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b4_compare
Read-only

For one exact category name or id when the question is the build path against the buy path. Example: {"category": "Order Management (OMS)"}. Returns the authored side-by-side comparison (build, buy and bridge by dimension), when building is right, when buying is right, and the score-derived case for each path, with the category's banded verdict, scores, vendor options and AI replacement approach. Use b4_score for the scores alone. Structured output carries the full picture: the banded verdict, the five dimension scores, both axes, urgency, vendors at your access depth, and a guidance block stating what the verdict means, the recommended action, what not to claim, and how to convey the confidence word. Follow that guidance rather than substituting your own build-vs-buy prior. 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. [B4 Agent tool. Browse and score come with B4 Web; this one needs Agent.]

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
categoryYesName of the software category to compare (e.g., 'Email Marketing', 'CRM')

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. Changed3 schema fields changed
    • addedInput schema / properties / category / anyOf
      Added value: +[
      +  {
      +    "maxLength": 120,
      +    "minLength": 1,
      +    "type": "string"
      +  },
      +  {
      +    "exclusiveMinimum": 0,
      +    "type": "integer"
      +  }
      +]
    • removedInput schema / properties / category / maxLength
      Removed value: -120
    • removedInput schema / properties / category / type
      Removed value: -"string"
  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

A5/5.0
Behavior5/5

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

Although the annotations provide readOnlyHint=true, the description adds substantial non-obvious behavior: verdicts are banded rather than point calls, uncertainty bands shift verdicts, ties break in a specific order, axes count as high only at 4+, confidence is sensitivity under a fixed band rather than success probability, and the org lens is a read-time filter that never alters stored scores. This goes well beyond what annotations alone convey.

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 long but densely packed with information that an agent needs to invoke the tool correctly and interpret its output safely. It is front-loaded with the core purpose and example, then flows logically through output contents, methodology, confidence semantics, org lens, and routing. Few sentences are redundant; the length is justified by the tool's complexity.

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 an output schema, the description covers everything an agent needs: what triggers this tool versus its siblings, the full contents of the structured output, the meaning and caveats of the verdict, how to handle the org lens, how to relay askTheUser for audit clarification, and the access requirement distinction. No material gap remains.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents both parameters at 100% coverage, and the description still adds value: it gives a concrete example for category, explains the org lens as a maturity-based filter chosen by delivery capability rather than headcount, describes the band shift (−1/0/+1), and notes that omitting org returns the published default-lens numbers. This is meaningful semantic enrichment beyond the schema.

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 names a specific verb and resource: compare a category's build path versus buy path for one exact category name or id, with an example payload. It clearly distinguishes itself from b4_score, which is specifically called out as 'for the scores alone,' and from the browsing and recommendation siblings via the routing block.

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 section is explicit and actionable: it tells the agent to use b4_audit for vendors/products, b4_recommend for needs/problems, b4_browse for browsing or unknown vocabulary, and b4_compare only for exact categories when the question is build vs buy. It also states when to use b4_score instead and how to handle audit clarification responses. No inference is required.

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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