Skip to main content
Glama

aibvf-mcp

by io.github.Bahamas1717

map_to_taxonomy

Read-onlyIdempotent

Map everyday business language to the canonical AI BVF values required by the scoring tools. CALL THIS when the user says customer service, procurement, banking, GenAI copilot or bureaucratic and the matching enum is not certain. Pass only the fields written in free text; each returns the canonical value, what it matched on, or null with suggestions. A null result requires the user to choose from the suggestions, because a plausible guess would change the score. Use list_taxonomy when the user needs every permitted value, then pass the mapped values into score_initiative, diagnose_process, get_benchmark or the portfolio tools. Pure deterministic lookup, no network, auth, or side effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ai_tierNoEveryday AI language, e.g. RPA, GenAI copilot, autonomous agents. Resolved to gen1/gen2/gen3.
functionNoEveryday function language, e.g. customer service, procurement, legal, people. Resolved to cx, supply, risk, hr and so on.
industryNoEveryday industry language, e.g. banking, ecommerce, pharma. Resolved to the canonical enum.
readinessNoEveryday culture language, e.g. bureaucratic, cross-functional, hierarchical. Resolved to agile/traditional/siloed.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
ai_tierNo
functionNo
guidanceYes
industryNoinput, resolved and matched_on; or resolved null with suggestions when no confident match.
readinessNo
bvf_versionYes

TDQS

A4.8/5.0
Behavior5/5

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

Annotations declare readOnlyHint, idempotentHint, destructiveHint. Description adds 'Pure deterministic lookup, no network, auth, or side effects' and explains null result behavior. This enriches transparency beyond structured fields.

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 two paragraphs with a clear first sentence summarizing purpose. It is well-structured and front-loaded, though slightly verbose. Every sentence adds value, so it earns a 4.

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?

Given an output schema exists, the description appropriately focuses on usage behavior. It covers return value structure, null handling, and side-effect-free nature. Context is complete for this complexity level.

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 baseline is 3. The description adds value by explaining that parameters are free text and describing the return structure (canonical value, match, or null with suggestions). This goes beyond the schema descriptions.

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 clearly states the verb 'Map' and the resource 'everyday business language to the canonical AI BVF values'. It distinguishes from sibling tools like list_taxonomy and the scoring tools, making the purpose unambiguous.

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?

Explicitly tells when to call this tool ('when the user says... and the matching enum is not certain') and when to use list_taxonomy instead. Also directs to pass mapped values into specific tools, providing clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All aibvf-mcp toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.7/5.0
Disambiguation4/5

Each tool has a clear trigger and purpose, with descriptions explicitly cross-referencing when to use which. However, assess_ai_initiative, score_initiative, and score_portfolio all produce verdicts and could be confused without carefully reading the canonical-vs-conversational distinction.

Naming Consistency5/5

All 13 tools follow a consistent snake_case verb_noun pattern: assemble, assess, calculate, diagnose, get, infer, list, map, recommend, score, sequence, validate. No mixed conventions or vague verbs.

Tool Count5/5

13 tools is well within the ideal range for a domain of this complexity. Each tool covers a distinct stage of the AI investment workflow—taxonomy, assessment, scoring, portfolio, sequencing, diagnostics—without redundancy or bloat.

Completeness5/5

The tool surface covers the full lifecycle: mapping input language, assembling and validating portfolio documents, assessing and scoring initiatives, diagnosing processes, measuring readiness, calculating pace-layer drag, recommending improvements, and sequencing portfolios. No obvious dead ends or missing operations for the stated domain.

Resources