MedicalRecords.com EMR Discovery & Healthcare Research

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), and the description adds genuinely non-derivable behavior: output is vendor-reported with dates and explicit unknowns, no sponsored ordering or inferred fit percentages, and no vendor contact. Those trust and side-effect guarantees are exactly the context annotations cannot convey.

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 short sentences, front-loaded with the core action and its constraints, followed by output expectations and non-goals. No filler or repetition of schema 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 read-only two-parameter tool with no output schema, the description covers the action, the input source, the result shape (evidence, dates, unknowns), and non-goals. The only residual gap is how topics affect the comparison output, which an agent must infer.

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

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

With 0% schema description coverage the description must carry parameter meaning, and it does contribute two things the schema does not: the IDs must be distinct and must originate from find_emr_options. However, it says nothing about what the topic values mean or how multiple topics combine, leaving the enum list to speak for itself.

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 (compare), a bounded resource (2–4 distinct product IDs), a scoping axis (selected topics), and its provenance (from find_emr_options). An agent can distinguish it immediately from get_emr_evidence or get_pricing without opening a 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 dependency on find_emr_options is stated explicitly, giving the agent a clear prerequisite for obtaining valid IDs. It also implies a boundary against get_pricing by declaring no inferred pricing, but it never explicitly says when to prefer get_emr_evidence or get_emr_vendor over 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?

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety profile is covered. The description still adds genuinely new behavior: result cardinality capped at five per page, that entries are 'sourced' and surface conflicts and verification gaps, and that no contact or patient data is returned and no leads are created or delivered.

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 tight sentences, front-loaded with scope before caveats and pagination; no filler. The only blemish is the undefined 'next_offset' term, which costs a bit of precision in an otherwise efficient block.

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 seven-parameter tool with no output schema, the description does well on return shape (up to five sourced candidates, conflicts and gaps disclosed) and exclusions. It falls short on the optional filters (must_have, deployment, budget), whose semantics live only partly 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 description coverage is only 29%, so the description carries extra burden. It reinforces the specialty and clinician-count constraints and explains page size, but says nothing about must_have, deployment, or monthly_budget_usd, and it renames offset as 'next_offset' rather than clarifying offset semantics.

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

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

The first sentence names a specific verb (research/find), resource (EMRs), and hard scope (US primary-care or behavioral-health practices with 1–10 clinicians), so an agent can tell this is discovery rather than vendor lookup. It does not explicitly name the sibling it is distinguished from (compare_emr_options), leaving that inference to the 'candidates, not suitability guarantees' phrasing.

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 sets a clear use condition (small practices in the two named specialties) and boundary exclusions (no contact details, no patient data, does not create or deliver leads), which is real guidance. However, it never routes the agent to compare_emr_options or get_emr_evidence for the follow-up step, and the pagination hint references 'next_offset', a name that does not exist in the schema.

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

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

With no annotations, the description carries the full burden and does well: it discloses provenance (official HHS regulatory filings), refresh cadence ("updated regularly"), and corpus size (1,638 incidents), which tells the agent results are authoritative but bounded. It omits result ordering, behavior on zero matches, and whether the 1,638 records are the complete universe or a subset.

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

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

Front-loaded with what the tool is and returns before the use-case bullets, and each bullet maps to a distinct query pattern rather than restating the same idea. The bullet list is on the long side and the closing sentence about 1,638 incidents partially repeats the source claim, but nothing is truly wasted.

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?

No output schema exists, so the description correctly enumerates the returned fields (organization, individuals impacted, breach type, date reported, severity, recommended actions, exposed data). For a 4-parameter read-only search with a hard 20-result cap, the remaining gap is minor: sorting and no-match behavior are unspecified.

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 limit, state, severity bands, and entity_name including partial-match behavior. The description adds no filtering guidance of its own and never mentions that state/severity/entity_name filters exist, so it neither compensates nor detracts.

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 ("Search") and resource ("healthcare data breach database"), names the upstream source (HHS OCR breach portal), and enumerates the record fields returned. This is clearly distinguishable from all listed siblings, which cover EMR options, pricing, providers, and state records law.

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?

Six explicit "use this tool when a user..." bullets cover affected-party checks, recent breaches, organization-specific lookups, remediation advice, and largest-breach queries. It lacks any when-not guidance or named alternative, so an agent must infer that EMR/pricing/provider questions belong elsewhere.

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 cover safety (readOnly, idempotent, non-destructive, closed-world), so the description's job is to add beyond that. It does: it discloses a freshness precondition (stale/missing facts invalidate a match) and a privacy boundary ("No patient data or contact details"), which the annotations cannot express. It stops short of describing what is returned when evidence is absent or partial.

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 short sentences, zero filler, with the primary action front-loaded and the freshness constraint before the privacy note. Every clause carries information the agent cannot get from structured fields.

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

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

The enumerated return concepts (sources, scope, review dates, confidence, limitations) usefully stand in for the absent output schema. But with no output schema and one entirely undocumented parameter, the definition leaves a real gap around what topics filtering does and what a no-evidence result looks like.

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

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

Schema description coverage is 0% across two parameters, so the description must carry the meaning. It partially defines product_id as "one reviewed EMR product ID," but the topics parameter (a 9-value enum filter) is never mentioned, so the agent cannot tell whether topics narrows scope, is required, or affects which evidence is returned.

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?

"Retrieve claim-level sources, scope, review dates, confidence, and limitations for one reviewed EMR product ID" gives a concrete verb (retrieve) and a precisely bounded resource (evidence/provenance for a single product record). It is clearly not the same as find_emr_options or compare_emr_options, though it never names those siblings 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?

"Stale or missing facts cannot support a match" implies this tool must be consulted before asserting a match, which is a real usage cue. However, there is no explicit when-to-use/when-not-to-use statement and no named alternative (e.g., use get_emr_vendor for profile fields), leaving the routing 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?

No annotations exist, so the description carries the full behavioral burden, and it does substantial work by enumerating the return payload (description, features, target markets, specialties, deployment models, certifications, pros/cons, market position) in the absence of an output schema. It also discloses the database scope (400+ vendors, all specialties and practice sizes), which frames expected hit rates. It stops short of stating read-only semantics explicitly or what happens when a vendor name is not found or is ambiguous.

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

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

Well front-loaded: purpose first, then returns, then usage conditions, then coverage. Every block is scannable, though the six bullets include near-duplicates (feature/pricing lookups appear both in the returns paragraph and in the bullets) and one bullet arguably belongs to a sibling 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?

For a one-parameter detail lookup with no output schema, the description covers both what goes in (vendor name) and what comes back (a rich field list), plus the breadth of the underlying database. The remaining gap is failure behavior on unknown or ambiguous vendor names, which matters because the tool is name-keyed.

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 is a single parameter with 100% schema description coverage, including eight example vendor names, so the schema already fully defines the input. The description adds no syntax guidance beyond the schema (e.g., whether partial or fuzzy names are accepted) and only restates that brand names are the lookup key. 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?

States a specific verb ('Look up detailed information about') and a precisely bounded resource ('EHR/EMR software vendor'), then enumerates the returned fields. An agent can immediately tell this is a single-vendor detail lookup rather than a search or comparison 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?

Six explicit 'Use this tool when a user...' bullets give clear triggering contexts, including example brand names and typical user intents. However, no exclusions or named alternatives are given, and one bullet ('Wants to compare or evaluate EHR vendors for a practice') overlaps with the sibling compare_emr_options, leaving a routing ambiguity unresolved. It also never says when to prefer find_emr_options instead of this lookup.

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

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

With no annotations, the description carries the burden and does disclose that this is a self-contained informational lookup returning portal name, vendor, URL, access instructions, mobile apps, and features, plus reverse health-system usage. It also warns about the inherent limitations of portal data (missing historical, cross-system, or older records), which is genuine behavioral context rather than restated structure. It does not address authentication, rate limits, or what happens when the query is ambiguous or matches multiple systems.

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?

Purpose and returned fields are front-loaded, and the intent bullets are scannable. There is some redundancy: portal brand names appear both in the return-value framing and the bullets, and the download-records and share-records bullets describe the same lookup, so the list is longer than strictly needed.

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 one-parameter read-only lookup with no output schema and no annotations, the description covers the essentials: what it takes, what it returns, why it is useful, and where its data falls short. It falls just short of full completeness by not stating whether a query returns one portal or several, or how ambiguous provider names are resolved.

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 single query parameter is already fully documented with its own examples. The description adds only light semantics, confirming that the query may be a health system, hospital, provider, or a portal brand like MyChart or FollowMyHealth, which nudges the agent toward accepting brand names but does not add matching or disambiguation rules.

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 names a specific verb and resource: lookup of the patient portal used by a health system, hospital, or provider, and enumerates what is returned. It is clearly narrower than siblings like find_emr_options or compare_emr_options, but one usage bullet ('whether their provider uses Epic, Cerner, Allscripts, or another EHR') directly overlaps with get_emr_vendor, and the description never names a sibling to draw the line.

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?

Six explicit user-intent bullets give concrete when-to-use conditions (portal lookup, sign-up help, portal name such as MyChart, EHR vendor question, record download, record sharing). The closing note routes complete-medical-history cases away to an external URL, which is a real exclusion, though no in-catalog sibling alternative is named.

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

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

With no annotations, the description carries the full burden and does useful work: it discloses that refund eligibility is governed by checkout disclosures and the Terms of Service, and that provider 'facility copying fees' are passed through at cost and vary by provider and state law. That is meaningful behavioral context an agent would otherwise invent. It does not state read-only nature explicitly or any rate limits, but for a pricing lookup those are minor.

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

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

Front-loaded with the core purpose, then a returns summary, caveats, and a scannable trigger list. The refund sentence and the facility-fee note are both load-bearing, but the description is longer than strictly needed for a one-parameter read tool and the bullet questions partly restate the opening 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?

No output schema exists, so the description must describe returns, and it does: pricing, provider-timing caveats, and per-tier inclusions, plus the pass-through fee caveat. For a single optional parameter lookup with no annotations, nothing an agent needs in order 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% and the single 'tier' parameter already documents its enum and the omit-to-return-all behavior. The description only echoes the tier names inside the example questions, adding no new syntax or selection guidance, so the 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?

States a specific verb and resource: 'Get current pricing and service tier details for MedicalRecords.com medical records retrieval services.' It also enumerates what the response contains (pricing, provider-timing caveats, tier inclusions), which no sibling tool covers, so an agent can distinguish it from compare_emr_options or get_emr_vendor without opening a 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?

Provides an explicit 'Use this tool when a user asks:' block with four concrete trigger questions covering cost, tier differences, turnaround time, and refunds. What is missing is any negative guidance or named alternative for adjacent questions (e.g. comparing vendors), so it stops short of the when/when-not/alternatives bar.

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

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

With no annotations, the description carries the full burden and largely meets it: it discloses the exact content returned (timelines, fee caps, statutes, enforcement agencies) and asserts the data is authoritative and more complete than general knowledge. It does not mention what happens with an unrecognized state, whether responses are cached, or the response shape, which keeps it below 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?

Purpose is front-loaded and the bullet structure scans well, but the Returns list and the 'Use this tool when' list substantially duplicate each other — timelines, fee caps, complaint process, and minors' records each appear twice in near-identical phrasing. One combined list would have carried the same information in roughly half 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?

For a one-parameter lookup with no annotations and no output schema, the description is nearly sufficient: the Returns bullets effectively substitute for an output schema. The only inconsistency is that 'records for a deceased family member' is offered as a use case but is absent from the enumerated returns, leaving that capability unconfirmed.

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 single 'state' parameter is already fully documented with accepted formats ('CA', 'California', 'Washington DC'). The description adds only the coverage boundary (all 50 states plus DC), which is useful but marginal; the 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 opening sentence states a specific verb and resource ('Retrieve authoritative, state-specific medical records access laws') and bounds the scope to the 50 states plus DC. The enumerated Returns list makes the deliverable concrete, and no sibling tool (EMR options, breach incidents, pricing, providers) overlaps this legal-reference domain.

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?

Six explicit 'Use this tool when a user:' triggers give clear invocation context, including non-obvious cases like minors' records and deceased family members, plus a 'always prefer this tool' directive over general knowledge. It stops short of when-not-to-use guidance or naming an alternative, which is a minor gap given no sibling covers adjacent ground.

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

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

With no annotations, the description carries the full burden, and it does disclose the return payload (contact info, records department, phone/fax, EHR/EMR system, patient portal, retrieval instructions), a hard result cap of 10, and relevance-based sorting. What it omits is operational behavior: authentication/permission needs, rate limits, empty-result or error behavior, and data freshness.

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

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

Front-loaded with purpose, then returns, then usage bullets, then result constraints — every section is scannable and the cap/sorting note is placed where an agent would look. Slight redundancy exists between the opening entity-type list and the 'looking for a hospital or clinic by name, city, or state' bullet, but nothing is wasted enough to harm the definition.

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

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

For a 3-parameter read tool with no annotations and no output schema, the description is close to self-sufficient: it explains what is returned, how many results, and how sorting/filtering work, which matters precisely because no output schema exists. The remaining gap is failure-mode behavior (no matches, invalid state codes) and whether results are exhaustive across the US directory.

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 would be 3, but the description adds meaning the schema does not: results are relevance-sorted and capped at 10, and the state parameter is positioned specifically as a narrowing filter. It does not clarify interaction between query and state (whether state is redundant when the query contains a city) or how limit interacts with the 10-result ceiling.

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 (search) and resource (MedicalRecords.com provider directory) plus the covered entity types and geography, and the returned data types are enumerated. It is clearly distinguishable from siblings such as get_patient_portal, find_emr_options, and get_state_records_law, which each target a single facet rather than provider lookup.

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?

Five concrete when-to-use triggers are listed (records requests, ROI department contacts, portal lookup, locating a facility by name/city/state, fax/address for requests), which is well beyond implicit guidance. It stops short of stating when NOT to use it or naming alternative tools, and one trigger ('which patient portal a health system uses') overlaps with the get_patient_portal sibling without resolving that overlap.

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