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repair

Read-onlyIdempotent

One-shot repair: return corrected values that would make failing data valid under the Blueprint. Use repair_path to see the steps instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesGeodesicAI API key (gai_...)
blueprintNoBlueprint name (workflow_name) to use
structured_dataYesThe document's extracted fields as key/value pairs. Keys are open by design - your Blueprint's rules define what is checked
derivation_rulesNoMath 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
formal_constraintsNoConstraint 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

TDQS

A4.4/5.0
Behavior4/5

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

The annotations already declare the tool read-only, idempotent, and non-destructive. The description adds that it returns corrected values and is one-shot, which clarifies the output behavior and invocation model beyond what annotations state. This is more than the minimal bar, so a 4 is warranted.

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 extremely concise: two sentences that front-load the core action ('One-shot repair') and immediately provide a key alternative. No wasted words; every sentence earns its place.

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?

Given the tool has no output schema but the description explains the return value (corrected values), and the schema fully documents all parameters, the description is complete enough for an agent to select and invoke it correctly. It does not cover edge cases like already-valid data, but that is not essential for basic invocation.

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?

The input schema covers all five parameters with 100% description coverage, so the schema already provides the necessary parameter semantics. The description does not add parameter information, but it does not need to; the baseline of 3 is appropriate.

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 identifies the tool as a one-shot repair that returns corrected values for failing data under the Blueprint. It uses a specific verb and resource, and explicitly distinguishes itself from the sibling tool repair_path by contrasting the one-shot output with step-by-step 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?

The description provides explicit guidance on when to use this tool versus repair_path: use repair for the corrected values directly, and use repair_path to see the steps. This satisfies the when-to-use and when-not-to-use criteria, even though it does not mention all other siblings.

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.

Resources