DenialPath

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

The description contradicts the openWorldHint annotation. openWorldHint=false implies a closed world where absence of data is conclusive, but the description explicitly warns that no_data is NOT a pass and that CMS may still deny under edits outside the loaded subset. This is an open-world behavior, directly conflicting with 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 longer than average but earns its length by explaining the nuanced status values and the no_data caveat. It is front-loaded with the main verb and resource, and every sentence adds necessary behavioral detail. Slightly verbose but justified.

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

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

Despite no output schema, the description thoroughly explains the response semantics: each finding.status, summary.clean rules, and the echoed editQuarter/coverage fields. It also covers edge cases (no_data not a pass) and constraints (free, rate limit). This is a complete picture for an agent to invoke and interpret results.

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

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

Schema coverage is 100%, so the schema already documents all parameters (dateOfService, lines, payerSlug). The description adds some context (e.g., dateOfService selects the edit quarter, modifiers resolve bypassed), but it does not significantly enhance parameter meaning beyond the schema. 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 verb and resource: 'Scrub a claim against the NCCI procedure-to-procedure (PTP) edits and medically unlikely edits (MUE)'. This clearly differentiates from siblings like get_ncci_edits, get_mue, explain_denial, etc., which focus on retrieving edits or explaining denials rather than checking a full claim.

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

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

The description provides clear context about when the tool is relevant (checking a claim against NCCI edits) but does not explicitly state when NOT to use it or recommend alternatives. The deterministic and free notes are usage benefits, but no direct comparison to sibling tools is made.

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 only indicate idempotency and non-destructiveness. The description adds substantial behavioral detail: deterministic assembly, access control, the 'never fabricates' guarantee, explicit placeholders for missing facts, and the 'grounded' flag semantics. This greatly exceeds annotation coverage.

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 long but every sentence carries essential constraints—purpose, plan requirement, placeholder behavior, and grounded semantics. It front-loads the core function and then logically layers edge cases. Slightly verbose but well-structured.

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 8 parameters, no output schema, and access-control nuance, this description is exceptionally complete. It covers required plan, error behavior, deterministic assembly, placeholder rules, billing-only scope, and the meaning of the 'grounded' flag—all relevant for invocation. No gaps remain.

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 descriptions cover 100% of the 8 parameters, so baseline is 3. The description adds critical semantics beyond schema: it explains how missing optional fields become '[TO BE COMPLETED BY PRACTICE]' placeholders and that codes are numeric reference only. This elevates the score to 4.

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: 'Generate an appeal letter for a denial, assembled deterministically from cited facts'. This clearly differentiates it from sibling tools like explain_denial or check_claim, which have different actions and outputs.

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

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

Explicitly states the Pro/Scale plan requirement including the upgrade_required error for unauthorized callers, giving clear access context. It doesn't name sibling alternatives, but the purpose is distinct enough that when-to-use is implied without confusion.

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 as read-only, idempotent, and non-destructive. The description adds meaningful behavioral detail beyond that: how unknown CARCs are handled (found:false), that unknown RARCs are reported rather than dropped (unknownRarc), the group-prefix fallback for group meanings, and the payer-dependent appeal deadline attachment. It also clarifies that no API key is needed, which is operational context not in 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 front-loaded with the core purpose, then efficiently details edge cases. It is somewhat dense, but each clause earns its place—no filler or repetition. The only mild issue is the slightly convoluted 'found:false' sentence, but it remains compact and informative.

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 moderate complexity (3 params, no output schema), the description covers essential usage aspects: inputs, outputs, edge cases, and operational requirements (free, no API key). It provides enough for an agent to select and invoke the tool correctly, including what happens when codes are 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 baseline is 3. The description enriches the parameters by explaining that carc accepts a group prefix, that rarc's unknownRarc behavior reports missing codes, and that payer affects appeal deadline retrieval. This goes beyond the schema's basic type/description while not diving into exhaustive syntax.

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: 'Explain a CARC... and any RARCs from a remittance' and enumerates concrete outputs: plain meaning, ranked corrective actions, and appeal worthiness. This clearly distinguishes it from siblings like check_claim, get_ncci_edits, or get_timely_filing, which address different facets of claim processing.

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 context of use is clear—when you need to interpret a denial from a remittance—but it does not explicitly contrast with alternatives or state when not to use it. The note about optionally passing payer to attach appeal deadlines hints at integration with appeal workflows without excluding any sibling tools.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds substantial behavioral context: exact status values ('found' vs 'no_data') with interpretation, the three MAI adjudication indicators with billing consequences (including 'not even on appeal' and 'may be allowed above the limit with supporting documentation'), and access requirements. This goes well beyond annotation coverage.

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

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

Four sentences, each conveying distinct, valuable information: core purpose, status semantics, MAI meanings, and free access. It is dense but not wordy, and front-loaded with the main purpose. The MAI explanation is lengthy but essential for interpretation.

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 no output schema, the description carries the full burden of explaining return behavior. It covers the status field, the meaning of no_data, the critical limit.mai field and its three variants, and the free access. For a single-parameter lookup, this is highly 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?

Schema covers the only parameter 'code' with a description and example ('CPT/HCPCS code, e.g. "36415"'). The description reiterates 'single CPT/HCPCS code' but adds no new format, constraints, or usage details beyond the schema, so baseline 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 opens with a specific verb and resource: 'Look up the medically unlikely edit (MUE) unit limit for a single CPT/HCPCS code.' It clearly distinguishes from sibling tools like get_ncci_edits by naming MUE rather than NCCI edits and emphasizing single-code scope.

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

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

Provides clear context for when to use the tool: for MUE unit lookup on a single code, and notes it is free with no API key. It does not explicitly name alternatives or state when not to use, but the purpose is unambiguous and the sibling set makes the distinction obvious.

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 read-only, idempotent, non-destructive, and closed-world behavior. The description adds substantial context beyond annotations: it explains the meaning of status values (no_data, not_applicable, applies) and modifierIndicator values ('0' vs '1'), including the nuance that no_data is dataset-specific, not CMS confirmation. This aligns perfectly with openWorldHint=false and adds value.

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

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

The description is concise yet comprehensive, front-loaded with its purpose. It structures the two modes clearly, uses bullet-like separation via 'Pair mode' and 'List mode,' and every sentence contributes essential details (status semantics, modifier meanings, free/no-key note). No redundancy or filler.

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

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

Despite having no output schema, the description fully covers behavioral context: both modes, status values, modifier indicators, the closed-world dataset nuance, and pricing/access. It leaves no major ambiguity for an AI agent selecting or invoking the tool correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds some contextual meaning—e.g., column order does not matter and why dateOfService is required—but most of this is already present in the schema descriptions. No substantial new parameter semantics are introduced 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 description begins with a specific verb and resource: 'Look up NCCI procedure-to-procedure (PTP) edits.' It immediately distinguishes two modes (pair and list), each clearly defined. This effectively separates it from sibling tools like get_mue and get_timely_filing, which target different edit types or workflows.

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

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

The description provides explicit guidance on when to use each mode: pair mode for a specific pair/date, list mode for a code. It also notes the tool is free and requires no API key. However, it does not explicitly mention alternatives or when not to use the tool relative to siblings, stopping short of a 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?

Read-only, idempotent, and non-destructive traits are already in annotations. The description adds behavioral context beyond that: the null behavior of "daysRemaining and expired" when no published limit exists, the contract-specific nature of limits, and the note about not treating results as actual deadlines. It also states the tool is free and requires no API key.

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

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

The description is two sentences, front-loaded with the primary purpose, and every sentence earns its place. The first sentence states the exact function, the second provides critical caveats about null values and deadline validity. No fluff or repetition.

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

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

Given no output schema, the description usefully explains the key output fields (daysRemaining, expired) and their null condition. It also covers the broader context that limits are often contract-specific. It doesn't list every possible field, but for this simple tool the description provides sufficient completeness.

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 already provides 100% coverage with clear descriptions for both parameters (payer enum with example, date of service format example). The description does not add parameter-specific semantics beyond the phrase "given a date of service," which aligns with the schema. 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 what the tool does: it returns "days remaining to file an initial claim" for a specific payer and date of service. It also distinguishes from siblings by focusing on timely filing limits, which is a distinct resource from claim checks, appeals, denials, MUE, or NCCI edits.

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 when to use the tool: when you need the days left to file a claim for a payer given a service date. It also includes a caveat to "check note before treating the result as an actual deadline," which guides interpretation. No explicit alternatives are named, 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.