factorguide

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

With no annotations, the description carries full burden. It discloses that it is a quick diagnostic with risk prediction and variance inclusion, plus auth and trial limits. It does not mention side effects or read-only nature, but the behavioral traits are sufficiently conveyed for a computational tool.

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 concise with 4 sentences, front-loading the purpose and then adding auth/trial info. No unnecessary words, and each 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?

Given no output schema and 6 parameters, the description covers purpose, key parameters, and auth, but lacks explanation of the output format and the role of sample_size. It is adequate for basic use but leaves some gaps.

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 low (2 of 6 parameters have descriptions). The description adds context by mentioning 'include variances for sign detectability', helping to understand variance_i and variance_j, but does not detail sample_size or j. This provides some compensation but not full clarity.

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 a 'quick single-pair diagnostic' with 'IC with risk prediction' and 'variances for sign detectability'. It distinguishes from siblings like factorguide_explain and factorguide_synergy_detect by specifying the single-pair diagnostic focus.

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 specifies the requirement of an X-Wallet header and mentions a free trial limit, giving practical usage context. However, it does not explicitly compare to alternative tools or explain when to choose this over siblings like factorguide_regime_detect.

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 bears the full burden. It discloses consumption of query allocation, required X-Wallet header, free trial, and the general nature of the output. It does not mention side effects, but the tool is likely read-only.

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 two sentences, each packed with essential information: purpose, prerequisite, cost, availability, and auth. No superfluous 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?

It covers purpose, prerequisite, and constraints. Though no output schema exists, it describes the output's nature. It lacks error handling info but is sufficient for a straightforward tool.

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 sole parameter 'prediction_hash' is described in the schema, but the description adds important context by linking it to a prior navigate call and emphasizing its role in the tool's purpose, exceeding basic schema info.

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 provides plain-language explanations of a previous navigate response, including wave mechanics grounding. It distinguishes itself from siblings like factorguide_navigate by explicitly requiring a prediction_hash from that call.

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 clearly states the prerequisite (a prior factorguide_navigate call and prediction_hash), and mentions query allocation and tier availability. While it doesn't explicitly exclude alternatives, the context implies use only after navigate.

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 are provided, so the description carries full burden. It discloses the need for an X-Wallet header, trial limitations, and optional report behavior. However, it does not explicitly state whether the tool mutates state or has side effects, leaving some behavioral aspects implicit.

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 front-loaded with the core purpose and then provides specific usage details. It is efficient but slightly verbose with the optional feature explanation. Overall, it earns its space but could be slightly tighter.

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 complexity (13 parameters, no output schema, no annotations), the description is incomplete. It lacks explanations for most parameters, does not describe the output structure, and omits behavioral details beyond authentication. This leaves an agent with insufficient information to use the tool correctly.

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 must explain parameters, but it only mentions a few (e.g., report_sign_detectability, input types). The schema has 13 parameters including task_type, cost_model, model_class, etc., which are not discussed, leaving significant gaps for agent understanding.

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's function: mapping factorization terrain and answering 'How should I factorize, and what will it cost me?'. It specifies inputs and outputs, and distinguishes itself from sibling tools like factorguide_diagnose by focusing on factorization strategy and cost prediction.

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 guidance on when to use each input type (precision matrix preferred for n>2, covariance for sign/CC), optional features (report_sign_detectability), header requirements, and trial info. It does not, however, explicitly state when not to use this tool or compare with all siblings.

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 but offers minimal behavioral disclosure. It mentions 'windowed IC' (implementation approach) and warns that the specification is pending, but lacks details on return format, side effects, input data requirements, or resource constraints.

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 consists of two efficient sentences. The first conveys the tool's purpose; the second communicates critical implementation status. No redundant or wasteful text is present, and information is appropriately front-loaded.

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 empty input schema (unusual for a time series analysis tool) and lack of output schema or annotations, the description is insufficient for operational use. While the 'v1.1 target' note honestly flags intentional incompleteness, it does not compensate for missing critical context such as how time series data is provided or what the tool returns.

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 contains zero parameters. According to scoring rules, 0 parameters establishes a baseline score of 4. There are no parameters requiring semantic clarification beyond the schema structure.

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 states a specific action (Detect), resource (coupling regime changes in time series), and method (via windowed IC), distinguishing it from sibling synergy_detect and other non-analysis tools. However, the 'Specification pending' caveat slightly tempers the clarity regarding current availability.

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 explicit when-to-use guidance or alternative recommendations are provided. While 'Specification pending' implies readiness constraints, it does not specify conditions for use versus sibling tools like factorguide_synergy_detect or factorguide_diagnose.

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 adds critical behavioral constraints not inferable from the schema: 'Zero additional computation required' and 'Does not consume a query allocation.' It also clarifies the feedback loop purpose ('so future predictions improve').

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?

Four sentences with zero waste. Front-loaded with the core purpose ('Complete the prediction loop'), followed by specific metrics, then critical behavioral constraints. Every sentence provides unique value not available in structured fields.

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 specialized statistical domain and lack of annotations/output schema, the description adequately establishes the workflow position and data flow (reporting back after prediction). It could improve by explaining how to obtain prediction_hash or what constitutes valid diagnostic values, but sufficiently covers the tool's functional role.

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, so the description must compensate. It explains the purpose of ess_ratio, psis_khat, and log_lik_gap by naming them as diagnostics to return, and implies approach_taken. However, it omits prediction_hash (required), n_replications, runtime_seconds, and actual_mse_ratio entirely, leaving half the parameters 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 clearly states the tool 'Complete[s] the prediction loop' and specifies reporting 'inference diagnostics' (ESS ratio, PSIS-khat, log-likelihood gap). It effectively distinguishes from siblings like 'diagnose' or 'regime_detect' by focusing specifically on post-prediction outcome reporting to improve future predictions.

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 clear temporal context ('After running the approach FactorGuide recommended') and specifies exactly which metrics to return. However, it lacks explicit contrast with siblings (e.g., when to use 'diagnose' vs this tool) or prerequisite details about obtaining the prediction_hash.

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 discloses the workflow distinction between x402 and MPP integration patterns, but fails to mention side effects, idempotency, verification behavior, or what constitutes success/failure for a financial transaction operation.

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?

Two tightly constructed sentences with zero waste. The first establishes the core operation; the second immediately provides the critical conditional logic for protocol selection. Perfectly front-loaded and appropriately sized.

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 covers input requirements and protocol selection logic, but lacks disclosure of return values, confirmation behavior, or post-submission state changes that would help an agent handle the response appropriately.

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 100%, establishing a baseline of 3. The description maps parameters to the x402 protocol use case but does not add syntax details, format constraints, or semantic relationships beyond what the schema already provides for tx_hash and chain.

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 (submit payment proof), the context (after sending stablecoins), and the target resource (FactorGuide wallet address). It distinguishes from siblings (diagnose, explain, navigate) by being the only payment-related operation.

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?

Excellent explicit guidance: specifies exactly when to use this tool ('For x402: provide tx_hash and chain') and when NOT to use it ('For MPP: use in-band Authorization header instead — no separate submission needed'), clearly distinguishing between two payment protocols.

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 but fails to disclose safety profile (read-only vs destructive), return format, or side effects. It does mention the 'Specification pending — v1.1 target' status, which is relevant behavioral context, but this is insufficient for a tool with complex mathematical processing.

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?

Three sentences with no wasted words. The information density is high, though the critical 'Specification pending' disclaimer might be more effective if front-loaded rather than placed at the end.

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 high complexity (Walsh-Hadamard analysis, 5 parameters with nested object structures, 0% schema coverage, no annotations, no output schema), the description is inadequate. It fails to explain parameter relationships, expected return values, or error conditions necessary for correct 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?

Schema has 0% description coverage, requiring the description to compensate. While it mentions 'pre-computed Walsh coefficients' (referencing the walsh_coefficients parameter), it completely omits explanations for n_samples, n_variables, ic_matrix_ref, and transform_method, leaving the majority of parameters 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 identifies the specific action ('Detect hidden synergistic structure') and method ('Walsh-Hadamard spectral analysis'), which distinguishes it from sibling tools like regime_detect or diagnose. However, it does not explicitly differentiate when to choose this over other analysis tools.

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 implicit usage context by stating it 'Accepts pre-computed Walsh coefficients' and that the 'agent performs the transform locally,' indicating prerequisites. However, lacks explicit when-to-use guidance compared to siblings and does not mention the 'Specification pending' status as a usage blocker until the final sentence.

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