governance-platform

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

With no annotations provided, description carries full burden and succeeds in disclosing the three specific analytical methods employed (structural fingerprinting, cluster analysis, twist-compression obstruction) and output format (human-readable geometric proof). Minor gap: doesn't explicitly confirm read-only safety or computational cost of 'deep' analysis.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear front-loading of purpose ('Deep anomaly analysis with geometric proof'), followed by methodology enumeration and return value description. The 'Args:' section is somewhat abrupt and isolated from the narrative flow, slightly disrupting readability.

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

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

Given no output schema exists, the description adequately explains return values (human-readable geometric proof). Addresses the complex analytical nature of the tool. Minor deduction for failing to mention authentication requirements (api_key) or expected structure of the nested structured_data input.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, requiring the description to fully compensate. It documents 'structured_data' in the Args section but completely omits 'api_key', leaving 50% of parameters undocumented. The nested nature of structured_data (additionalProperties: true) is also not explained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States specific action (deep anomaly analysis) with distinctive methodology (geometric proof) and output format. Explicitly distinguishes from simpler flagging tools via 'not just a flag' and differentiates from blueprint-dependent siblings with 'No Blueprint required.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage context (when you need explanatory proof vs. simple detection) and notes prerequisite absence (no Blueprint). However, lacks explicit comparison to similar siblings like check_drift, validate, or check_feasibility regarding when to prefer this deeper analysis.

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

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

No annotations provided, so description carries full burden. Adds valuable context about format conversion ('Blueprint-compatible format') and side effect ('inclusion in a Blueprint'). However, lacks disclosure on idempotency, reversibility, or what happens if the rule is already approved.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with clear purpose statement, followed by workflow context, then structured Args documentation. No redundant or filler text; every sentence provides actionable information for tool selection and invocation.

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

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

Given zero annotations and no output schema, the description successfully documents the parameter semantics and workflow integration. Minor gap: does not describe return values or success/failure behavior, which would help the agent handle the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, but the Args section fully compensates by documenting all 3 parameters with rich semantics: api_key includes format hint ('starts with gai_'), rule_id specifies provenance ('from discover_patterns results'), and blueprint clarifies concept ('Discovery session namespace').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States specific action ('Promote into Blueprint-compatible format') and resource ('discovered rule'). Clearly positions this as an approval action that follows pattern discovery, implicitly distinguishing it from sibling 'reject_rule' and fitting it into the 'discover_patterns' workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use ('After running discover_patterns') and selection criteria ('high-confidence rules'). References the prerequisite sibling tool. Lacks explicit mention of 'reject_rule' as the alternative for low-confidence rules, though this is implied.

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

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

With no annotations, the description carries the full burden. It discloses internal behavior ('Runs full validation, then applies the Blueprint's execution gate') and output format ('Returns a simple allow/block decision with reasoning'). Minor gap: does not clarify if calling this tool has side effects (e.g., logging the decision) or if it is idempotent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections: purpose statement, behavioral details, usage context with concrete examples, sibling differentiation, and parameter documentation. Every sentence provides distinct value; no redundancy or filler.

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

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

Strong completeness given constraints (no annotations, no output schema, 0% schema coverage). Describes return value ('allow/block decision with reasoning') despite lack of output schema. Could improve by mentioning error conditions (e.g., invalid blueprint) or whether the decision is recorded.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, requiring full compensation. The 'Args' section documents all three parameters: api_key includes format hint ('starts with gai_'), structured_data explains it contains 'the data associated with the action', and blueprint notes it 'governs this action type'. Deducted one point because structured_data could clarify expected schema/shape.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific action ('Decide whether an action should be allowed to proceed'), identifies the resource (Blueprint's execution gate), and explicitly distinguishes from sibling tool 'validate' ('Different from validate: validate says 'is this data correct?' authorize_execution says 'should this action happen?'').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use ('when your agent is about to take a real-world action (payment, filing, API call, data write) and needs a deterministic go/no-go') and provides clear differentiation from the 'validate' alternative, framing the decision as validation vs. authorization.

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

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

With no annotations provided, the description carries the full burden. It successfully explains what constitutes drift ('geometric embedding moves to a different region'), but fails to disclose operational traits: whether the tool is read-only, idempotent, has side effects, or what errors might occur. The repeated call suggestion implies safety but doesn't explicitly state it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with purpose front-loaded, conceptual explanation in the middle, and parameter documentation at the end. The geometric embedding explanation, while technical, earns its place by defining the detection mechanism. Only minor verbosity prevents a 5.

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

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

Given the lack of output schema and annotations, the description should explain return values (e.g., drift detected boolean, confidence score), but it doesn't. While parameters are well-covered, the absence of output documentation and error handling details leaves a significant gap for a monitoring tool intended to be called repeatedly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the Args section comprehensively documents all three parameters. It adds crucial semantic context: api_key format ('starts with gai_'), structured_data's role ('New data point'), and blueprint's purpose ('geometric embedding context'). This fully compensates for the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks for data pattern shifts and specifically mentions 'since previous observations' and 'track drift over time,' which distinguishes it from point-in-time analysis tools like analyze_anomaly. It defines the specific domain (geometric embedding, constraint space) though could more explicitly contrast with siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance ('Call this repeatedly as new data arrives') and notes flexibility ('Works with or without a Blueprint'). However, it lacks explicit 'when not to use' guidance or named alternatives, leaving the agent to infer when to prefer this over analyze_anomaly or validate.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully explains the detection algorithm ('twist-compression operator'), what constitutes a conflict ('fundamental conflicts...cannot be resolved by adjusting any single field'), and the return structure ('obstruction type, magnitude, and specific constraint interactions'). It lacks operational details like rate limits or error states, but covers the core behavioral logic well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with zero waste: a clear purpose statement, technical method explanation, return value specification, and usage context—all in four sentences. The Args section follows as structured documentation. Every sentence earns its place by adding distinct information not present in the schema or title.

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

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

For a 3-parameter tool with no output schema and no annotations, the description achieves completeness by documenting all inputs (via Args), explaining the algorithmic behavior, and describing the conceptual return values ('obstruction type, magnitude') despite the lack of formal output schema. It provides sufficient information for an agent to invoke the tool correctly and interpret results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the Args section provides critical compensatory documentation: it adds format constraints ('starts with gai_'), semantic typing ('derivation rules and formal constraints'), and value constraints ('numeric key-value pairs') that are completely absent from the schema. This is exemplary parameter documentation for low-coverage schemas.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb-resource pair ('Check whether a set of constraints can be simultaneously satisfied') and distinguishes itself from siblings like 'validate' and 'validate_and_repair' by positioning this as a 'Fast pre-check before running full validation.' The technical specificity ('twist-compression operator', 'structural obstructions') further clarifies its unique role in the toolset.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear temporal context ('Fast pre-check before running full validation') indicating when to use it in a workflow. However, while it implies the existence of full validation alternatives, it does not explicitly name sibling tools like 'validate' or describe when NOT to use this tool (e.g., when you need actual solutions rather than feasibility checks).

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

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

With no annotations provided, the description carries the full burden. It discloses that the tool performs 'trajectory analysis' and compares 'valid state space,' which hints at the computational nature of the operation. However, it fails to specify whether this is read-only (implied by 'compare' but not stated), expected runtime, rate limits, or the specific structure of the output since no output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured with a front-loaded purpose statement ('Compare outcomes under different rule sets') followed by behavioral details and a structured Args list. No sentences are wasted; the 'what-if analysis' phrase immediately signals the tool's analytical nature.

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

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

For a tool with 5 parameters, nested objects, and no output schema, the description adequately covers all input parameters and explains the comparative logic. It conceptually describes the output ('shows how the valid state space differs'). It could be improved by describing the actual return format or structure since no output schema is available, but it meets the minimum threshold for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the Args section fully compensates by documenting all five parameters. It adds crucial semantic context that the schema lacks, particularly the relationship between 'blueprint' (rule set A) and 'rules_b'/'constraints_b' (rule set B), and the api_key format hint ('starts with gai_').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs comparative trajectory analysis under two rule sets to show differences in valid state space. It effectively distinguishes this as a 'what-if' analysis tool, though it could more explicitly differentiate from siblings like 'check_feasibility' or 'validate' which operate on single rule sets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides an implied usage pattern through the 'what happens if I change this rule?' example, suggesting it's for comparing alternative configurations. However, it lacks explicit guidance on when to use this versus 'check_feasibility' or 'validate', and doesn't specify prerequisites or when not to use it.

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

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

No annotations provided, so description carries full burden. Explains critical behavioral modes ('observe' vs 'enforce'), describes what each boolean flag controls (anomaly detection, drift tracking, high assurance), and clarifies that the platform validates against these rules. Missing: return value description, idempotency behavior, or error conditions for duplicate workflow_name.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Length is substantial (16 params with 0% schema coverage necessitate detail), but structure is logical: concept → prerequisites → alternatives → detailed args. Each sentence adds value, though the Args section is necessarily dense. No wasted words or repetition of schema titles.

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

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

Excellent coverage of 16 input parameters, but tool has no output schema and description fails to specify return values (e.g., does it return the blueprint ID, a confirmation object, or nothing?). For a creation operation, this omission leaves a critical gap in contextual completeness despite strong input documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage, but the Args section compensates extensively. Provides examples for extracted_fields and derived_fields, enumerates and explains all derivation_rules types (add, subtract, items_multiply, etc.) and formal_constraints types (magnitude_anchor, relative_anchor, etc.), and clarifies boolean defaults and semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with specific verb+resource ('Create a Blueprint') and immediately defines it as 'a governance contract that defines validation rules.' Distinguishes from siblings by referencing load_rule_pack and discover_patterns as alternatives for different use cases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use alternatives: 'If you don't know what rules to define, use load_rule_pack... or use discover_patterns.' References blueprint_guide prompt for complete schema reference, providing clear decision criteria for tool selection.

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

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

With no annotations provided, the description discloses important behavioral traits including the validation checkpoint between stages and forward propagation of repair suggestions. It omits critical execution details such as the return value structure, error handling behavior, and whether the operation is synchronous.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description follows a logical structure with a front-loaded purpose statement followed by behavioral elaboration and an Args section. The text is appropriately sized with no redundant sentences, efficiently using vertical space for the parameter examples.

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

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

For a complex orchestration tool with no output schema, the description adequately covers the conceptual model and most input parameters but leaves significant gaps regarding the return value (e.g., chain ID, status) and the undocumented `api_key` parameter.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema coverage, the description effectively compensates by providing semantic meaning for three parameters: blueprint governance, stage definitions with JSON examples, and TTL semantics. It notably omits any description of the `api_key` parameter, leaving authentication requirements undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Create a multi-agent sequential execution chain') and clarifies the mechanism ('pipeline where multiple agents process data in sequence'). However, it does not explicitly differentiate this tool from siblings like `create_blueprint` or `submit_chain_stage`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implicit usage guidance by describing validation behavior and repair propagation, suggesting appropriate contexts for multi-stage workflows. It lacks explicit statements about prerequisites (e.g., blueprint must exist first) or when to prefer single-stage alternatives.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately explains the conceptual decomposition model (three orthogonal error components) but lacks operational transparency regarding side effects, idempotency, whether results are cached, or rate limiting implications of the GeodesicAI API dependency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately structured with the conceptual overview front-loaded, followed by the Args documentation. The technical terminology is dense but necessary for the domain. The Args list format is efficient, though the 'Args:' prefix style slightly duplicates structured schema information without adding value beyond the list items themselves.

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

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

Given the absence of an output schema and the complexity of the mathematical operation, the description is insufficient regarding return values. While it states the tool 'Identifies the primary failure type and contributing fields,' it does not describe the structure of the decomposition result (e.g., whether it returns objects keyed by 'exact', 'co-exact', 'harmonic' or another format).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Excellent compensation for 0% schema description coverage. The Args section provides semantic meaning for all six parameters, including critical format hints (api_key starts with 'gai_'), data type expectations ('numeric key-value pairs'), and conditional logic ('optional if blueprint provided') that the schema completely lacks.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool's specialized purpose using domain-specific terminology ('Hodge-style decomposition') and enumerates the three specific error components (Exact, Co-exact, Harmonic) it identifies. However, it does not explicitly differentiate this from siblings like `analyze_anomaly` or clarify its position in a validation workflow relative to `validate`.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like `validate`, `analyze_anomaly`, or `repair`. It does not specify prerequisites (e.g., 'use this after validation fails') or contraindications for its use.

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

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

Without annotations, the description carries significant weight. It crucially discloses 'Source data is never stored — only statistical summaries persist' and explains the deterministic nature of the analysis. It also details the return structure. Missing minor operational details like rate limits or execution duration.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The narrative portion is efficiently front-loaded with key constraints (no Blueprint needed) and privacy guarantees. However, the Args section contains a redundant duplicate entry for 'api_key', which wastes tokens and creates minor confusion.

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

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

Given the lack of output schema and annotations, the description adequately covers the return value structure, workflow integration (approve_rule), data handling policies, and parameter meanings. Sufficient for an agent to invoke correctly, though error handling details would improve it further.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates effectively by explaining all three parameters: api_key format (starts with gai_), documents semantics (structured data objects), and blueprint purpose (namespace). Deduction for redundant listing of api_key in the Args section.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool 'discovers patterns deterministically' using specific techniques (motif discovery, structural routing, geometric fingerprinting) and defines the output (candidate validation rules with confidence scores). It clearly distinguishes from siblings by stating 'No Blueprint required' and referencing the specific promotion path via 'approve_rule'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear workflow context by stating no Blueprint is required upfront and explicitly naming 'approve_rule' as the mechanism to promote discovered rules. However, it lacks explicit guidance on when to choose this over similar analysis tools like 'analyze_anomaly' or 'structural_types'.

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

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

Discloses deterministic nature and ranking algorithm (drift, confidence, shortest path, risk). With no annotations provided, the description carries full burden but omits whether the operation is read-only or has side effects, and doesn't mention error conditions or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with conceptual summary first, then mechanism, then parameter details. Minor deduction for duplicate 'api_key' entry in Args section. Given zero schema coverage, the length is appropriate and every sentence adds value.

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

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

For a complex 6-parameter tool with no annotations, no output schema, and many siblings, the description adequately covers the conceptual model, parameter semantics, and ranking behavior. Minor gap in not describing the return structure or error scenarios despite lacking output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the Args section comprehensively compensates by documenting all 6 parameters including format hints ('starts with gai_'), valid ranges ('1-10'), and enum semantics for rank_by options. Exceptional compensation for schema deficiency.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Excellent clarity with 'Deterministic forward reasoning' and 'generates candidate next states by applying Blueprint rules.' Specifically contrasts with sibling tools like 'counterfactual' (hypothetical/backward) and 'repair' (fixing current state) by emphasizing forward/reachable state generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage context ('Given the current data state') and prerequisites (Blueprint rules, API key), but lacks explicit when-to-use vs siblings like 'counterfactual' or 'solve'. No guidance on when forecasting is preferred over other prediction tools.

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

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

With no annotations provided, the description carries the full burden. It explains the algorithmic behavior (six weighted signals with detailed descriptions) and return format (confidence level + recommendation), but omits operational details like error conditions, rate limits, or idempotency that would be necessary for full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a one-line summary, constraint note, detailed bullet points for the six signals, return value description, and parameter documentation. The bullet points earn their place by explaining the composite score components, though the Args section repeats information that could be inferred.

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

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

Despite no output schema, the description documents the return values (confidence level high/medium/low and recommendation). It covers the tool's purpose, input requirements, internal methodology, and typical usage context, making it sufficient for an agent to invoke correctly despite the minimal schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description compensates effectively via the Args section. It documents the api_key format ('starts with gai_') and crucial context for state_vector ('from validate results'), providing the semantic meaning missing from the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool computes a 'composite geometric confidence score' and lists six specific validation signals it combines. The 'No Blueprint required' note helps distinguish it from blueprint-dependent siblings like create_blueprint or validate_and_repair.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides context that the tool 'works on any validate result' and is 'typically called with the state_vector from a validate result,' establishing a workflow relationship with the validate tool. However, it lacks explicit when-not-to-use guidance or comparison to similar analysis tools like analyze_anomaly or check_drift.

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

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

Without annotations, the description carries the full burden. It successfully describes the output content (execution sequence, determinism flags, runtime metrics) but fails to disclose operational traits like whether this consumes API quota, writes audit logs, or is safe to call repeatedly (read-only vs side effects).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear paragraph breaks separating the action, output details, usage guidance, and parameter documentation. It avoids fluff while conveying necessary information, though the Args section could benefit from noting parameter optionality.

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

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

Given the lack of output schema and annotations, the description adequately explains what the trace contains but leaves gaps regarding the return structure format, error handling behavior (what happens if the API key is invalid?), and whether the trace is returned synchronously or async.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the Args section compensates effectively by documenting all three parameters with useful context (API key format 'starts with gai_', purpose of structured_data and blueprint). Docked one point for failing to mention that 'blueprint' is optional with a default value of 'default'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Run[s] validation and return[s] the detailed execution trace' and specifies what the trace contains (sequence, determinism, runtime). However, it could more explicitly distinguish this from the sibling 'validate' tool by clarifying that this returns node-level debugging details versus a simple pass/fail result.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides specific use cases ('Use for debugging, compliance audits, or understanding exactly what the platform checked'), but lacks explicit guidance on when to use this versus the sibling 'validate' tool, or whether this should be used in production workflows versus troubleshooting only.

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

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

No annotations provided, so description carries full burden. Discloses return structure ('name, workflow identifier, mode, and field/rule/constraint counts') despite lack of output schema. Documents auth format ('starts with gai_'). Does not explicitly confirm read-only status or pagination behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Efficient four-sentence structure: purpose, return values, usage guidance, and parameter documentation. Every sentence earns its place. Minor deduction for embedded 'Args:' formatting which is slightly informal but still clear.

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

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

For a simple listing tool with 1 parameter and no output schema, the description is complete. It explains the return payload, documents the single parameter, and provides integration context with sibling tools. No significant gaps remain given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage (only title present). Description compensates by documenting the api_key parameter's purpose ('GeodesicAI API key') and format hint ('starts with gai_'), adding essential semantic meaning missing from the structured schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States specific verb ('List') + resource ('Blueprints') + scope ('all available governance configurations'). Distinguishes from sibling 'create_blueprint' by contrasting list vs. create operations, and references 'validate' tool to clarify output usage.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit integration guidance: 'Use the workflow_name as the 'blueprint' parameter when calling validate.' This clarifies how to use output with a specific sibling tool. However, lacks explicit 'when not to use' guidance or filtering alternatives.

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

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

With no annotations provided, the description carries the full burden. It discloses the dual-mode behavior (list vs. load), clarifies that this tool does not persist data ('then use create_blueprint to save it'), and details the content structure of returned packs ('field definitions, derivation rules, constraints'). Minor gap: does not explicitly state read-only nature or error conditions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with purpose front-loaded, followed by concept explanation, usage patterns, examples, and parameter details. Minor redundancy exists ('Omit to list available packs' appears twice), but information density remains high with no wasted sentences.

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

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

For a governance-platform tool with no output schema, the description provides excellent domain context by enumerating specific pack types (invoice, payroll, legal, etc.) and explaining pack contents. It adequately prepares the agent to understand return values even without a formal output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Given 0% schema description coverage, the description fully compensates by providing crucial semantic details: api_key format hint ('starts with gai_') and pack_id behavioral semantics ('Omit to list available packs'). It transforms the schema's bare types into actionable parameter guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb-resource pair ('Load a prebuilt Blueprint template') and immediately distinguishes the domain ('fast onboarding', 'governance configurations'). It effectively differentiates from sibling tool create_blueprint by stating this tool only loads configurations while create_blueprint is needed to save them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly documents the two operational modes: 'Call with no pack_id to list all available packs' versus 'Call with a pack_id to load the full configuration.' It also establishes the workflow sequence by directing users to use create_blueprint as the next step after loading, providing clear when-to-use guidance.

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

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

No annotations provided, so description carries full burden. While 'Reject' implies a state change, the description does not clarify if this is permanent, reversible, or what the side effects are (e.g., if rejected rules are deleted or archived). No auth requirements beyond the api_key parameter.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise with no filler. The 'Args:' format efficiently delivers parameter documentation, though front-loading behavioral context before the argument list would improve structure slightly.

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

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

For a state-changing operation with no annotations and no output schema, the description lacks critical behavioral context (what happens upon rejection, error conditions). The parameter documentation partially compensates for poor schema coverage but is insufficient for a complete operational picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description effectively compensates by documenting all three parameters: api_key includes format hint ('starts with gai_'), rule_id clarifies it targets a 'discovered rule', and blueprint adds semantic context ('Discovery session namespace').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Reject') and target resource ('discovered candidate rule'). However, it does not explicitly differentiate from sibling tool 'approve_rule' or explain the workflow context of candidate rules.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance provided on when to use this tool versus 'approve_rule' or other alternatives. No prerequisites or workflow steps mentioned (e.g., whether discovery must be complete first).

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

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

With no annotations provided, the description carries full behavioral disclosure burden. It successfully explains the algorithmic approach ('algebraic projection for linear constraints and iterative geodesic projection for nonlinear constraints') and output format. However, it omits operational details such as error handling, idempotency, or side effects of the repair computation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured logically with a high-level mathematical definition, functional explanation, algorithmic details, and parameter documentation. Every sentence contributes value. It is slightly dense with technical terminology ('constraint manifold,' 'geodesic projection') but appropriate for the domain complexity.

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

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

Given the tool's complexity (5 parameters, nested objects, mathematical domain) and lack of output schema, the description adequately covers the core functionality and parameter semantics. It mentions the API key requirement implying authentication needs. It could be improved by describing the output structure or error scenarios, but the 'field-by-field repair suggestions' provides essential return value context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description compensates excellently through the 'Args:' section. It adds critical semantic details: api_key format ('starts with gai_'), structured_data structure ('key-value pairs'), and conditional requirement logic ('optional if blueprint is provided'). This fully compensates for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool computes 'the nearest valid point on the constraint manifold' and handles 'structured data with errors, missing values, or inconsistencies.' It specifies the output as 'field-by-field repair suggestions with geometric confidence scores.' However, it does not explicitly differentiate from the sibling tool 'validate_and_repair'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implicit usage guidance by explaining that derivation_rules/formal_constraints are 'optional if blueprint is provided,' indicating mutual exclusivity between direct rules and blueprint loading. However, it lacks explicit guidance on when to use this tool versus siblings like 'validate_and_repair' or 'validate,' and provides no 'when-not-to-use' criteria.

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

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

With no annotations provided, the description carries full burden. It discloses behavioral constraints like max_depth range (1-10) and rank_by options, and mentions 'minimal' changes implying optimization. However, it omits safety-critical details like whether this performs mutations or is read-only analysis, and what happens when no path exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with a front-loaded summary sentence, contextual elaboration, and a clean Args section. No redundant or wasted text; every sentence contributes to understanding the tool's function or parameters.

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

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

Adequate for the input side given the parameter documentation, but incomplete regarding outputs. With no output schema provided and a complex return type implied (a path/sequence), the description should specify what the tool returns (e.g., a repair plan or sequence of operations).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Excellent compensation for 0% schema description coverage. The Args section documents 4 of 5 parameters with meaningful semantics: structured_data (invalid state), blueprint (constraint space), max_depth (search limit with valid range), and rank_by (criterion with enumerated options). Only api_key is undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool finds a 'shortest path' and computes a 'sequence of minimal field changes' to fix invalid data. Strong verb+resource combination. However, it does not explicitly differentiate from the sibling 'repair' tool, which could cause selection ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context ('Given data that fails validation') establishing when to use it, but lacks explicit guidance on when to choose this over siblings like 'repair' or 'validate_and_repair', and does not state prerequisites or exclusions.

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

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

With no annotations provided, the description carries the full disclosure burden. It effectively communicates deterministic execution, zero LLM token consumption, and the fallback flag (not_deterministic). Minor gap: lacks details on output format, latency, or whether results are cached.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with high information density. Front-loads key differentiators (deterministic, no tokens, no blueprint) before enumerating domains. The bulleted list efficiently covers 9 domains without verbosity. No redundant sentences.

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

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

Given the tool's complexity (9 distinct mathematical domains) and lack of output schema, the description adequately covers scope, failure modes, and fallback behavior. Could be improved by describing the return structure on success, but the deterministic/not_deterministic contract is clearly defined.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates effectively by specifying that api_key starts with 'gai_' (format hint) and that query accepts both 'natural language or symbolic notation' (type guidance). This adds essential semantic meaning missing from the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with specific verbs ('Route... to the appropriate deterministic solver') and clearly delineates the 9 supported domains. It explicitly distinguishes from siblings by stating 'No Blueprint required,' contrasting with blueprint-centric tools like create_blueprint, list_blueprints, and submit_chain_stage.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-not-to-use guidance: 'If the problem cannot be solved deterministically, returns not_deterministic: true. The agent should then use its own LLM for that problem.' This clearly defines the boundary between this tool and the agent's native LLM capabilities.

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

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

With no annotations provided, the description carries the full burden. It successfully describes the return payload structure (categories with counts, fields, hints) and the unsupervised nature of the discovery ('without being told what categories exist'). However, it lacks disclosure on mutability, caching behavior, or error states when the blueprint session is invalid.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized at four sentences, front-loaded with the action ('Get'), followed by workflow context, return value details, and parameter info. The Args section is informal but efficient given the lack of schema descriptions. No redundant or wasteful text.

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

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

Given the lack of output schema, the description adequately describes what the tool returns. However, with 0% schema coverage and two parameters (one required), documenting only one parameter leaves the tool under-specified. The workflow relationship with discover_patterns is well-covered, but parameter documentation is incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage. The description compensates partially by documenting 'blueprint' as 'Discovery session namespace' in the Args section, but completely omits the required 'api_key' parameter. With zero schema coverage and a required parameter undocumented, this is a significant gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves 'auto-discovered structural type classifications' and distinguishes itself from the sibling 'discover_patterns' by specifying this is used after that step to get results. It specifies the resource (structural categories) and what attributes are returned (document counts, distinguishing fields, domain hints).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear workflow context with 'After running discover_patterns,' establishing the prerequisite sequence. It implies this tool is for retrieval of previously discovered data rather than performing discovery itself, though it could explicitly state what happens if called before discovery completes.

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

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

With no annotations provided, the description carries full burden. It successfully discloses the validation logic, conditional state change (advancement only if validation passes), and response contents (next stage info, accumulated repair suggestions). Missing error handling details and idempotency/safety characteristics.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured docstring format with clear separation between behavioral description (2 sentences) and Args documentation. Zero redundant text; every sentence conveys distinct information about the tool's operation or parameters.

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

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

Given no output schema, the description admirably documents return value structure ('next stage info', 'accumulated repair suggestions'). References prior stages and chain workflow context. Would benefit from noting what happens when validation fails.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Excellent compensation for 0% schema description coverage. All 4 parameters are documented: api_key includes format hint ('starts with gai_'), chain_id references sibling tool create_chain for provenance, and structured_data is contextualized as the data being validated.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb-resource combination ('Submit data for a chain stage') with specific behavioral elaboration (validation against Blueprint, conditional advancement). Distinguishes from validation-only tools by emphasizing the advancement mechanism.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies workflow position through 'advance the chain' and 'from prior stages', plus references to create_chain for chain_id provenance. However, lacks explicit contrast with validate or repair siblings (e.g., 'use this instead of validate when you need to progress the chain').

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

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

With no annotations provided, the description carries full disclosure burden. It successfully explains the three validation dimensions (mathematical accuracy, structural consistency, semantic plausibility), return values (PASS/FAIL/REVIEW), and determinism guarantees (hash for auditability). Minor gap: doesn't mention rate limits, error handling, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections: purpose, validation types, determinism guarantee, and prerequisites. The determinism paragraph ('Auditable, replayable, legally defensible') conveys compliance-critical behavior efficiently. Slight verbosity in the determinism explanation, but justified given legal/defensibility context.

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

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

Despite no output schema, the description documents return values (PASS/FAIL/REVIEW) and the determinism hash. It contextualizes the tool within the broader Blueprint workflow (referencing create_blueprint, load_rule_pack, list_blueprints). Minor gap: doesn't describe error response format or partial failure behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no parameter descriptions in JSON schema), but the description fully compensates via the Args section. It documents all three parameters: api_key includes format hint (starts with gai_), structured_data clarifies structure (key-value pairs), and blueprint explains how to discover valid values (use list_blueprints).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a precise action-statement: 'Validate structured data against a Blueprint's rules.' It specifies the verb (validate), target resource (structured data), and scope (Blueprint rules). It distinguishes from siblings like repair or analyze_anomaly by focusing on rule-based validation rather than anomaly detection or remediation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states prerequisites and alternatives: 'A Blueprint is required for meaningful validation. Without one, use create_blueprint or load_rule_pack to define your governance rules first.' Also references list_blueprints for discovering valid blueprint values, creating a clear decision tree for the agent.

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

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

With no annotations, description carries full burden. Discloses three distinct behavioral states (PASS/FAIL/REVIEW) and their respective return payloads (determinism hash, field-by-field corrections with confidence scores, review reasoning). Lacks operational details (idempotency, side effects, persistence of repairs).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded purpose statement followed by behavioral states, usage recommendation, and parameter details. No tautology or redundancy. Structured with clear Args section. Every sentence advances understanding of tool selection or invocation.

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

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

For a complex dual-operation tool with no output schema, comprehensively describes return values across three states and agent workflow. Cross-references sibling for parameter enumeration. Minor gap: could clarify if repairs are persisted or ephemeral.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, requiring full compensation. Args section documents all 3 parameters: api_key (with format hint 'starts with gai_'), structured_data (semantics as 'key-value pairs'), and blueprint (with usage instruction to use list_blueprints). Adds significant value beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States specific dual action (validate + repair) and target resource (structured data). Explicitly distinguishes from siblings 'validate' and 'repair' by noting this is a 'Single call that combines' both operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'recommended starting point for most agent integrations' (when-to-use). Provides workflow guidance for each outcome (inspect repairs and resubmit). Cross-references sibling tool 'list_blueprints' for parameter discovery.

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