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

Annotations already declare readOnlyHint=true and openWorldHint=false, and the description does not contradict them. The reghdfe-style reference adds minimal algorithmic identity (within-transformation/demeaning), but the description itself discloses little beyond what the annotations and schema already carry.

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?

Eight words with zero padding, and the core method is front-loaded in the first clause. It is efficient, though it leaves spare capacity that could have held a one-line usage hint without bloat.

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?

The output schema and exhaustive parameter documentation cover return values and arguments, so those gaps are closed. The main missing piece is selection context among the roughly 70 siblings, which matters for a complex 17-parameter estimation 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?

Schema description coverage is 100% and the schema is exceptionally rich, covering token budgets for detail, SE formulas for cluster, solver backends, and chaining via as_handle/result_id. The description contributes no parameter-level meaning, so the schema-carrying baseline of 3 applies.

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 verb and resource: OLS with absorbed high-dimensional fixed effects, anchored by the well-known 'reghdfe-style' reference. It is clear what the tool computes, but it does not explicitly differentiate itself from close siblings like hdfe_ols, feols, or demean.

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 is given on when to use absorb_ols versus alternatives such as regress, hdfe_ols, feols, feglm, fepois, or ppmlhdfe. There are no exclusions, preconditions, or context cues beyond the name itself, leaving tool selection to inference.

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?

The readOnlyHint annotation already covers the safety profile. The description adds only a cryptic 'Validation: certified parity evidence' quality claim, which hints at trustworthiness but does not describe output behavior, edge cases, or handling of invalid inputs. No contradiction with annotations.

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 short sentences with the core purpose front-loaded. The validation sentence is concise, though its jargon ('certified parity evidence') is under-specified; overall there is no padding.

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?

The output schema and detailed parameter schema cover return values and most invocation details. Still, with eight parameters and several method-specific siblings, the description would benefit from orienting the agent on when to call this generic tool rather than bonferroni/holm/romano_wolf, and from clarifying the expected pvalues 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?

Schema description coverage is 100%, so the baseline applies. The description adds no parameter-specific meaning; the method field's schema text already enumerates accepted methods and points to romano_wolf where appropriate.

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 names a specific operation ('adjust p-values') and the context ('multiple comparisons'), so an agent can tell what the tool does. It does not, however, differentiate this generic adjustment tool from method-specific siblings like bonferroni, holm, or benjamini_hochberg.

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 when-to-use or when-not-to-use guidance appears in the description. It does not mention that method-specific corrections exist or that romano_wolf is the right tool for resampling-based methods; the method parameter schema supplies some of this, but the tool description itself offers no routing.

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?

The annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds a cryptic 'Validation: certified parity evidence' note but does not explain what it means or what behaviors to expect beyond fitting. No contradiction with annotations is present.

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 short and front-loaded with the core purpose. The 'Validation: certified parity evidence' sentence is somewhat vague but still adds a distinct behavioral claim without bloating the 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?

The presence of an output schema and a fully documented input schema reduce the burden on the description. However, the description omits usage context, such as when AFT is preferable to similar survival models, leaving the agent to infer tool selection.

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 100%, and the parameter descriptions themselves are detailed. The tool description does not add parameter semantics beyond the schema, but with full schema coverage the baseline of 3 is appropriate.

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 verb and resource: 'Fit an Accelerated Failure Time model by MLE.' This clearly identifies the tool's purpose and method. It does not explicitly distinguish AFT from close survival-model siblings such as survreg or cox, which costs a point.

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?

There is no guidance about when to use this tool versus alternatives. Although many sibling tools include survival models and related diagnostics, the description never mentions when AFT is appropriate or when to prefer another tool. This leaves the agent without routing context.

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?

Beyond the annotations, the description discloses the multiplier bootstrap inference, unit-level clustering behavior, dynamic-balancing requirement, and failure modes involving missing influence functions. There is no contradiction with the read-only annotation.

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, then organized into clearly labeled sections: assumptions, preconditions, failure modes, alternatives, and typical minimum N. It is dense but well-structured, with each section adding actionable information.

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 18-parameter tool with an output schema, this description supplies the missing context an agent needs: assumptions, valid inputs, likely failure modes, alternatives, and an operating constraint. Nothing essential for correct invocation is missing.

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%, so the schema already documents every parameter. The description adds operational meaning beyond the schema by explaining what result.detail must contain, when balance_e is needed, and how min_e/max_e relate to empty event-time aggregation.

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: 'Aggregate Callaway-Sant'Anna group-time ATTs into interpretable summaries' and enumerates the four aggregation modes. It also names alternatives, so the tool is distinguishable from source estimators like sp.callaway_santanna.

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 gives explicit preconditions: the result must come from sp.callaway_santanna or sp.did with staggered=True and must contain per-(g,t) influence functions. It also documents failure modes and corrective actions, making when-to-use and alternatives clear.

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?

The readOnlyHint annotation already signals a safe read operation; the description adds valuable context beyond that: it states no refit occurs, no original data is needed, and it lists failure modes and validation tiers. It also explains the workflow linkage to influence_functions, giving the agent a clear mental model of side-effect-free operation. No contradiction with annotations.

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 dense but every sentence earns its place: it front-loads the core action, then adds pre-conditions, failure modes, alternatives, and a typical N in a compact, scannable format. There is no filler or redundant restating of the tool name. The structure uses clear separators (periods, hyphens) that aid parsing.

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, the description covers the essential context: pre-conditions, failure modes, alternatives, and typical sample size. An output schema exists, so return-value details are not needed. It leaves out explicit pagination or caching behavior, but the as_handle parameter description in the schema covers that, and the annotation covers safety. Overall, it is sufficiently complete for an agent to call it 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?

The input schema has 100% description coverage for all parameters, including the 'type' enum and 'source' format details. The description mentions the summary types and the source provenance, but this largely mirrors schema content. It does not add deeper semantics about parameter interactions or format specifics beyond what the schema already documents, so it holds at the baseline 3.

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 ('Aggregate Callaway-Sant'Anna group-time ATTs') and enumerates the exact summary types (event-study, group, calendar, overall) and inference method (multiplier-bootstrap). It names direct siblings (sp.aggte, sp.influence_functions) and distinguishes itself by stating 'no refit and no original data required', making the tool's scope unmistakable.

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 explicit pre-conditions ('source was produced by sp.influence_functions'), failure modes ('missing required columns -> Re-export'), and alternatives ('Alternatives: sp.aggte, sp.influence_functions'). It frames itself as 'the post-hoc half of the Stata csdid saverif() workflow', which clearly indicates when to use it. It stops short of an explicit 'do not use when...' clause, but the context strongly implies 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?

Beyond the readOnlyHint annotation, the description richly discloses behavior: cross-fitting, double robustness, formal assumptions (unconfoundedness, overlap, SUTVA), failure modes such as propensity scores near 0/1, and practical remedies like trimming or switching estimands. This gives the agent realistic expectations about when results may be unreliable.

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 dense but well-structured, front-loading the core definition before assumptions, pre-conditions, failure modes, and alternatives. The only mild excess is the generic 'Validation: validated evidence tier' sentence, which contributes little to invocation decisions.

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 13-parameter schema, output schema, and read-only annotation, the description covers everything an agent needs: estimand semantics, assumptions, preconditions, failure-mode handling, and alternatives. An agent can decide whether to call this tool and how to interpret borderline cases without further round-trips.

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%, so the baseline is 3, but the description adds meaningful parameter-level context: covariates must contain all confounders, treatment must be binary with both arms present, and overlap must not be badly violated. It also connects n_folds to the failure mode of very wide confidence intervals.

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 identifies AIPW as the canonical doubly-robust ATE estimator and explains its mechanism: cross-fitting an outcome regression and propensity model combined via the efficient-influence-function formula. This clearly distinguishes it from nearby siblings such as ipw, dml, tmle, and matching, and names alternatives explicitly.

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 gives clear usage context through pre-conditions, failure modes, typical minimum N, and an explicit alternatives list (sp.ipw, sp.dml, sp.tmle, sp.matching). It stops short of fully articulating when to prefer AIPW over each alternative, so it is not a complete decision rule, but the guidance is strong.

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?

Annotations declare readOnlyHint=true, so the agent knows this is a read-only computation. The description adds the 're-export of Validation: certified parity evidence' context, which is a useful behavioral note about provenance and reliability. However, it does not disclose what the confidence set is based on, how the grid is used, or what the output contains; the output schema presumably covers the return shape, so a 3 is fair.

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 a single compact sentence that front-loads the core purpose and adds a provenance note. It is not bloated, though it is terse enough that it could have included a bit more functional detail without becoming verbose.

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 14 parameters, 3 required, and a large sibling list, the description is too thin. It does not explain the statistical context (instrumental variables, weak-instrument robust inference), when the confidence set is appropriate, or how it relates to anderson_rubin_test. The output schema exists, so return values are covered, but the missing usage context is a significant gap.

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 100%, so the schema already documents all 14 parameters. The description adds no parameter-level meaning beyond the schema. Baseline 3 is appropriate because the schema carries the burden and the description does not need to repeat it.

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 names a specific statistical object (Anderson-Rubin confidence set) and notes it is a re-export of Validation with certified parity evidence. It does not explicitly say 'computes' or 'constructs', but the name plus 'confidence set' conveys the purpose. It is distinguishable from the sibling anderson_rubin_test, which is a test rather than a confidence set, though the description does not explicitly draw that contrast.

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 on when to use this tool versus alternatives such as anderson_rubin_test, liml, or ivreg. The description does not state conditions, exclusions, or prerequisites. The only hint is the name itself, which is not enough for an agent to choose among many IV-related 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?

The description adds that the test is size-correct under weak instruments and claims 'certified parity evidence' for validation, while readOnlyHint=true already covers the absence of side effects. It does not describe the returned statistic or decision rule, but the output schema is available and the safety profile is carried by the annotations.

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

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

The text is short and front-loads the main identification ('Anderson-Rubin test ... size-correct under weak instruments'). However, 'Validation: certified parity evidence' is a cryptic fragment that does little to help an agent select or invoke the tool, and the overall description is under-sized relative to the tool's 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?

Between the detailed schema and the one-line purpose, a knowledgeable agent can assemble the required inputs (data_path, y, endog, instruments) and infer this is a weak-instrument-robust IV test. Missing explicit guidance on when to prefer it over siblings such as 'anderson_rubin_ci' or 'ivreg' leaves clear gaps, but they are not fatal given the schema richness.

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 100%, so all 15 parameters are already documented in the input schema, including details like h0's null hypothesis value and cluster's robust quadratic form. The description itself adds no parameter-level meaning, so it sits at the baseline for schema-covered tools.

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 names the exact procedure ('Anderson-Rubin (1949) test') and gives a meaningful property ('size-correct under weak instruments'), so an agent can identify it as a weak-instrument-robust IV hypothesis test. It does not explicitly contrast it with the sibling 'anderson_rubin_ci' or other IV estimators, but the core purpose is reasonably clear.

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 phrase 'size-correct under weak instruments' is an implicit usage signal: use this test when weak-instrument robustness matters. However, it does not say when not to use it, name alternatives such as 'anderson_rubin_ci' or 'ivreg', or state prerequisites, so the guidance remains implied rather than explicit.

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?

Annotations provide readOnlyHint=true and openWorldHint=false, so the description is not burdened with declaring that fitting is a read-only computation. The description adds only the vague phrase 'certified parity evidence,' which does not clarify what is validated or against which reference. No contradiction with annotations 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 very short and front-loads the core purpose. However, the second sentence, 'Validation: certified parity evidence,' is cryptic and does not earn its place because it is not actionable or clearly explained.

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 15-parameter fitting tool with auto-search, exog, seasonal_order, method variants, and handle-based caching, the description is thin. The rich schema and output schema compensate for invocation details, but the description alone gives an agent little context about when this tool is the right choice or what 'certified parity evidence' means.

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 100%, so the parameter meanings are already fully documented in the schema. The description adds no parameter semantics beyond hinting at SARIMAX, which maps to the seasonal_order parameter already described. A baseline score of 3 is appropriate.

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 and resource: 'Fit ARIMA(p,d,q) or SARIMAX.' This is clear and distinguishes the tool from the many non-time-series siblings. However, it does not explicitly contrast with close model-fit siblings like garch or var, so it stops short of full differentiation.

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?

There is no guidance on when to use this tool versus alternatives, nor any indication of prerequisites or preferred workflows. The cryptic 'Validation: certified parity evidence' does not help an agent decide when to call arima instead of garch, var, or regress.

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?

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds no behavioral context beyond a vague promise of 'comprehensive' analysis, but it does not contradict annotations. It would be helpful to note that no model mutation occurs, but the annotation already does that implicitly.

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 a single sentence with no waste, but it is under-specified for a tool with 9 parameters. It fails to front-load key information such as what an assumption audit actually does, what types of inputs are accepted, or how the output is structured. Conciseness here reads as deficiency, not efficiency.

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 (9 parameters, extensive sibling list), the description is incomplete. It does not explain the core function, what 'result' means as an estimated model, or how this tool differs from other audit/diagnostic tools. The existence of an output schema is helpful, but the description still leaves an agent with insufficient understanding to invoke the tool 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 description coverage is 100%, so all parameters are documented with types, defaults, and constraints. The description adds no extra semantic meaning beyond the schema, which is acceptable given the baseline. It doesn't, for example, explain the relationship between 'result', 'data_path', and 'result_id', but the schema descriptions cover the basics.

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 names a clear verb+resource ('audit assumptions') and specifies 'any estimated model,' but it does not differentiate this from many sibling tools like 'audit' or 'diagnose_result.' It is not a tautology but lacks the specificity that would let an agent pick it among alternatives.

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 usage guidance is provided. There is no mention of when to use this tool vs alternatives like 'audit' or 'robustness_report,' nor any exclusions or prerequisites. The description offers no context beyond 'for any estimated model,' which is too broad for an agent to decide correctly.

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?

Annotations already declare readOnlyHint=true, and the description is consistent with that. The description adds value beyond annotations by specifying the behavioral output shape: a literature checklist with per-item status and a concrete suggested function for missing checks. This is useful context for an agent planning next steps.

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 tight sentences with no filler. The first sentence states the function and scope, and the second explains the output contract. Every sentence earns its place and the key idea is 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?

With an output schema present and rich parameter descriptions, the description does not need to restate parameter or return details. It is adequate but minimal: it lacks workflow context such as 'call after fitting a result' and does not distinguish audit from semantically overlapping siblings, leaving selection among similar tools partially unresolved.

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 100%, and the individual parameter descriptions (detail levels, as_handle caching, data_path formats, result_id, projection, sampling) already carry the semantic load. The description adds no parameter-level detail, but it does not need to compensate because the schema is thorough. Baseline 3 is appropriate.

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 a 'reviewer-grade audit on a result' and enumerates concrete checks (parallel-trends, honest-DID, Bacon decomposition, placebo, balance) plus the output form (status per item, suggested follow-up function). However, it does not explicitly differentiate itself from closely related siblings like audit_result, assumption_audit, diagnose, or robustness_report.

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 phrase 'audit on a result' implies the tool is used after a fitted result exists, and the mention of 'suggest_function to fill any missing high-importance check' hints at a follow-up workflow. But there is no explicit when-to-use guidance, prerequisites, or exclusion of alternatives, and the large sibling list makes this omission more costly.

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?

Annotations already signal a read-only operation, so the description adds useful behavior beyond that: it returns status values of 'present|missing|run' and suggested_function names for missing checks. It also indicates output scope ('every robustness check the literature expects'), giving the agent a clear expectation of payload content.

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 sentences, front-loaded with the core purpose, followed by the essential input requirement and return behavior. Every clause earns its place; there is no filler or repetition of schema content.

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 rich input schema, output schema, and readOnlyHint annotation, the description covers what the agent needs to invoke the tool correctly: the required handle, how to obtain it, and what the response will contain. The optional parameters are adequately documented in the 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?

Schema coverage is 100%, so the schema already documents all six parameters. The description adds workflow-critical meaning around result_id by instructing that it must come from an earlier call made with as_handle=true, which is not fully obvious from the schema alone.

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 verb ('audit'), a specific resource ('previously-fitted result'), and the required input ('result_id returned by an earlier tool call'). It distinguishes itself from a plain design audit by emphasizing that it operates on a fitted result and returns the same checklist sp.audit() produces with status flags and suggested functions.

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 gives an explicit precondition: pass the result_id produced by an earlier tool call with as_handle=true. This tells the agent when the tool can be invoked, though it does not explicitly name alternatives or state when not to use it (e.g., for a design not yet fitted).

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?

Beyond the readOnlyHint annotation, the description discloses modeling assumptions (SUTVA, no anticipation), preconditions, and failure modes such as large pre-treatment RMSPE and non-extreme placebo estimates. It does not contradict the annotations. The truncated final failure-mode sentence and the unexplained 'certified parity evidence' line prevent a 5.

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 organized with labeled sections and is mostly front-loaded, but it runs long and includes unhelpful or redundant content ('Validation: certified parity evidence', and suggesting augsynth within augsynth's own failure mode). It is also cut off mid-sentence at 'the effect...'.

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 estimator, the description covers assumptions, preconditions, failure modes, and alternatives; the output schema relieves it from describing return values. Completeness is slightly undercut by the truncated final sentence and the opaque validation line, but the core information an agent needs to call it correctly is present.

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 100%, so the baseline applies; most parameter descriptions are adequate and data_path is detailed. The tool description adds context about data shape (donor pool, pre-treatment window) but does not deepen the meaning of specific params like treated_unit or treatment_time beyond their schema 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 names a specific estimator ('Augmented Synthetic Control with ridge bias correction') with a citation, and contrasts it with plain synth and other panel estimators, so an agent can identify what the tool does. It lacks a direct action verb like 'estimate' or 'fit', and the 'Validation: certified parity evidence' line does not clarify purpose.

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 explicitly gives negative usage conditions ('Do NOT use when: pre-treatment fit ... is already good', 'many treated units') and names alternatives (sp.gsynth, sp.sdid). It also states assumptions and pre-conditions, making it clear when the tool is appropriate.

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 readOnlyHint=true and openWorldHint=false already provided, the description adds meaningful behavioral context: assumptions, preconditions, failure modes, and the leaderboard+winner output. It does not contradict the annotations, and the extra context about unstable CATE estimates and overlap support is genuinely useful.

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 labeled sections: purpose, validation, assumptions, preconditions, failure modes, alternatives, and minimum N. It is information-dense but every section earns its place, and the core purpose is 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 18-parameter complexity and the presence of an output schema, the description covers selection-relevant and invocation-relevant context: when it applies, what data it needs, what can go wrong, and how to react. Nothing essential for an agent to call it correctly is missing.

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%, so the schema already documents all 18 parameters thoroughly. The description adds only indirect parameter context by naming covariates, treatment, and outcome as preconditions. That meets the baseline but does not go beyond what the schema provides.

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 opening sentence states a specific verb ('Race'), resource ('meta-learners'), and deliverable ('scored leaderboard + winner'). This clearly distinguishes auto_cate from single-learner tools like metalearner or dml. The rest of the description reinforces the scope without blurring it.

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 gives clear context through assumptions, pre-conditions, failure modes, and a typical minimum N. It names alternatives (sp.dml, sp.causal_forest, sp.tmle), though it does not spell out exactly when to choose one over this tool or mention closely related siblings like auto_cate_tuned.

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?

Annotations already mark the tool read-only, and the description consistently adds behavioral context: required causal assumptions, cross-fitting, failure modes around instability/extrapolation, and validation tiers. This goes beyond the annotation without contradicting 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 compact and front-loaded with the core action, then uses labeled sections for validation, assumptions, pre-conditions, failure modes, alternatives, and sample size. A few phrases are slightly redundant (e.g., 'validated evidence tier'), but overall every section earns its place.

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 22 parameters and an output schema, the description covers the high-level conceptual context an agent needs: assumptions, preconditions, failure modes, and alternatives. It does not need to repeat return values because an output schema exists, though it could have been slightly more explicit about when to prefer this over auto_cate.

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 100%, so the schema fully documents all 22 parameters. The description only echoes the tune enum in its first sentence and adds no parameter-level detail beyond what the schema already provides, so the baseline 3 is appropriate.

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: an Optuna-tuned CATE learner race with three tuning regimes. This clearly identifies the tool's function, but it does not explicitly contrast it with the closest sibling auto_cate or with metalearner/compare_metalearners, so differentiation is left mostly to the word 'tuned'.

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 pre-conditions, assumptions, a typical minimum N of 500, and failure-mode guidance (e.g., restrict overlap, use a DR-/R-learner). It lists alternatives (sp.dml, sp.causal_forest, sp.tmle) but does not give decision rules for choosing among them, so it stops short of full when/when-not 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?

Annotations declare readOnlyHint=true, so the description doesn't need to cover safety. The description adds no behavioral context beyond the schema, such as how 'side by side' manifests in output or whether results are cached. With annotations already covering the safety profile and an output schema present, a neutral score is appropriate.

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 a single, focused sentence that front-loads the core purpose. It avoids fluff and is appropriately terse given the rich schema. It could be slightly more informative about usage, but for conciseness it earns a high score.

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 the presence of an output schema, the description is too brief for a tool with 14 parameters and multiple estimation methods. It fails to explain when to prefer this over running estimators individually, how to interpret the 'side by side' comparison, or what the 'agent' detail level entails. For such a complex tool, a one-sentence description is inadequate.

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 100%, so all 14 parameters are documented in the input schema. The description adds no additional parameter semantics beyond what the schema provides. Baseline 3 is correct when the schema carries the parameter documentation burden.

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 clear verb and resource: 'Run several staggered-DiD estimators side by side.' It conveys that the tool compares multiple estimators, which distinguishes it from single-estimator siblings like 'did' or 'staggered_cs'. However, it doesn't explicitly name those alternatives or the specific estimators (cs, sa, bjs) beyond what the schema provides, so it stops short of full differentiation.

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. It doesn't mention that it should be used when comparing multiple staggered DiD estimators, or that it supersedes running them individually. With dozens of DiD-related siblings, the lack of usage context leaves the agent to infer applicability from the schema alone.

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?

Annotations already declare readOnlyHint=true. The description adds no behavioral detail beyond that – it does not mention selection logic, caching via as_handle, or any side effects. Since the description carries some burden even with annotations, this sparse disclosure falls short.

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?

One concise sentence that is front-loaded with the core action. Every word contributes meaning; no filler or repetition. For a tool with a rich schema, this is appropriately terse.

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?

The schema provides thorough parameter documentation and there is likely an output schema, so the description can afford to be minimal. However, the key term 'Race' is undefined—what does running all three produce, and how does select_by decide? This leaves a meaningful gap in understanding the tool's full 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 description coverage is 100%, so the parameter descriptions already document each field. The description itself adds no extra parameter meaning; its reference to 'single-endogenous' aligns with the `endog` and `instruments` parameters but does not elevate beyond the schema baseline.

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 uses a specific verb ('Race') and clearly enumerates the estimators (2SLS, LIML, JIVE) and the target specification (single-endogenous IV). It differentiates itself from single-estimator siblings like `jive`, `liml`, and `ivreg` by signaling a combined comparison/selection tool.

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?

Usage is implied ('race' suggests comparing estimators), but there is no explicit guidance on when to prefer this over `ivreg` or `liml`, nor any conditions or exclusions. No alternatives are named, leaving the agent to infer the appropriate context.

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?

The readOnlyHint annotation already establishes this is a safe read operation. The description adds minimal behavioral context beyond 'returns a list', and there is no mention of output size, runtime, or whether any state changes occur. It does not contradict the annotations.

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

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

The description is a single, front-loaded sentence with no filler. Every word contributes to the core purpose, and it is appropriately sized for a simple registry-listing tool.

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?

The output schema covers return values and the annotations cover safety, so the simple core purpose is addressed. However, the six-parameter input schema includes data_path, data_sample_n, and result_id, which are not connected to the act of listing registered method names, leaving an agent unsure whether data-related arguments affect the returned list.

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 100%, so the input schema already documents all six parameters in detail. The tool description adds no parameter-specific meaning. With full schema coverage, a baseline of 3 is appropriate even though the description does not compensate further.

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 verb ('Return') and a precise resource ('list of all registered decomposition method names'). 'Decomposition' narrows the resource and differentiates it from the broad field of sibling tools, so an agent can understand the tool's role without opening the schema.

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 implies the tool is the go-to for discovering available decomposition methods, but it provides no explicit when-to-use/when-not-to-use guidance or alternatives. It does not mention that it should be called before selecting a method, nor how it relates to sibling tools like 'decompose' or 'methods_appendix'.

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?

Annotations include readOnlyHint=true, so the description does not need to restate that. It adds substantial behavioral context: assumptions (unconfoundedness, overlap, honesty), failure modes (calibration test rejects) with remediation steps, and validation tier. This exceeds the bar set by annotations.

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 with labeled sections (validation, assumptions, pre-conditions, failure modes, alternatives, typical N), is front-loaded with the core purpose, and every sentence contributes useful information without redundancy.

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 tool with 12 parameters and an output schema, the description covers purpose, assumptions, pre-conditions, failure modes, alternatives, and sample size guidance. An agent has sufficient information to decide when to call it and how to handle common issues.

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 100%, so all 12 parameters are already documented in the schema. The description does not add parameter-specific details beyond what the schema provides, so the baseline 3 is appropriate.

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 a specific verb (aggregate) and resource (CATE predictions into ATE/ATT/ATC/ATO targets), and distinguishes from siblings by naming alternatives. The purpose is unambiguous and agent-actionable.

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 context: pre-conditions (requires a forest) and typical minimum N, plus alternatives. It does not explicitly state when NOT to use this tool versus the alternatives, but the pre-condition implies you need a CausalForest, which is a clear usage signal.

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?

The annotations already establish readOnlyHint=true and openWorldHint=false, so the description does not need to restate safety. It adds useful behavioral context by warning about negative weights and by noting 'certified parity evidence,' which signals implementation validation. No contradiction with annotations.

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 compact and front-loaded: it states the method, the key output, the risk it addresses, and when to run it—all in three sentences plus a short validation phrase. Every sentence earns its place.

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 output schema, annotations, and standard DID parameter names, the description is largely complete for invoking the tool correctly. It is missing only explicit guidance about closely related sibling tools like bacon_plot or twfe_decomposition, and a direct parameter role mapping.

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 60%, so the four required parameters id, time, treat, and y lack explicit schema descriptions. The tool description provides methodological context that implies their roles but does not explicitly define them. This is adequate for a DID-savvy agent but not strongly compensating.

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 names a specific estimator (Goodman-Bacon 2021) and states precisely what the tool does: decomposes a TWFE-DID estimator into 2x2 comparison weights and exposes potentially negative treated-vs-treated comparisons. This clearly distinguishes it from generic decomposition or plotting 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?

It gives an explicit decision rule: 'Run this before trusting a TWFE-DID point estimate.' This tells the agent when the tool is appropriate. It stops short of a 5 by not stating when not to use it or naming an alternative such as bacon_plot/twfe_decomposition.

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?

Annotations already declare readOnlyHint=true, so the description carries little additional burden. However, it discloses nothing about what the scatter plot contains, whether it renders interactively, returns a figure object, or requires any side effects. It adds no behavioral context beyond what annotations imply.

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?

A single sentence with no filler, and the core resource is front-loaded. It is appropriately terse, though it borders on under-specification. Still, every word earns its place.

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 11 parameters, a required bacon_result object with specific required keys ('decomposition' DataFrame and 'beta_twfe'), and a workflow chaining from bacon_decomposition, this description is too thin. It does not explain what the plot shows, how to chain calls, or what the required input structure guarantees. The output schema helps with return values, but the usage context is largely missing.

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 100%, so all parameters are documented structurally. The description itself adds no parameter semantics beyond naming the bacon_result input, which is already present in the schema. Baseline 3 applies since the schema does the heavy lifting.

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 a specific verb ('Scatter plot') and resource ('Goodman-Bacon decomposition'), cleanly identifying it as the visualization of output from bacon_decomposition. It does not explicitly distinguish it from sibling plot tools, but the resource reference is sufficient to know what it operates on.

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 on when to use this tool versus alternatives. It does not mention that bacon_result should come from bacon_decomposition() first, nor does it contrast with other plotting tools like plot_from_result or did_plot. Usage is only implied by the resource name.

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?

The readOnlyHint annotation already covers the read-only safety profile, lowering the burden on the description. The description adds the estimator scope ('matching and weighting estimators') but does not disclose behavioral details such as what diagnostics are included, whether it is a composite call, or any side effects beyond the annotation.

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 a single concise sentence that names the tool's domain without filler. It is not verbose, though it may be too terse to fully orient an agent among the many sibling balance-related tools.

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?

The rich input schema and output schema carry substantial explanatory weight, and annotations confirm read-only behavior. However, for a tool with 12 parameters and many related siblings, the one-line description does not fully contextualize when this 'unified' diagnostic should be invoked versus alternatives like ps_balance or balance_panel.

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 100%, so the input schema already documents all 12 parameters in detail, including the meaning of 'weights', 'ps', 'method', and 'detail'. The description itself adds no parameter-level meaning beyond mapping the tool to matching/weighting contexts, so the baseline score of 3 is appropriate.

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 identifies the tool as providing 'balance diagnostics' for 'matching and weighting estimators', which clearly indicates its subject area. It lacks an explicit verb and does not differentiate itself from sibling tools like balance_panel, ps_balance, or balanceplot, so it cannot earn a 5.

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 phrase 'for matching and weighting estimators' gives an implied usage context: call this after matching or weighting-based estimation to check balance. However, there is no explicit guidance about when to choose this tool over sibling diagnostics, nor any when-not-to-use 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?

Annotations already declare readOnlyHint=true, and the description consistently conveys a read-only filtering operation. It adds the specific rule about time periods, but the 'Validation' clause is unexplained, leaving ambiguity about what evidence is required or produced.

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 short and front-loads the primary purpose in its first sentence. The second sentence is cryptic but does not add length or complexity that obscures the purpose, so it earns a concise structure score.

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?

While the schema is rich and an output schema exists, the description fails to provide usage context or explain the validation clause. An agent selecting among many panel-related tools has no cues for when this tool is appropriate, leaving the description 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?

Schema description coverage is 100%, so each parameter is fully documented in the schema. The description contributes no additional parameter meaning, leaving the baseline of 3 appropriate.

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 verb ('balance') and resource ('panel') with a concrete method ('keeping only units observed in every time period'). This clearly distinguishes it from siblings like balance_diagnostics or balanceplot.

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. It does not mention typical workflows, prerequisites, or exclusions. The cryptic 'Validation: certified parity evidence' offers no contextual help either.

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?

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds one useful behavioral detail: it produces a dot plot of standardized mean differences. It does not describe output or side-effect behavior further, but the annotations and output schema carry most of that burden.

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 a single compact sentence with no filler words; it front-loads the core purpose and visualization format. It is concise without being padded, though it is slightly too terse to carry full usage 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?

The structured fields are rich: full parameter descriptions, an output schema, and read-only annotations. The main gap is prose context about the match()/ebalance() workflow and how to choose this over love_plot, which the schema cannot fully compensate for.

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%, so all 11 parameters already have descriptions. The tool description adds no parameter-level semantics, which is acceptable under the high-coverage baseline.

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 identifies the tool as a covariate balance visualization and specifies the x-axis metric as SMD, making it distinct from generic plotting or regression tools. However, it does not differentiate balanceplot from the sibling love_plot, which appears to serve the same purpose.

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 gives no explicit guidance on when to use this tool, and it does not mention alternatives such as love_plot or balance_diagnostics. The phrase 'covariate balance visualization' only implies a post-matching/weighting diagnostic context without saying so.

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?

The annotations declare readOnlyHint=true, so the agent knows this is a read-only estimation operation. The description adds substantial behavioral context beyond the annotations: it discloses the asymptotic framework (many shocks, L -> inf), the minimum sample size requirement (>=50 regions), and specific failure modes with their consequences. It doesn't describe the output format in detail, but the output schema exists and covers that. The only minor gap is not explicitly stating that this is a read-only operation, but the annotation covers that.

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 dense and information-rich, covering assumptions, pre-conditions, failure modes, and alternatives in a compact format. It's front-loaded with the core definition. However, it's quite long and the 'Typical minimum N:...' sentence is cut off, which is a minor structural flaw. The density is justified by the complexity of the tool, but the truncation 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?

For a complex econometric estimator, the description is remarkably complete: it covers the estimator's mechanism, assumptions, pre-conditions, failure modes with remedies, and alternatives. The output schema exists, so return values are documented. The only missing piece is the truncated 'Typical minimum N' sentence, but the >=50 regions condition is already stated. An agent has everything needed to decide whether to use this tool and how to interpret its 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?

Schema description coverage is 100%, so the schema already documents all 11 parameters. The description adds context for the key parameters (shares, shocks, endog, y) by explaining the econometric mechanism, but doesn't add syntax or format details beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 identifies the tool as a Bartik/shift-share IV estimator, names the specific econometric references (Adao-Kolesar-Morales 2019; Borusyak-Hull-Jaravel 2022), and explains the core mechanism: pre-period shares x exogenous shocks as an instrument for local outcome exposure. This is a specific verb+resource combination that distinguishes it from the many sibling 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?

The description provides extensive usage guidance: explicit assumptions (exogeneity of shocks, shock-level IV, many shocks asymptotic framework), pre-conditions (pre-determined shares, as-good-as-random shocks, >=50 regions), and failure modes with specific remedies (Herfindahl concentration -> use sp.shift_share_se; weak first stage -> sp.anderson_rubin_ci; pre-trend correlation -> drop shock or add controls). It also names alternatives (sp.iv, sp.shift_share_se, sp.shift_share_political, sp.shift_share_political_panel). This is exemplary.

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?

The annotations already declare readOnlyHint=true, and the description adds only that the dataset is 'simulated', which is a property of the data, not the tool's behavior. It does not disclose what happens when the tool is called, how the dataset is accessed, or any side effects beyond the read-only guarantee.

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 a single short sentence with no redundant words. It is efficiently front-loaded with the dataset name and the simulated nature. However, its brevity borders on under-specification, though not for lack of conciseness per se.

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 having six optional parameters and a rich output schema, the description provides almost no operational context. It does not explain what the tool actually does (e.g., returns a dataset, supports subsetting) or how the parameters relate to the dataset, so an agent cannot predict the tool's role within the large sibling set.

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 provides detailed descriptions for all six parameters (100% coverage), including enums, defaults, and explanations. The description adds no parameter-level information, so it does not compensate for anything missing in the schema, matching the baseline of 3.

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 identifies a specific resource ('Basque Country terrorism dataset'), which distinguishes it from the statistical analysis siblings, but it lacks a verb or action. It does not state whether the tool loads, previews, or processes the data, leaving the agent to infer the tool's function from its name alone.

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 is provided on when to use this tool versus alternatives. There is no mention of use cases, exclusions, or relationship to sibling tools, so an agent receives no help in selecting 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the read-only safety profile is covered. The description adds that the decomposition includes validation and 'certified parity evidence,' implying the output contains a parity-validation component, but it does not clarify what that evidence is or how it appears in the result. With annotations carrying the safety burden, the additional context earns partial but not full credit.

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 a single dense sentence with no filler, and the method name plus the distinguishing validation feature are front-loaded. It would be a 5 with an explicit functional verb (e.g., 'Estimates...' or 'Decomposes...'), but it remains efficient and easy to scan.

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 a rich input schema (100% parameter coverage), output schema, and safety annotations, the description does not need to restate parameters or returns. However, for a complex estimator with many decomposition siblings, the missing 'when to use' context and the ambiguous 'certified parity evidence' leave selection partially to inference, so the description is minimally adequate but not complete.

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 100% description coverage across all 12 parameters, so the schema already documents parameter meaning and the description need not repeat it. The description itself adds no parameter-level detail, which is acceptable under the baseline but yields no extra credit.

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 identifies a specific estimator (Bauer-Sinning 2008 nonlinear Oaxaca-Blinder decomposition) and a distinguishing feature ('Validation: certified parity evidence') that separates it from generic oaxaca or yun_nonlinear. However, it lacks an explicit verb and a plain-language statement of what the tool returns, so it does not fully earn a 5.

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 gives no guidance on when to prefer this tool over similar decomposition tools (oaxaca, melly_decompose, yun_nonlinear, fairlie) or what conditions make the validation/certified-parity feature necessary. The phrase 'with Validation' hints at a niche but never states exclusions, prerequisites, or 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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the read-only nature is known. The description adds valuable behavioral context beyond that: it discloses failure modes (MCMC non-convergence, prior sensitivity), preconditions (internal propensity model to limit confounding), and guidance for remediation (increase draws, re-scale outcome, report diagnostics). This is strong additional transparency, though it could also mention the output format or side effects, but since output schema exists, a 4 is appropriate.

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 a single dense paragraph that front-loads the purpose and then systematically covers validation, assumptions, pre-conditions, failure modes, alternatives, and typical N. Each sentence serves a purpose. While packed, it is organized and efficient, earning a 4 rather than a 5 because it is somewhat dense and could be slightly more scannable with bullet points.

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 (15 parameters, Bayesian estimation), the description covers essential context: assumptions, preconditions, failure modes, alternatives, and sample size guidance. An output schema exists, so return values are covered. It does not mention specific details like expected runtime or data formatting beyond what schema lists, but for the agent to call it correctly, the description is largely sufficient. A 4 is justified.

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 covers 100% of parameters with descriptions, so the baseline is 3. The tool description does not add parameter-specific detail beyond what the schema already provides (e.g., it doesn't explain nu_mu vs nu_tau semantics). However, the schema descriptions are adequate on their own. The description adds contextual meaning about the overall method but not per-parameter, so 3 is correct.

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 verb and resource: 'Estimate heterogeneous treatment effects using Bayesian Causal Forest.' It clearly distinguishes BCF from sibling tools like sp.dml, auto_cate, and causal_forest by naming them as alternatives. The purpose is unambiguous and actionable.

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 pre-conditions (covariates, treatment, outcome), assumptions (unconfoundedness and overlap, prior appropriateness), and names alternatives (sp.dml, sp.auto_cate, sp.causal_forest). It also gives a typical minimum N (250), which helps the agent decide when the tool is appropriate. This is clear and actionable 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?

Annotations declare readOnlyHint=true and openWorldHint=false. The description goes well beyond these by disclosing failure modes (MCMC diagnostics failing, prior sensitivity), validation tiers, internal propensity model fitting, and diagnostics recommendations. It also mentions the compression via SVD or loadings, which is a behavioral detail not in annotations. No contradiction with annotations.

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 labeled sections (Validation, Assumptions, Pre-conditions, Failure modes, Alternatives, Typical minimum N). It is concise for the complexity, each sentence adds value, and the core purpose is front-loaded. No wasted words.

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?

The description covers purpose, validation, assumptions, pre-conditions, failure modes, alternatives, and typical sample size. Given the tool's complexity (14 parameters) and that an output schema exists, this is complete for an agent to decide when to call and what to expect. It does not explain return values, but the output schema covers that.

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 100%, so all parameters have descriptions. The tool description adds minor context: it mentions 'user-supplied loadings' and 'SVD' which clarifies the 'loadings' parameter, and 'PCA-factor scores' clarifies 'n_factors'. However, it does not deeply elaborate on each parameter beyond what the schema provides, so the baseline of 3 is appropriate.

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 purpose: it applies BCF to PCA-factor scores of a high-dimensional exposure vector, using SVD or user-supplied loadings, then fits one BCF per factor. This distinguishes it from siblings like bcf, bcf_longitudinal, and bcf_ordinal, which likely handle different structures. The verb 'fits' and resource 'BCF' are specific.

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 pre-conditions (covariates, treatment, outcome), assumptions (unconfoundedness, overlap), and typical minimum N. It lists alternatives (sp.dml, sp.auto_cate, sp.causal_forest) but does not explicitly contrast with the bcf family (e.g., when to use bcf vs bcf_factor_exposure). This is a minor gap but the intended use case is implied by the title.

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?

Beyond the readOnlyHint annotation, the description discloses that 'a propensity model is fit internally to limit regularization-induced confounding' — an internal behavior that affects how estimates should be interpreted — and lists concrete failure modes with remediation: 'MCMC diagnostics fail to converge... -> Increase draws/tuning, re-scale the outcome, and report posterior diagnostics.' This is substantial behavioral context that annotations alone do not provide. No contradiction with annotations.

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?

Roughly 90 words organized into labeled sections (Assumptions, Pre-conditions, Failure modes, Alternatives, Typical minimum N), with the model identity front-loaded. Every section earns its place and the structure is highly parseable for an agent. The only arguable waste is the sp.*-prefixed alternative names, which look inconsistent with the sibling namespace.

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 Bayesian tool with an output schema and fully-described parameters, the description covers the modeling context — assumptions, pre-conditions, internal behavior, failure modes with remediation, alternatives, and minimum sample size. The 'Typical minimum N: 250' guidance is slightly underspecified (units vs. observations) and alternative routing is thin, but nothing critical for invoking the tool correctly is missing.

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 100%, so the baseline of 3 applies; every parameter already carries a description in the schema. The tool description adds no parameter-level detail — the hint to 'Increase draws/tuning' refers conceptually to the n_trees_*/n_bootstrap tuning parameters but never names or maps them to specific schema fields.

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 definition — 'Hierarchical Bayesian Causal Forest for longitudinal data (BCFLong)' — and states exactly what the model does: 'allows mu_t(X), tau_t(X) to evolve across time with unit-level random intercepts.' The 'longitudinal data' qualifier differentiates it from sibling BCF variants (bcf, bcf_ordinal, bcf_factor_exposure) without requiring the agent to open their schemas.

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 gives applicability context — assumptions (unconfoundedness, overlap), pre-conditions (covariates, treatment, outcome), and a 'Typical minimum N: 250.' It names alternatives (sp.dml, sp.auto_cate, sp.causal_forest) but attaches no selection conditions; there is no explicit statement of when to prefer this tool over bcf or causal_forest, and no exclusions. Usage is implied rather than stated.

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?

Annotations declare readOnlyHint=true, so the agent knows this is a read-only estimation call. The description adds valuable behavioral context beyond annotations: it discloses that a propensity model is fit internally to limit regularization-induced confounding, that MCMC diagnostics may fail to converge, and that estimates may be prior-sensitive. It also describes the chained-BCF estimation strategy. It does not detail output structure, but the output schema exists and the detail parameter covers payload depth.

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 dense but well-organized with labeled sections (Assumptions, Pre-conditions, Failure modes, Alternatives, Typical minimum N). Every sentence carries information. It is somewhat long, but the complexity of a BCF tool with MCMC diagnostics justifies the length. The most important identifying information (ordered/dose-level, cumulative dose-response) is 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 tool's complexity (16 params, MCMC, BCF), the description covers the key contextual needs: what it estimates, assumptions, pre-conditions, failure modes, alternatives, and sample size guidance. The output schema and detail parameter handle return-value expectations. It could be more complete with explicit guidance on which parameters to tune when diagnostics fail, but the failure-mode section already points to draws/tuning and outcome rescaling.

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 100%, so the schema already documents all 16 parameters. The description adds context for the treatment variable (ordered/dose-level) and mentions internal propensity fitting, which helps interpret treat and covariates. However, it does not add meaning beyond the schema for most parameters (e.g., n_trees_mu, n_trees_tau, n_bootstrap, alpha, baseline). Baseline 3 is appropriate given full schema coverage.

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 identifies the tool as a Bayesian Causal Forest for ordered/dose-level treatment, specifies the method (chained BCF between consecutive levels), and states the estimand (cumulative dose-response curves). It distinguishes itself from siblings like bcf, bcf_factor_exposure, bcf_longitudinal, and causal_forest by the ordered/dose-level 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 provides explicit pre-conditions (covariates, treatment, outcome; propensity model fit internally), assumptions (unconfoundedness, overlap, BART priors appropriate), and failure-mode guidance (increase draws/tuning, re-scale outcome, report diagnostics). It names alternatives (sp.dml, sp.auto_cate, sp.causal_forest) and gives a minimum N of 250. It does not explicitly state when NOT to use this tool versus those alternatives, but the context is strong.

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?

Annotations declare readOnlyHint=true, so the agent knows this is a read-only calculation. The description adds little behavioral context beyond naming the procedure; it doesn't disclose output shape, assumptions, or side effects. Since the annotation already covers the safety profile, a 3 is appropriate.

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 short sentences and contains no wasted words. The core identity is front-loaded; the validation note is second. It's appropriately minimal for a self-explaining statistical procedure.

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 FDR-correction function with only one required parameter (pvalues) and full schema coverage, the description plus schema are mostly sufficient. The main gap is that it doesn't relate to or distinguish the tool from the multiple-adjustment siblings (bonferroni, holm, romano_wolf), and it doesn't mention whether adjusted p-values are sorted or returned in original order. But output schema exists and the operation is a narrow read-only calculation.

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 100%, so all parameters are already documented in the schema. The description adds no parameter-specific meaning, but with full coverage the baseline of 3 applies. The detail parameter in the schema already explains payload depth options well.

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 names the Benjamini-Hochberg (1995) FDR correction, which is a specific verb+resource combination. It is distinguishable from siblings like romano_wolf, holm, and bonferroni because it names the exact procedure. However, it doesn't explicitly contrast with those FDR-adjustment siblings, so it loses the fifth point on sibling differentiation.

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 states what the tool is but not explicitly when to use it compared to alternatives. The 'Validation: certified parity evidence' line hints at a use case (validated parity with an established implementation) but doesn't say where it fits among the many multiple-testing siblings. The detail parameter does explain when different payload depths are appropriate, providing a moderate usage signal.

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?

The annotations declare readOnlyHint=true and openWorldHint=false, so the tool is known to be read-only. The description adds only a validation claim ('certified parity evidence') but discloses no behavioral aspects such as computational cost, assumptions, or response structure. It does not contradict annotations, but adds negligible behavioral transparency beyond them.

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 extremely brief (two short sentences), but the brevity is under-specification rather than efficient conciseness. The first sentence simply restates the tool's name in a verbose form, and the second is a vague validation claim. It does not provide essential information in a structured or front-loaded manner.

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 15 parameters and no required ones, the description is grossly inadequate. It does not explain the overall workflow, expected inputs, output format, or how it relates to similar tools. Despite the schema having descriptions, the high-level context is missing, leaving an agent unable to determine when or how to invoke it effectively.

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 100% description coverage for all 15 parameters, so the schema already documents each parameter. The description adds no additional meaning or context about parameters, such as the role of 'x', 'y', and 'z' or the 'detail' levels. Given the high schema coverage, a baseline of 3 is appropriate, but the description does not enhance understanding beyond 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 performs beta regression and cites the reference (Ferrari & Cribari-Neto 2004). It identifies the method and its intended use for bounded outcomes. However, it does not differentiate from other regression siblings like 'glm' or 'logit', relying on the name alone to imply the distinction.

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?

There is no guidance on when to use beta regression versus alternatives. The description does not mention conditions such as outcome in (0,1) or when to prefer it over other models, nor does it exclude any scenarios. An agent would have to infer from the name and schema that it is for bounded continuous outcomes.

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?

The description discloses failure modes (quantile crossing, unstable tail quantiles), assumptions (unconfoundedness, instrument validity, rank invariance), pre-conditions, and a typical minimum N of 500. This goes well beyond the readOnlyHint annotation, which only signals no side effects. No contradiction with annotations.

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 labeled sections (Validation, Assumptions, Pre-conditions, Failure modes, Alternatives, min N) and front-loads the purpose. However, the Validation sentence ('validated evidence tier (known-truth, reference, external-parity, or Monte Carlo artifact)') is somewhat boilerplate and not directly actionable for invoking the tool, so it doesn't fully earn its place.

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 econometric tool, the description covers the full decision surface: purpose, assumptions, pre-conditions, failure modes, alternatives, and data-size guidance. The output schema exists, so return-value details are secondary. Nothing an agent needs to select and invoke the tool correctly is missing.

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 100% description coverage, so the schema carries the burden. The description does not add parameter-level detail—it only lists generic pre-conditions (covariates, treatment, outcome, instrument) without mapping them to schema fields. The weak schema entry for 'instrument' (just 'instrument parameter (str).') is not compensated by the description.

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 clear verb+resource: 'Identifies the entire treatment-effect distribution among compliers under incomplete compliance, not just its mean.' It names the specific method (Xie-Wu 2025) and explicitly contrasts with the mean-based LATE, which distinguishes it from standard LATE tools and sibling quantile-treatment-effect 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?

The description ends with 'Alternatives: sp.qte, sp.iv, sp.dml', explicitly routing an agent to sibling tools. It also gives assumption-based selection guidance ('Selection-on-observables... or, for IV variants, instrument validity') and failure-mode remediation ('Use rearrangement / monotonization'), so an agent knows both when and when not to use this tool.

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?

The readOnlyHint annotation already covers the safety profile, so the description does not need to belabor non-mutation. The description adds conceptual context by naming the Ding & VanderWeele bias factor, but it does not disclose behavioral details such as formula interpretation, numerical range, or how optional data/result parameters interact with the required rr_eu and rr_ud arguments.

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 very short and free of filler, which is efficient. However, for a tool with 8 parameters and a statistical concept, a single noun-phrase fragment is under-specified rather than ideally concise; it does not provide the structure an agent needs to use the tool safely.

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?

The description is incomplete relative to the tool's context: it omits usage guidance, fails to explain how the optional data_path/result_id parameters relate to the two required risk-ratio inputs, and gives no interpretive context for the returned bias factor. The output schema may cover return shape, but the surrounding call context is still too sparse.

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 100%, so the baseline is 3 even though the parameter descriptions are thin ('rr_eu parameter (float)'). The description itself adds no semantic meaning beyond the formula reference; rr_eu and rr_ud remain cryptic and are not connected to the bias-factor calculation.

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 identifies the object as the confounding bias factor B and cites Ding & VanderWeele (2016), making the core purpose reasonably clear. However, it uses a noun phrase rather than a verb like 'computes/returns, and it does not differentiate this tool from nearby sensitivity siblings such as sensitivity_rr, evalue_rr, or sensitivity_from_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?

There is no guidance on when to use this tool versus alternative sensitivity analyses, and no mention of what inputs are expected beyond the schema. The description does not explain whether rr_eu and rr_ud are risk ratios, nor does it give any exclusion or alternative-selection hints.

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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so the description adds no behavioral nuance. It doesn't mention the caching behavior via as_handle, the effect of detail levels, or any side effects. Since annotations carry the safety profile, the description fails to add beyond that.

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 a single sentence, which is concise and front-loaded, but it is too sparse to be useful for an agent. For a tool with seven parameters and meaningful choices (detail levels, as_handle), a bit more guidance would be warranted without becoming verbose. It is not wordy, but it under-delivers.

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 many parameters and the sibling context (e.g., bibtex), the description is incomplete. It doesn't explain when to use this vs. bibtex, nor the significance of detail levels or as_handle. While an output schema exists (so return values needn't be described), an agent still lacks guidance on invocation choices.

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?

All seven parameters are described in the schema (100% coverage), so the baseline is 3. The description adds no information about parameters itself, but the schema does the heavy lifting. The description's brevity doesn't detract from parameter 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 states a clear action: providing a structured citation for a fitted result. It identifies the resource (fitted result) and the output (citation). However, 'top-level' is ambiguous and doesn't distinguish well from sibling tools like bibtex, which may also generate citations. Still, it conveys the core purpose unambiguously.

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?

There is no guidance on when to use this tool versus alternatives (e.g., bibtex, brief_result). No mention of entry conditions, required prior steps, or when the different detail levels would be appropriate. The description is purely a statement of what it does, lacking any 'when' or 'when not' context.

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?

Annotations already declare readOnlyHint=true, so the safety profile is known. The description adds valuable behavioral context: it confirms it returns verified entries, warns against inventing citations, and specifies that unknown keys produce an empty entry plus close matches. This goes beyond the annotations and helps the agent handle edge cases.

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 zero filler. The core purpose and usage are front-loaded, the warning about inventing citations is prominent, and the unknown-key behavior is stated concisely. Every sentence earns its place.

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 lookup tool with an output schema (not shown) and read-only annotations, the description covers the essential aspects: what it returns, how to invoke it (with an example), and the edge-case behavior. Nothing an agent needs to call it correctly is missing.

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%, so the schema already documents all parameters. The description adds an example bib key ('callaway2021difference') and clarifies that multiple keys can be passed, which reinforces the keys parameter. It does not explain the other parameters (detail, as_handle, etc.), but they are well-documented in the schema, so the description adds marginal value.

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 specific verb ('Return'), a precise resource ('verified BibTeX entries from paper.bib'), and includes an example key. It is unambiguously distinct from the statistical siblings; the only related tool, bib_for, is not mentioned, but the description's citation-specific scope makes it stand apart.

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 gives a strong usage directive: 'NEVER invent citations -- call this tool instead.' This tells the agent exactly when to use it (whenever a citation is needed) and implies when not to (do not fabricate). It also mentions the behavior for unknown keys, which guides error handling. It does not name an alternative tool, but the context is clear.

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?

Annotations already mark readOnlyHint=true, and the description adds non-obvious context: proxy validity and completeness assumptions, failure modes for weak/invalid proxies, an evidence tier, and a minimum N warning. This goes well beyond the annotations and helps an agent anticipate poor identification.

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 organized into focused sections (validation, assumptions, pre-conditions, failure modes, alternatives, N) with no repetition of schema fields or filler. The core identification is front-loaded and each section earns its place.

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 estimator, it supplies assumptions, pre-conditions, failure modes, alternatives, evidence tier, and sample-size guidance, and an output schema exists so return details need not be repeated. The main gap is the missing explicit W/Z role mapping and a more precise contrast with the proximal/proximal_regression siblings.

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%, so the baseline is 3. The description adds meaning to the proxy parameters by defining them as treatment-inducing and outcome-inducing negative controls relevant to the confounder and excluded from the causal channel. However, it never explicitly maps proxy_w to treatment-inducing and proxy_z to outcome-inducing, which prevents a higher score.

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 method ('Bidirectional proximal causal inference (Min, Zhang & Luo 2025)') and a precise action: solves both outcome and treatment bridges simultaneously in a two-way regression system. This distinguishes it from univariate proximal regression, select_pci_proxies, and DML even without opening the schema.

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?

Pre-conditions state when the method is applicable (valid negative-control proxies, bridge function/completeness), failure modes say what to do if assumptions fail, and the Alternatives line points to sp.select_pci_proxies and sp.dml. It lacks an explicit 'use X instead when Y' contrast, so it is not a full 5.

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 readOnlyHint=true in annotations, the safety profile is already known, so the description does not need to restate that. However, the description adds no useful behavioral context: it does not explain what 'certified parity evidence' means, what the fitting process returns, or any assumptions or side effects. There is no contradiction with annotations.

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 very short, but this is under-specification rather than effective conciseness. It contains no front-loaded action, no selection-relevant details, and the 'Validation' fragment is vague and does not earn its place.

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 15-parameter estimator with multiple optional features (x2, cluster, robust, detail, as_handle), the description is far too thin. The rich schema covers parameters, but the description leaves the tool's purpose, relationship to siblings, and validation semantics unexplained.

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 100%, with every parameter already documented in detail. The description itself contributes nothing beyond the tool name, so the baseline of 3 is appropriate: the schema carries the semantic burden.

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 only restates the tool name ('Bivariate probit model') without an explicit action verb like 'estimate,' 'fit,' or 'test.' It does not distinguish biprobit from the sibling 'probit' tool or other discrete-choice estimators, leaving the agent uncertain about the tool's exact role.

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?

There is no guidance on when biprobit should be chosen over alternatives such as probit, panel_probit, or ivreg. The cryptic phrase 'Validation: certified parity evidence' does not explain use conditions, prerequisites, or when not to use this tool.

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