ClearMind Capital

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed world), so the bar is lower. The description still adds meaningful domain context: this queries public government filings that are a specific subset (Ohio, 5500-SF, ~<100 participants), and it discloses the return shape (plan_id values). It does not mention result caps, pagination, or how ambiguous name matches are resolved, which keeps it out of 5 territory.

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

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

Two sentences, front-loaded with the search verb and scope, with the parenthetical qualifying the data source rather than padding it. Every clause (Ohio, 5500-SF, <100 participants) narrows the agent's expectation, and the final sentence explains the return handoff.

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 correctly explains what is returned (plan_id values) and which tool consumes them, and it fully covers the single input dimension. It stops short of describing result limits, ranking, or empty-result behavior, which a search endpoint would ideally disclose.

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 query parameter is already documented as 'Company or plan name, or EIN.' The description adds only the '9-digit' formatting detail for EIN, which is a marginal increment over the schema text; 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 (Search) and a precisely scoped resource: public U.S. DOL Form 5500-SF filings underlying the ClearMind 401(k) Scorecard, restricted to Ohio plans, usually under 100 participants. It also names the downstream tool the returned plan_id feeds, so the agent can distinguish it from the unrelated siblings (booking options, firm overview, etc.) 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?

It tells the agent exactly what inputs select the right record (company/plan name or 9-digit EIN) and routes the agent forward to get_401k_scorecard_preview with the returned plan_id. There are no explicit when-not-to-use exclusions, but the sibling set is unrelated so the omission is low-risk.

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 establish readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds genuinely useful context beyond that: the data provenance (latest public Form 5500-SF filing), the band value set, and an explicit scope disclaimer ('Not an audit or compliance determination').

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 the tool's purpose and payload, then the provenance and scope caveat. Every clause earns its place: the band enumeration, the filing source, and the compliance disclaimer all affect 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 burden and does so by naming all return fields and the data source. Minor gaps remain — what happens when the plan has no filing, or whether results are cached — but nothing critical for correct invocation is missing.

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

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

Only one parameter, and schema coverage is 100% — the schema already documents plan_id format and its origin in find_401k_plan. The description adds nothing further about the parameter, so the baseline of 3 applies.

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

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

States a specific verb and resource — a preview of the ClearMind 401(k) Scorecard for a single plan — and enumerates the returned components (0-100 score, band, headline, story). It does not explicitly name a sibling (e.g., find_401k_plan), so differentiation is implied rather than stated.

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 scopes usage to 'one plan' and points to the Scorecard page for the full breakdown, which implies this is the lightweight variant. However, it never states when to prefer this over the full Scorecard or how a plan_id is obtained, leaving 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?

Annotations already cover read-only/idempotent/non-destructive/closed-world safety, so the bar is lower. The description still adds real value by stating the server never submits anyone's information, clarifying this is a self-serve handoff rather than an action taken on the user's behalf.

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 with the purpose front-loaded and the team routing given immediately after. No filler and no redundancy with schema or annotations.

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 low-complexity, single-enum, read-only tool with no output schema, the description covers what is returned, who each option applies to, and the no-submission behavior. Nothing an agent needs to call it correctly is missing.

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

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

Schema coverage is 100%, so baseline is 3, but the description goes further by mapping the audience values to concrete outcomes: individual_or_family to Private Wealth, business_owner to Workplace Retirement. That meaning isn't in the enum labels alone.

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

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

States a specific verb+resource — returns booking links and contact details for reaching ClearMind Capital — and is clearly distinct from siblings like get_firm_overview or find_401k_plan. An agent can tell what this returns without opening the schema.

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

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

Gives concrete routing context (individuals/families vs. business owners/HR leads) so the agent knows who to fetch options for. It does not name sibling tools or explicitly state when NOT to use it, so it stops short of full alternative routing.

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

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

Annotations already declare readOnlyHint, idempotentHint, closed-world and non-destructive, so the safety profile is covered. The description adds real value beyond that by disclosing the exact content categories returned (credentials, CRD, fee schedules, Form ADV/SEC links), which tells the agent what it can and cannot answer from this call.

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, both front-loaded: the identity of the firm comes first, then the enumerated content scope, then the usage rule. Every clause carries information and there is no 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?

No output schema exists, but the description compensates by listing the returned fact categories in detail, which is all an agent needs to decide whether this tool answers a given question about the firm.

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 tool takes zero parameters, so per the rubric the baseline is 4. The description adds nothing about arguments because none exist, and nothing is needed.

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 resource (facts about the firm itself) and enumerates exactly what is covered: services, advisers, fee schedules, custodian, CRD IDs, contacts, and regulatory links. No sibling tool covers firm-level facts, so the agent can disambiguate immediately.

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?

'Use for any question about the firm itself' gives a clear trigger condition and implicitly excludes client-specific or plan-specific queries handled by siblings. It does not name an alternative tool explicitly, so it stops short of 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?

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so safety is covered. The description adds genuinely useful behavior beyond annotations: result ordering (newest first) and the shape of returned fields (titles, summaries, dates, URLs). It omits pagination behavior given the limit parameter, which is a minor gap.

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, zero filler. Scope and ordering are front-loaded, filtering and return values follow. Every clause carries information.

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

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

With no output schema, the description usefully enumerates returned fields and ordering, and annotations fully cover the safety profile. The only shortfall is the unexplained limit parameter and pagination behavior for a list 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 coverage is 67% and the schema already documents kind (with values) and topic. The description restates those filterable fields and confirms both are optional, but adds no syntax or format detail beyond the schema; the limit parameter is undocumented in both places. 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 (List) and resource (Clarity Corner education library) with scope and ordering (newest first). It is clearly distinguished from the sibling tools by resource, though it does not explicitly contrast itself with search_answers, which could plausibly retrieve similar content.

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?

'Optionally filter by topic or kind' implies the filtering use case, but there is no statement of when to prefer this over search_answers or other retrieval siblings, and no exclusions or prerequisites. Usage is inferable rather than stated.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered by structured data. The description adds genuinely useful context beyond that: it discloses the return shape ('published answer text with its source URL') and a scope disclaimer ('General education only'), which tells the agent not to treat results as personalized advice. It does not cover result-count or ranking behavior, keeping it short of a 5.

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

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

The verb and resource are front-loaded in the first clause, followed by the topical enumeration, the return description, and the disclaimer. The long mid-sentence list of domains is heavy but each item is informative; there is no filler 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?

With no output schema, the description correctly carries the burden of describing returns ('published answer text with its source URL') and does so. For a two-parameter read-only search this is nearly sufficient; only the handling of the limit parameter and any ranking/pagination behavior are left unstated.

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 50%: 'query' is documented in the schema, but 'limit' (default 5, max 10) is not described in either place. The description's topic enumeration effectively explains what kind of query text is expected, which adds some meaning, but it says nothing about controlling result count or how limit interacts with the search.

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 gives a specific verb and resource ('Search ClearMind Capital's published answers and Clarity Corner articles') and enumerates the topical scope, so the agent knows exactly what corpus is being searched. It is clear on its own but never explicitly distinguishes itself from overlapping siblings such as list_clarity_corner or find_401k_plan, which cover adjacent content.

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

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

Usage is only implied through the topic list; there is no statement of when this search should be preferred over list_clarity_corner or find_401k_plan, nor any exclusion guidance. The closing line 'General education only' hints at scope and limits, but no alternative tool is named or routed to.

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