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check_realization

Structural realization analysis of a payload against the Blueprint's reference configuration (requires a 'realization' block; otherwise status=skipped). Diagnostics-tier tool; prefer validate or analyze_anomaly for standard checks.

Input Schema

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

TDQS

A4.4/5.0
Behavior3/5

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

The description adds the key behavioral trait that the tool returns 'status=skipped' without a realization block, and labels itself as diagnostics-tier. However, the annotations provide no safety hints (all false), and the description does not disclose whether this tool mutates state, requires special permissions, or what the output structure looks like. It carries only part of the burden.

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 two sentences, front-loaded with the main purpose, and includes all necessary scope and alternative guidance without extraneous text. Every clause provides useful information.

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 diagnostics tool with no output schema and all-false annotations, the description adequately covers the tool's purpose, triggering condition, and relationship to alternatives. It omits details about the response format or side effects, but the core selection and invocation context is complete enough for an agent to act.

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 descriptions for each parameter, so the baseline is 3. The description adds meaningful context by explaining that a 'realization' block is required within the payload, which clarifies the expected structure for the 'structured_data' parameter beyond the schema's generic 'extracted fields' description.

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 tool performs 'Structural realization analysis of a payload against the Blueprint's reference configuration', which is a specific verb-resource combination. It also distinguishes itself from siblings by mentioning 'Diagnostics-tier tool' and directing standard checks to 'validate or analyze_anomaly'.

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 guidance is provided: 'prefer validate or analyze_anomaly for standard checks' names specific alternative tools. The prerequisite requirement ('requires a realization block; otherwise status=skipped') also clarifies when this tool is appropriate versus when it will not produce useful results.

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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