AI Design Blueprint

Summarize Team Usage

team.summarize

Read-onlyIdempotent

Pro/Teams — summarises the caller's tool-usage patterns and value signals over a configurable window (default 30 days). Returns tool_call_counts, top principles cited in validate runs, value_event_counts by event_type, and an aggregate readiness trend. WHEN TO CALL: the user asks 'how is the Blueprint helping me/my team', 'what should I explore next', or 'show me my Blueprint usage'. WHEN NOT TO CALL: proactively or on every conversation turn (the summary is an explicit retrospective, not telemetry); to compare users (returns only the caller's own data). BEHAVIOR: read-only, idempotent over the same window. Aggregates from AIToolCallLog + ValueEvent + AIValidationRunLog. Pass private_session=true to bypass server-side logging for this summary call (the underlying historical data still exists; only this read is untracked). Auth: Bearer , Pro or Teams plan. UK/EU residency.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days_back`	No	Number of days of usage history to include in the summary.
`private_session`	No	Set to true to skip logging this summary call.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description aligns without contradiction. It adds valuable context: data sources (AIToolCallLog, ValueEvent, AIValidationRunLog), private_session behavior (bypasses logging but historical data remains), auth requirements (Bearer token, Pro/Teams), and residency (UK/EU). This depth exceeds what annotations alone provide.

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 well-structured with labeled sections (WHEN TO CALL, WHEN NOT TO CALL, BEHAVIOR) and every sentence serves a purpose. It is information-dense without fluff, front-loading the core purpose before diving into usage and behavioral details.

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?

Covers all necessary aspects: purpose, return values, usage conditions, exclusions, behavior, data sources, privacy, auth, and residency. An output schema exists so detailed return structures are understandably omitted, but the description gives a high-level overview of what is returned, making it complete for an agent to decide when and how to invoke.

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% with both parameters described, but the description enriches meaning: it explains the configurable window defaults to 30 days and clarifies that private_session=true bypasses server-side logging while historical data persists. This adds behavioral nuance beyond the schema's concise descriptions, though much is redundant.

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 it 'summarises the caller's tool-usage patterns and value signals over a configurable window' and lists specific return fields (tool_call_counts, top principles, value_event_counts, readiness trend). This specific verb+resource+scope distinguishes it from sibling tools focused on architecture, validation, or personal guidance.

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?

Provides explicit WHEN TO CALL and WHEN NOT TO CALL sections, including example user queries ('how is the Blueprint helping me/my team') and clear exclusions (proactive calls, cross-user comparison). This goes beyond typical usage guidance and effectively prevents misuse.

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

All AI Design Blueprint tools Inspect

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

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose within its domain: the validators are differentiated by lens (architect/design/spec), content tools are split by entity (principles/clusters/guides/examples/assets) with list/get/search variants, and even the me.* and handoffs.* tools have non-overlapping functions. The only near-overlap (architect.validate vs architect.validate_consensus) is explicitly disambiguated by the consensus variant's description.

Naming Consistency4/5

Tool names consistently use a domain prefix (architect., principles., me., etc.) and snake_case throughout. While most are action-oriented (validate, list, get, search, add, await, report, summarize), some me.* and handoffs.* names are noun phrases (me.learning_path, handoffs.agency) that don't signal the action as clearly, creating minor deviation from a pure verb_noun or action pattern.

Tool Count3/5

At 29 tools, the set is heavy but justified by the server's broad multi-domain scope (doctrine, validation, learning, support, and team analytics). Each tool has a distinct role, but the number exceeds the typical well-scoped range, and some content types (e.g., examples) could have been consolidated without losing function.

Completeness4/5

The server covers its apparent domains thoroughly: doctrine content has list/get/search for most entity types, validation covers architecture/design/spec with consensus and certification, and user learning/support have appropriate tools. Minor gaps exist—e.g., examples have no list-all endpoint, and session management is web-only—but none are blocking for core workflows.

Resources

Need Help?