPIRED.
domainNoFilter by domain (e.g., 'Marketing Technology', 'CRM & Sales')
industryNoFilter by industry group. Returns matching vertical categories + all horizontal categories. Options: Healthcare, Financial Services, Construction & Real Estate, Education, Energy & Utilities, Government, Automotive, Agriculture, Transportation & Logistics, Media & Entertainment, Legal, Professional Services, Nonprofits & Associations, Manufacturing, Retail & Commerce, Hospitality & Food Service, Telecom
quadrantNoFilter by quadrant
includeVendorsNoExpand vendor lists; omitted by default for bounded pages.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
axesNostrategicDifferentiation (X) and aiFeasibility (Y)
lensNo
domainNo
scoresNothe five v3.2 dimension scores, 1-5
urgencyNo
vendorsNo
verdictNobanded verdict: primary, confidence, nearCall, distribution
categoryNo
evidenceNosources and research trail; present only with includeEvidence
guidanceNohow to use this result: meaning, action, what not to claim, confidence handling
resolvedYesfalse when nothing matched; the payload then carries no verdict
rationaleNo
vendorCountNo
evidenceHintNo
aiReplacementNo
vendorsWithheldNo
evidenceAvailableNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changed
    • addedInput schema / properties / cursor
      Added value: +{
      +  "description": "Opaque continuation from the previous page. Keep filters and lens unchanged; restart on SNAPSHOT_EXPIRED.",
      +  "maxLength": 2000,
      +  "minLength": 1,
      +  "type": "string"
      +}
    • addedInput schema / properties / domain / minLength
      Added value: +1
    • addedInput schema / properties / includeVendors
      Added value: +{
      +  "default": false,
      +  "description": "Expand vendor lists; omitted by default for bounded pages.",
      +  "type": "boolean"
      +}
    • addedInput schema / properties / industry / minLength
      Added value: +1
    • addedInput schema / properties / query / minLength
      Added value: +1
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "$schema": "http://json-schema.org/draft-07/schema#",
      +  "additionalProperties": true,
      +  "properties": {
      +    "aiReplacement": {},
      +    "axes": {
      +      "description": "strategicDifferentiation (X) and aiFeasibility (Y)"
      +    },
      +    "category": {
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "domain": {
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "evidence": {
      +      "description": "sources and research trail; present only with includeEvidence"
      +    },
      +    "evidenceAvailable": {
      +      "type": "boolean"
      +    },
      +    "evidenceHint": {
      +      "type": "string"
      +    },
      +    "guidance": {
      +      "description": "how to use this result: meaning, action, what not to claim, confidence handling"
      +    },
      +    "lens": {
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "rationale": {
      +      "type": [
      +        "string",
      +        "null"
      +      ]
      +    },
      +    "resolved": {
      +      "description": "false when nothing matched; the payload then carries no verdict",
      +      "type": "boolean"
      +    },
      +    "scores": {
      +      "description": "the five v3.2 dimension scores, 1-5"
      +    },
      +    "urgency": {},
      +    "vendorCount": {
      +      "type": "number"
      +    },
      +    "vendors": {},
      +    "vendorsWithheld": {
      +      "type": "number"
      +    },
      +    "verdict": {
      +      "description": "banded verdict: primary, confidence, nearCall, distribution"
      +    }
      +  },
      +  "required": [
      +    "resolved"
      +  ],
      +  "type": "object"
      +}
  3. Changed1 schema field changed
    • addedInput schema / properties / org
      Added value: +{
      +  "default": "medium",
      +  "description": "Org-maturity lens: \"small\" (no dedicated engineering), \"medium\" (default — some AI capability), \"large\" (AI-mature). Shifts the AI-feasibility band center by −1/0/+1 at read time. A filter the caller looks through, never a stored profile.",
      +  "enum": [
      +    "small",
      +    "medium",
      +    "large"
      +  ],
      +  "type": "string"
      +}
  4. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Even though annotations already mark this as read-only, the description adds substantial behavioral context: industry filters return vertical plus horizontal categories, verdicts are banded rather than point calls, ties break in a defined order, the org lens shifts reading but never changes raw scores, and the lens is never stored. This goes well beyond the annotation signals.

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 it is front-loaded with the core purpose and an example, then organized into capability, methodology, lens, routing, and plan sections. The dense verdict-methodology paragraph is relevant for correct interpretation, though it could be tightened without losing essential meaning.

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 tool with 8 parameters, an output schema, and complex verdict semantics, the description is remarkably complete. It covers the filtering behavior, the banding methodology, the org-lens effect, sibling routing, contingency handling (clarification.needed), and licensing context. Nothing an agent needs to invoke it correctly is missing.

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 schema already handles most parameter documentation. The description nonetheless adds real meaning beyond the schema for industry (returns vertical + horizontal categories) and quadrant (matches the verdict at whichever lens is active), while clarifying the org lens semantics and default behavior. Not every parameter receives extra treatment, but the ones that need nuance are covered.

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 purpose — 'browsing, filtering, or finding the index's name for something' — and supports it with a concrete example. It also differentiates b4_browse from its siblings by explicitly naming when not to use it, so an agent can disambiguate before opening any schemas.

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 Routing paragraph is explicit and actionable: vendor/product names go to b4_audit, worded needs go to b4_recommend, exact category names go to b4_score/b4_compare, and browsing/filtering/unknown vocabulary goes to b4_browse. It also gives guidance on choosing the org lens and notes the B4 plan requirement.

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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