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AI Design Blueprint Doctrine

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me.add_evidence

Capture concrete evidence of progress by appending implementation notes to a specific stage in your design blueprint course.

Instructions

Authenticated — append a free-text evidence note to a specific stage in the caller's active course. Notes record concrete implementation observations, decisions, or artefacts that demonstrate progress through a Blueprint principle (e.g. how a delegation boundary was implemented, what approval flow was chosen and why). Persisted as UserStageEvidence rows scoped to (user_id, course_slug, stage_slug). WHEN TO CALL: AFTER the user has articulated something concrete they have built, observed, or decided — not to capture intent or speculation. Pair with me.coaching_context to close evidence gaps. WHEN NOT TO CALL: to log every conversation turn; to record planning, ideas, or todos; on behalf of another user; without the user's awareness (they should know their progress is being recorded). BEHAVIOR: write-only, single insert. Auth: Bearer (Firebase ID token, any plan). UK/EU residency. Notes are visible only to the owning user and are surfaced on me.learning_path / me.coaching_context. Confirms the stage_slug + course_slug pair in the response so the user can see which stage was credited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYesEvidence note to append to the delegation boundary notes for this stage.
stage_idYesID of the stage to append the evidence note to.
course_slugYesSlug of the course the stage belongs to (e.g. 'agentic-fundamentals').
Behavior5/5

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

Description goes beyond annotations (readOnlyHint=false, idempotentHint=false) by detailing write-only, single insert behavior, auth requirements (Bearer token, any plan, UK/EU residency), visibility (user only), and response confirmation. No contradiction with annotations.

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?

Description is well-structured with clear sections (purpose, when to call, when not to call, behavior). Every sentence serves a purpose without being overly verbose. Front-loaded with essential purpose.

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 no output schema, the description explains response contents (confirms stage_slug and course_slug). It covers purpose, usage, behavior, auth, and visibility. For a simple 3-param tool, this is complete and leaves no ambiguity.

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 description coverage is 100%, but the description adds broader context about what notes are for (implementation observations, decisions, artefacts) and that they are appended to a specific stage in the caller's active course. This adds meaningful context beyond the schema's per-parameter 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?

Description clearly states the tool appends a free-text evidence note to a specific stage in the caller's active course. It specifies the action, resource, and differentiates itself by mentioning pairing with me.coaching_context and when not to call, distinguishing it from siblings.

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?

Explicit 'WHEN TO CALL' and 'WHEN NOT TO CALL' sections provide concrete guidance on appropriate usage, including pairing with another tool and examples of when not to use it. This clearly guides the AI agent on selection.

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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