Constat MCP — FDA Device Evidence Lifecycle
Server Details
FDA and CMS evidence for AI medical devices: 510(k), postmarket, reimbursement, and compliance.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- thehealthai/fda-risk-radar-mcp
- GitHub Stars
- 0
- Server Listing
- Constat — 483 Risk Radar
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Tool Definition Quality
Average 4.1/5 across 14 of 14 tools scored. Lowest: 3.4/5.
Every tool targets a distinct entity or action: premarket evidence, postmarket intelligence, product code risk, firm compliance, reimbursement pathways, etc. Even the two 'cohort' tools are clearly separated by premarket vs. postmarket context, and descriptions clarify any ambiguity.
Tools consistently use snake_case and a noun_noun or noun_noun_verb pattern (e.g., device_evidence_lookup, reimbursement_stats). Most end with a verb or a clear noun (stats, history, chain). Minor deviation: 'evidence_cohort_stats' lacks a verb, but the pattern is still readable and predictable.
With 14 tools covering premarket evidence, postmarket signals, risk, compliance, predicate chains, reimbursement, and watchlist monitoring, the count fits the FDA device evidence lifecycle well. No tool is redundant, and the scope is neither too narrow nor overwhelming.
The tool set provides broad coverage across the device evidence lifecycle: lookup, search, stats, and risk analysis for both pre- and post-market, plus reimbursement and compliance. Minor gaps exist, such as missing a generic device-name search or clinical study tool, but core workflows are well supported.
Available Tools
14 toolscohort_postmarket_statsBInspect
Postmarket presence rates across the snapshotted AI/ML device cohort (optionally by panel): share with any recall in 24 months, with a rising MAUDE trend, with any drift signal, with a warning-letter match — every rate with its denominator inline, never pooled across devices.
| Name | Required | Description | Default |
|---|---|---|---|
| panel | No | Advisory panel, e.g. Radiology; omit for all |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description bears the full burden. It discloses that rates include denominators and are never pooled across devices, and implies a static snapshot. However, it omits details on authentication, data freshness, or any destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is informative and front-loaded with the main purpose. While it is somewhat long (55 words), it efficiently packs necessary detail 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?
The description outlines the metrics returned but lacks an explicit specification of the output format or time window (e.g., '24 months' is only mentioned in one metric). Without an output schema, more detail on return structure would improve 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?
Schema description coverage is 100% and the only parameter 'panel' is adequately described. The tool description adds 'optionally by panel' but does not introduce new semantic meaning 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 explicitly states it computes postmarket presence rates for an AI/ML device cohort with specific metrics (recall, MAUDE trend, drift signal, warning-letter match). It clearly distinguishes from sibling tools which focus on individual device lookups or evidence search.
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 this tool versus alternatives like device_postmarket_lookup or evidence_cohort_stats. The description does not mention scenarios, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
device_evidence_lookupAInspect
Look up the structured premarket evidence FDA accepted for a specific AI/ML-enabled device by 510(k) number (e.g. K252148). Returns parsed summary fields — validation study design, sample sizes, endpoints, reported performance, predicate chain, PCCP — each with a verbatim source quote and page. Null means the summary did not state it.
| Name | Required | Description | Default |
|---|---|---|---|
| k_number | Yes | 510(k) or De Novo number, e.g. K252148 or DEN180001 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return format (parsed summary fields with verbatim quotes and pages) and meaning of null. No annotations provided, so description carries full burden; it is adequate but lacks info on authentication or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences: first states purpose with example, second explains return fields and null meaning. No wasted words, 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?
For a simple lookup with 1 parameter and no output schema, description covers purpose, input, and output structure. Lacks info on rate limits or auth, but negligible given tool nature.
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% with pattern and example; description adds context (AI/ML-enabled devices) and reinforces the example, adding value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb+resource: 'look up structured premarket evidence' for AI/ML devices by 510(k) number, with example. Distinguishes from siblings like device_postmarket_lookup by specifying 'premarket' and 'AI/ML-enabled'.
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?
Implied usage when a 510(k) number is known and premarket evidence is needed. No explicit alternatives or when-not-to-use guidance, though sibling tools exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
device_postmarket_lookupAInspect
Post-clearance intelligence for one AI/ML device by 510(k) number: its product code's recalls, MAUDE adverse-event level and trend, warning-letter and 483 matches for the applicant, plus per-device drift signals (adverse-event inflection, re-clearances of the same device line, software-recall patterns, predicate-cohort recall activity). Descriptive observables with sources — never a safety judgment.
| Name | Required | Description | Default |
|---|---|---|---|
| k_number | Yes | 510(k) or De Novo number, e.g. K252148 or DEN180001 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavioral traits. It states that outputs are 'descriptive observables with sources — never a safety judgment,' which clarifies the nature of results. However, it does not mention whether the tool is read-only, any prerequisites (beyond valid k_number), or potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured sentence that front-loads the core purpose and enumerates data types. It is informative without being overly verbose, though it could be slightly more concise.
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 (multiple data sources), the description provides a thorough list of what it returns, compensating for the lack of an output schema. It covers recalls, MAUDE events, warning letters, and drift signals, making the tool's capabilities clear.
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% for a single parameter, k_number. The description does not add significant meaning beyond the schema's own description; it only reiterates that the tool uses the k_number for lookup. The pattern is already clear from the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: providing post-clearance intelligence for a single AI/ML device identified by a 510(k) number. It lists specific data points (recalls, MAUDE events, warning letters, etc.) and sources, distinguishing it from sibling tools like cohort_postmarket_stats or device_risk_lookup by focusing on an individual device.
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 usage for obtaining aggregated post-market data on a specific device, hinting at when not to use it (e.g., not for cohorts). However, it lacks explicit alternatives or when-not scenarios, though 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.
device_risk_lookupAInspect
Review a medical-device category's public FDA signals by three-letter product code (e.g. FRN = infusion pump). Returns recalls, MAUDE adverse-event trend, warning-letter matches, a normalized category signal, its driver contributions, and interpretation limits. It does not predict enforcement against a firm.
| Name | Required | Description | Default |
|---|---|---|---|
| product_code | Yes | FDA product code, e.g. FRN |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It describes the tool as reviewing and returning signals, implying a read-only operation, but does not explicitly state read-only behavior, auth requirements, or rate limits. The mention of 'interpretation limits' hints at constraints but lacks detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, each serving a clear purpose. The first sentence front-loads the tool's function and outputs; the second adds a key limitation. No extraneous words 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 a single parameter (fully described in schema) and no output schema, the description lists six types of returned data (recalls, MAUDE trend, etc.), which is sufficient for a lookup tool. However, it does not explain how to interpret the signal or the output format, leaving some gaps for an agent.
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% for 'product_code', but the description adds meaningful context: it explains the parameter as a three-letter code with an example and ties it to FDA product categories. This goes beyond the schema's basic description, aiding correct usage.
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 ('Review') and clearly identifies the resource ('public FDA signals by three-letter product code'). It provides an example ('FRN = infusion pump') and lists the outputs (recalls, MAUDE trend, etc.), distinguishing it from sibling tools that focus on other aspects like device evidence or postmarket statistics.
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 when to use the tool (review FDA signals by product code) and explicitly mentions a limitation ('does not predict enforcement against a firm'). However, it does not compare to sibling tools or specify when not to use it, leaving room for confusion among similar device lookups.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
evidence_cohort_statsAInspect
Reporting-rate stats across the parsed AI/ML corpus (optionally by panel). Each rate is a presence figure with its denominator — 'reported in X of Y audited devices' — never a pooled performance value. Excludes not-yet-parsed devices from every denominator and discloses the parse queue separately. Predicate age (median years between a clearance and its cited predicates) is included when decision-date coverage clears a 60% floor, and withheld otherwise.
| Name | Required | Description | Default |
|---|---|---|---|
| panel | No | Advisory panel, e.g. Radiology; omit for all |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description thoroughly discloses behavior: denominators exclude unparsed devices, parse queue disclosed separately, predicate age included only if coverage exceeds 60%.
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 with information but front-loads purpose and uses clear, specific language. A minor reduction for slight verbosity in the third sentence.
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 fully explains return format (rates as presence figures), predicate age condition, and exclusion behavior, making it self-contained.
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% with a single parameter. The description adds context about optional panel filtering and how rates are computed, going beyond the schema 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 clearly states it reports 'reporting-rate stats across the parsed AI/ML corpus' with optional panel filtering, differentiating it from sibling tools by specifying its scope and data characteristics.
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 usage for obtaining cohort-specific rates and explains optional panel parameter, but does not explicitly mention when to use alternatives like cohort_postmarket_stats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
evidence_searchAInspect
Find AI/ML device clearances by filter — product code, panel, applicant, and whether the submission reported clinical data, any sensitivity metric, or a PCCP. Answers 'what evidence did FDA accept for devices like mine'. Returns matching records with their parsed evidence. Presence flags are descriptive: 'reports a sensitivity metric' is not 'reports a comparable sensitivity' — analysis units differ across devices.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| panel | No | Advisory panel, e.g. Radiology | |
| has_pccp | No | Included a Predetermined Change Control Plan | |
| applicant | No | Substring match on applicant/company name | |
| product_code | No | FDA product code, e.g. QAS | |
| has_clinical_data | No | ||
| reports_any_sensitivity_metric | No | Reported any sensitivity metric (canonical — includes per-finding sensitivities, not just the top-level slot). Not a claim of cross-device comparability. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It usefully clarifies that presence flags are descriptive and not comparative, which is critical for correct interpretation. However, it does not mention other behavioral aspects like pagination, rate limits, or response structure beyond 'parsed evidence'.
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 succinct sentences. The first states the overall purpose and lists filters, the second clarifies a semantic nuance. No redundant or extraneous text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 7 parameters, no required fields, no output schema, and no annotations, the description covers the core functionality and return type ('matching records with their parsed evidence'). It could be more specific about the scope (e.g., FDA clearances) or mention any limitations, but it is sufficiently complete for a search 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 high (71-86%), so baseline is 3. The description adds value by clarifying that 'reports_any_sensitivity_metric' is not a claim of cross-device comparability and that flags are descriptive. This nuance is not captured in the schema alone, thus raising the 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 clearly states the tool finds AI/ML device clearances by various filters, answering a specific user question. It uses a specific verb ('Find') and resource ('AI/ML device clearances'), and the listed filters distinguish it from sibling tools like device_evidence_lookup (which likely operates by ID).
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 includes a motivating question ('what evidence did FDA accept for devices like mine') that implies when to use it, but it does not explicitly state when not to use it or mention alternative tools. This leaves room for improvement in guiding the agent toward correct selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
firm_compliance_historyAInspect
Build a recent, source-bounded FDA public-record timeline for a device firm: matched recalls, warning letters, and Form 483 citations where exact FEI numbers are available. Product codes are discovered from Constat's AI/ML-device corpus or may be supplied explicitly. Returns attribution and coverage limits with the records; it is not a finding of noncompliance or a prediction of FDA action.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum records per source and events in the timeline (default 50). | |
| since | No | Earliest event date, YYYY-MM-DD. Defaults to five years ago. | |
| firm_name | Yes | FDA applicant or company name, e.g. 'Medtronic' | |
| fei_numbers | No | Optional exact FDA FEI numbers; improves Form 483 attribution. | |
| product_codes | No | Optional FDA product-code scope. When omitted, Constat discovers codes from applicant matches in its device corpus. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must fully disclose behavior. It notes that matches depend on exact FEI numbers, mentions return of attribution/coverage limits, and clarifies it is not a compliance finding. However, it omits details on error handling, data refresh frequency, or what happens when FEI numbers are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no repetition. The first sentence delivers the core function, the second clarifies parameter behavior, and the third states return type and limitations. 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 5 parameters, 1 required, no output schema, and no annotations, the description covers the tool's inputs, output elements (attribution and coverage limits), and caveats. It could be more complete by hinting at the response format or pagination, but overall is sufficiently informative.
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% with descriptive parameter comments, so baseline is 3. The description adds value by explaining how product codes are discovered from Constat's AI/ML corpus when omitted, going beyond the schema's static 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 uses a specific verb ('Build') and resource ('FDA public-record timeline for a device firm'), enumerates matched sources (recalls, warning letters, Form 483 citations), and adds a qualifier ('where exact FEI numbers are available'), making it unmistakably distinct from sibling tools that cover postmarket stats or evidence search.
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 explains product code discovery (from corpus or supplied) and includes a disclaimer about non‑compliance findings, but does not explicitly state when to use this tool over siblings like device_postmarket_lookup or evidence_search. No comparison or alternative guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
postmarket_searchAInspect
Find AI/ML devices by postmarket criteria — product code, panel, applicant, whether any drift signal exists, minimum recalls in 24 months, or a rising MAUDE trend. Returns per-device postmarket summaries with drift-signal counts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| panel | No | Advisory panel, e.g. Radiology | |
| applicant | No | Substring match on applicant/company name | |
| product_code | No | FDA product code, e.g. QIH | |
| maude_trend_up | No | Code-level MAUDE trend above its own 3-year baseline | |
| has_drift_signal | No | ||
| min_recalls_24mo | No | Product-code recalls in trailing 24 months | |
| signal_specificity | No | Restrict the drift-signal filter to one tier: 'device' = attributable to this device (own line re-cleared, own applicant's recall, predicate neighbors) — the high-signal tier; 'product_code' = observed in its category |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description should disclose behavioral traits. It mentions returning 'per-device postmarket summaries with drift-signal counts,' indicating a read operation. However, it lacks details on pagination, rate limits, or side effects. The behavior is adequately implied but not fully transparent.
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 (two sentences), front-loads the purpose, and includes essential details without extraneous text. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main filters and return type but does not explain the relationship between has_drift_signal and signal_specificity, or define 'drift signal.' Given no output schema, it could be more complete. Score 3 is adequate but not excellent.
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 75%, and the description lists the criteria but does not add significant meaning beyond the schema descriptions for most parameters. It provides a high-level summary but not deeper semantics. Baseline 3 is appropriate given 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 states the tool 'Find AI/ML devices by postmarket criteria' with specific filters listed (product code, panel, applicant, etc.). It distinguishes from sibling tools like device_postmarket_lookup by emphasizing search across multiple criteria and returning summaries. However, it could more explicitly contrast with cohort_postmarket_stats.
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 usage for searching devices by the given criteria but provides no explicit guidance on when to use this tool versus alternatives like evidence_search or cohort_postmarket_stats. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
predicate_chainAInspect
Trace the predicate ancestry of a 510(k) device, with each cited predicate's age (how many years old the predicate was when the child cleared). Reveals how AI/ML devices chain to older predicates.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Max ancestry depth (default 4) | |
| k_number | Yes | 510(k) or De Novo number, e.g. K252148 or DEN180001 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses output includes predicate age and hints at AI/ML relevance, but does not describe return format, rate limits, or read-only nature. Adequate but not detailed.
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 concise sentences with no redundant information. Front-loaded with the main action (trace ancestry), followed by key output detail (predicate age).
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 moderate complexity and no output schema, description reasonably explains functionality and key output. Mentions AI/ML relevance, adding context. Could improve by describing return structure (e.g., chain 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 coverage is 100% with descriptions for both parameters. Description adds value by explaining the output (predicate age) but does not elaborate on parameter semantics beyond 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?
Description clearly states 'Trace the predicate ancestry of a 510(k) device' with specific verb and resource. It distinguishes from sibling tools (e.g., device_evidence_lookup, postmarket_search) by focusing on predicate chaining and age calculation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use vs alternatives, but the purpose is clear enough to infer usage for ancestry tracing. No exclusions or conditions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reimbursement_lookupAInspect
Trace the clearance-to-payment pathway for an AI/ML device by FDA clearance number (K/DEN, e.g. DEN170073) OR bare CPT code (e.g. 75580). Returns every payment mechanism (NTAP add-on, Category I/III CPT + CMS rate, HCPCS, MAC LCD) with amounts, effective dates, and source links, plus any commercial/MAC payer coverage policies that reference the clearance or its codes. Answers 'who got paid, how much, through which mechanism, on what basis.' CPT codes are bare factual identifiers only — no procedure descriptors; follow the CMS source link for the official descriptor.
| Name | Required | Description | Default |
|---|---|---|---|
| cpt_code | No | Bare CPT code, e.g. 75580 or 0932T | |
| k_number | No | FDA clearance number, e.g. DEN170073 or K252148 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully shoulders the burden of disclosing behavior. It comprehensively lists the types of outputs returned (payment mechanisms with amounts, dates, links, and policies) and clarifies important nuances (e.g., CPT codes are bare identifiers without descriptors, directing users to the CMS source link for official descriptors). It is a read-only lookup, implied but not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficiently front-loaded with the core purpose in the first sentence, followed by a list of outputs and a final summary. While comprehensive, some redundancy exists (e.g., 'bare CPT code' is repeated). Overall, it is well-structured and earns its length for a complex lookup 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?
Given the lack of an output schema, the description provides a thorough account of what the tool returns: 'every payment mechanism (NTAP add-on, Category I/III CPT + CMS rate, HCPCS, MAC LCD) with amounts, effective dates, and source links, plus any commercial/MAC payer coverage policies.' It answers the key question 'who got paid, how much, through which mechanism, on what basis.'
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% with descriptions and patterns for both parameters. The description adds value by reinforcing the format (e.g., examples like 'DEN170073' and '75580') and clarifying that CPT codes are 'bare factual identifiers only.' However, this information largely mirrors the schema's pattern and descriptions, so the contribution is incremental.
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: tracing the clearance-to-payment pathway for an AI/ML device using either an FDA clearance number or a bare CPT code. It specifies the exact outputs (payment mechanisms, amounts, effective dates, source links, and coverage policies), distinguishing it from sibling tools like reimbursement_search (which likely offers broader search) and reimbursement_stats (which provides statistics).
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 tells when to use the tool: with an FDA clearance number (K/DEN format) or a bare CPT code. It provides examples (DEN170073, 75580). However, it does not explicitly state when not to use it or provide alternatives, though the specificity of inputs serves as implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reimbursement_searchAInspect
Find AI/ML device payment pathways by mechanism — e.g. 'devices that got NTAP', 'devices paid under a Category I CPT code', 'pathways with a known CMS dollar rate'. Filters: mechanism, CPT category, NTAP status, applicant. Returns pathways with amounts, effective dates, and sources. Use reimbursement_stats for the mechanism distribution (never a single pooled reimbursement rate).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| applicant | No | Substring match on device maker / applicant name | |
| mechanism | No | Payment mechanism | |
| ntap_status | No | ||
| cpt_category | No | CPT category | |
| has_cms_rate | No | Only pathways with a known CMS dollar rate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It conveys that the tool returns matching pathways with amounts, dates, and sources, implying a read-only query. However, it does not explicitly state read-only or mention any side effects, though for a search tool this is sufficient.
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 clear sentences plus filter list. Front-loaded with purpose and key examples. Could be slightly more concise, but no fluff.
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 6 parameters, no output schema, and no annotations, the description effectively explains what the tool returns (amounts, dates, sources) and provides usage context. It also cross-references a sibling tool, making the description complete for a search 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 67% (4 of 6 parameters have descriptions). Tool description mentions filtering by mechanism, CPT category, etc., but adds little beyond what schema already provides. Baseline 3 is appropriate; minimal extra 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?
Description clearly states it finds AI/ML device payment pathways by mechanism, with specific examples like 'devices that got NTAP'. It also explicitly distinguishes from sibling reimbursement_stats, making the tool's specific purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit guidance on when to use this tool versus reimbursement_stats, stating the latter is for mechanism distribution and that this tool never returns a single pooled rate. Also lists available filters, guiding appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
reimbursement_statsAInspect
Distribution of payment mechanisms across the AI/ML reimbursement corpus — pathway and distinct-device counts per mechanism (NTAP, Cat I, Cat III/APC, …) with the min/median/max dollar amounts for each. Deliberately never a single pooled 'reimbursement rate': NTAP add-on amounts and CMS rates are different measurements and are reported separately with their own spreads.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of transparency. It discloses that NTAP add-on amounts and CMS rates are reported separately with their own spreads, and that distribution data includes min/median/max. This is clear behavioral context.
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, highly informative with no redundancy. Every sentence adds value: first states what it provides, second clarifies what it does not provide and why.
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 fully explains the return value (counts per mechanism and dollar amount spreads). For a zero-parameter stats tool, this is complete and leaves no ambiguity.
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?
There are zero parameters, so schema coverage is 100%. The description correctly adds no parameter information since none exist, but the baseline of 4 is appropriate as it does not need to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it provides distribution of payment mechanisms with counts and dollar amounts, and distinguishes itself by noting it never returns a single pooled reimbursement rate. This clearly differentiates from sibling tools like reimbursement_lookup and reimbursement_search.
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 usage for aggregate statistics rather than individual lookups or searches, but does not explicitly state when to use this tool versus alternatives. It warns against expecting a single pooled rate, which provides some guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vehicle_risk_lookupAInspect
Look up NHTSA safety history for a vehicle by make, model, and model year. Returns recall campaigns and complaint statistics (crashes, fires, injuries, top components).
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | e.g. honda | |
| year | Yes | model year, e.g. 2020 | |
| model | Yes | e.g. civic |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral transparency burden. It indicates a read operation with no side effects but does not disclose error handling, rate limits, or how missing data is reported. The description is adequate but lacks 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 a single sentence with no wasted words. It is front-loaded with the core purpose and efficiently adds return details.
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 no output schema, the description provides an overview of return content (recall campaigns and complaint statistics) but does not specify format or structure. This is adequate for a basic use case but could be more 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 description coverage is 100%, so each parameter already has a basic description (e.g., 'e.g. honda'). The tool description does not add additional semantics beyond what the schema provides, making it baseline adequate.
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 ('Look up') and clearly identifies the resource (NHTSA safety history for a vehicle by make, model, and model year). It specifies what the tool returns (recall campaigns and complaint statistics) and is distinct from sibling tools focused on devices or reimbursement.
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 does but provides no guidance on when to prefer it over alternatives or when not to use it. While the sibling tools are in different domains, no explicit usage context is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
watchlist_diffAInspect
Return machine-generated FDA public-record changes detected for monitored product codes since a caller-supplied date, plus each code's latest category snapshot and postmarket coverage. Defaults to Constat Radar's five-code watchlist and the last seven days. Analyst verdict text and internal review status are excluded; use next_since as the next polling cursor.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum changes to return (default 100). | |
| since | No | Return changes on or after this ISO date or UTC timestamp. Defaults to the last seven days; pass the prior response's next_since for exact polling. | |
| product_codes | No | FDA product codes to poll. Omit for the default Radar watchlist. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that changes are machine-generated, excludes specific fields, and explains pagination via next_since. It does not mention mutation or auth requirements, but for a read-oriented tool the transparency is good.
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 concise sentences, front-loaded with the primary function, followed by defaults and exclusions. Every sentence adds value 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?
Given no output schema, the description covers purpose, defaults, pagination, and exclusions. It could be improved by describing the output structure, but it is sufficiently complete for a simple polling 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%, baseline 3. The description adds value by explaining defaults for since and product_codes, and the polling use of next_since, which goes beyond the 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 clearly states it returns machine-generated FDA public-record changes for monitored product codes, plus category snapshots and postmarket coverage. It specifies the resource and action distinctly, and differentiates from sibling tools by mentioning the exclusion of analyst verdict text and internal review status.
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 describes defaults (five-code watchlist, last seven days) and provides guidance on using next_since for polling. While it does not explicitly contrast with alternatives, the context of polling incremental changes is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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