governance-platform

create_blueprint

Create a Blueprint - the governance contract validation runs against.

A Blueprint defines what correct means for your data: fields, the math
that must hold between them, and acceptable ranges. Start from
load_rule_pack or discover_patterns if you have no rules yet; invoke
the blueprint_guide prompt for the full rule/constraint reference.
Returns the new Blueprint's API key.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	No	observe: platform checks the agent's work; enforce: platform computes derived fields itself	observe
`api_key`	Yes	GeodesicAI API key (gai_...)
`require_math`	No	Validate mathematical relationships
`customer_name`	Yes	Organization or project name (also used for storage folder naming)
`workflow_name`	Yes	Unique Blueprint identifier; the value passed as 'blueprint' in validate
`derived_fields`	No	Field names the platform computes from other fields, e.g. ['subtotal','total']
`semantic_checks`	No	Domain-specific semantic check objects
`derivation_rules`	No	Math rules as objects. Types: add, subtract, multiply, divide, round, copy, sum (multi-operand), items_multiply, items_sum. Each needs 'type' plus its fields; see the blueprint_guide prompt
`extracted_fields`	No	Field names the agent extracts from source data, e.g. ['vendor','qty','unit_cost']
`require_coherence`	No	Check cross-field plausibility
`formal_constraints`	No	Constraint objects. Types incl. magnitude_anchor {field,min,max}, relative_anchor {field,reference_field,ratio_min,ratio_max}, max_action_threshold {field,threshold,on_violation}, required_fields {fields}, equals, range, in_set, regex_match, items_magnitude_anchor; see the blueprint_guide prompt
`require_provenance`	No	Require extraction source locations for fields
`require_consistency`	No	Check internal field consistency
`enable_drift_tracking`	No	Track pattern stability across batches
`require_high_assurance`	No	Strictest mode: every check must pass
`enable_anomaly_detection`	No	Flag records that break no rules but do not fit the reference pattern

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already indicate a write operation (readOnlyHint=false). The description adds valuable behavioral context by stating it 'Returns the new Blueprint's API key,' and explains the Blueprint is the governance contract validation runs against. No contradiction with annotations; minor lack of detail about failure modes is acceptable given the 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?

The description is three sentences, front-loaded with the core verb+resource, and each sentence adds value: the definition, guidance on alternatives, and the return value. No wasted words or redundant repetition of schema details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex 16-parameter tool with no output schema, the description provides a strong mental model, usage alternatives, and the key return value. The schema covers individual parameters, so the description does not need to enumerate them. It could mention the observe/enforce mode more prominently, but that is already well-described in the schema.

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%, so the baseline is 3. The description mentions 'blueprint_guide' for rule/constraint types, which mirrors schema descriptions but adds no additional parameter syntax or format beyond what the schema already provides.

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 begins with 'Create a Blueprint - the governance contract validation runs against,' providing a specific verb and resource while immediately defining the Blueprint's role. It further clarifies what the Blueprint defines (fields, math, ranges), which distinguishes it from sibling tools like update_blueprint or delete_blueprint.

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 description explicitly states when not to use this tool: 'Start from load_rule_pack or discover_patterns if you have no rules yet,' and points to the blueprint_guide prompt for full reference. This gives clear alternatives and conditions, satisfying the dimension fully.

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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TDQS

A3.6/5.0

Disambiguation3/5

Most tools have distinct purposes, but several pairs overlap heavily (validate vs validate_repair, repair vs repair_path, analyze_anomaly vs check_drift vs decompose_failure). Detailed descriptions help disambiguate, but the large number of analytics and diagnostics tools creates real selection risk.

Naming Consistency4/5

The vast majority use a consistent snake_case verb_noun pattern (create_blueprint, list_api_keys, verify_certificate). A few single-word or noun-phrase exceptions (validate, forecast, structural_types, recent_inference_decisions) are minor deviations, but overall the pattern is predictable.

Tool Count2/5

At 37 tools, this exceeds the 25+ threshold for 'too many'. While the governance domain is broad, the set could be consolidated (e.g., merging validate_repair into validate, folding repair_path into repair, or trimming diagnostics-tier tools like check_realization and geometric_confidence).

Completeness4/5

The surface covers the full blueprint lifecycle, validation, repair, API key management, discovery, inference governance, and chain management. Minor gaps exist: no direct get_blueprint (only list with counts), and chain lifecycle lacks delete/list/cancel operations. Overall, agents can accomplish core governance tasks without dead ends.

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