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AI-first company: evidence on AI adoption

Open one part of the evidence map

open
Read-onlyIdempotent

Open one part of the evidence map: a list (practices, cases, studies, figures, claim-checks, assessments) with filters such as theme, outcome, industry, company size, stage, priority, figure group (line) or assessment kind. Returns every matching row in one line with its id, or, when there are more than 25, a menu splitting them by the next filter, with counts. Use the arguments the menus show. Then fetch the rows you pick with get_evidence.

Input Schema

NameRequiredDescriptionDefault
kindNoFigures: the kind of number, e.g. measured-in-a-study or self-reported. Assessments: the kind key from start, e.g. readiness-surveys.
lineNoFigures only: the group key from start, e.g. time-freed or running-cost.
listYesWhich list: practices, cases, studies, figures, claim-checks or assessments.
sizeNoCompany size band: micro-small, medium, mid-market, enterprise (1–49, 50–249, 250–999, 1,000+ employees). Or pass employees instead.
taskNoCases only: the kind of work AI was used for, a task key from start, e.g. customer-service-and-support or legal-research-drafting-and-filings.
stageNoHow far the company is: explore, adopt, redesign or run.
themeNoA theme key from start, e.g. governance-and-risk or people-and-skills.
outcomeNoCases with this outcome: measured, company-reported, went-wrong or walked-back.
industryNoCases in this industry: professional-services, agencies, ecommerce-retail, manufacturing, or other (none of the four, e.g. banks, airlines, public bodies). Practices apply to all industries.
priorityNoPractices with this priority.
employeesNoNumber of employees, e.g. 120.
free_onlyNoAssessments only: free to use.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare this as a safe, idempotent read. The description adds real behavioral detail beyond them: a full row set with ids when matches are 25 or fewer, otherwise a menu split by the next filter with counts. That pagination/splitting behavior is exactly what an agent needs and is not in the schema.

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?

Three tightly packed sentences with the outcome-relevant behavior front-loaded and the next step last. The parenthetical filter list is dense but every clause carries information; no filler sentences.

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 12-parameter tool with no output schema, the description still covers what the response looks like (one row per line with id, or a capped menu with counts) and what to do next. Nothing needed for correct invocation is absent, other than the sibling-overlap issue noted elsewhere.

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?

Schema description coverage is 100% and five parameters are enums with inline descriptions, so the schema does most of the work. The description's paraphrase of filter families (theme, outcome, industry, size, stage, priority, line, kind) adds little; it does not, for example, clarify which filters are valid for which list.

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?

States a concrete verb and resource ('Open one part of the evidence map') and enumerates the six selectable lists behind the required `list` argument. The scope is clear, but the description never distinguishes this tool from sibling lookups such as find_practices, find_company_cases and find_readiness_assessment, which appear to cover the same lists.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a workflow hint — 'Use the arguments the menus show' and 'Then fetch the rows you pick with get_evidence' — which is useful sequencing. However it never says when to pick `open` versus the find_* siblings, which is the main selection decision an agent faces here.

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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