MyLearners K53 & DMV Practice Test

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

With no annotations, the description must carry the full behavioral burden; it usefully discloses read-only safety and that results include execution timing and column metadata. It omits material behaviors an agent needs: row/result-size limits, timeouts, error semantics, and whether malformed statements fail closed as read-only.

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

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

A single, front-loaded sentence with the action and target stated first and no wasted clauses. The trailing mention of timing and column metadata is slightly tacked on but earns its place given there is no output schema.

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 should describe the return shape; it partially does by naming execution timing and column metadata but not row format or limits. For a raw-SQL tool with no annotations, the operational boundaries (limits, failure modes) 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 100%, so both sql and params are already documented in the schema. The description only reinforces the sql parameter's allowed statement types and adds nothing about the params binding array. Baseline 3 applies 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?

"Execute a read-only SQL query against the K53 Co-Pilot Cloudflare D1 database" names a specific verb, resource, and target system, so the agent knows exactly what the tool does. It does not explicitly contrast itself with siblings such as search or get_candidate_dossier, leaving the agent to infer that this is the raw-SQL escape hatch.

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 by constraining the statement type (SELECT or PRAGMA only), which signals an ad-hoc/read query tool. Beyond that restriction it gives no guidance on when to prefer this over the purpose-built sibling tools, nor any prerequisites or exclusions.

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

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

No annotations are provided, so the description carries the full burden. It does disclose the payload shape (readiness, hurdle breakdown, trajectory, top 5 mistakes, coaching), which is valuable since there is no output schema, but it omits key behavioral facts: whether the call is read-only, what happens if zero or multiple candidates match (0 required params), and whether ambiguous name lookups error or return a list.

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, no filler, with the purpose front-loaded ahead of the return-value inventory. The second sentence is a dense but useful enumeration rather than padding.

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

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

Because there is no output schema, the description rightly summarizes return contents, which covers the biggest gap. However, with no annotations and zero required parameters on a multi-key lookup, the definition is silent on resolution/ambiguity behavior and on safety profile, leaving a real gap for an agent calling it blind.

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

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

Schema description coverage is 100%, so the schema already documents all three parameters; baseline 3 applies. The description restates the lookup keys ('by ID, email, or name') and so adds only marginal mapping value, and does not explain precedence or what happens when 'query' matches multiple candidates.

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 ('Retrieve') and resource ('deep diagnostic dossier for a learner candidate') and enumerates the lookup keys. The adjective 'deep diagnostic' implicitly separates it from the sibling get_candidate_summary, but the description never names that sibling to make the distinction explicit.

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: 'deep diagnostic' hints this is the heavier alternative to get_candidate_summary, but there is no explicit when-to-use / when-not-to-use statement or named alternative. An agent must infer the routing rule from the sibling name alone.

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

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

No annotations are provided, so the description carries full burden. It discloses the read-only nature and the data returned (readiness, scores, trajectory, weaknesses), but says nothing about permissions, behavior when multiple identifiers conflict or are omitted, or error conditions.

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 front-loaded sentences with no waste. The alias declaration leads, and the payload description follows immediately.

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 lookup tool with 100% schema coverage and no output schema, the description is adequate on payload but incomplete on lookup semantics: with no required parameters, an agent needs to know at least one identifier is needed and how they combine.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds no guidance on how the three lookup keys (email, query, candidate_id) interact or which takes precedence, which is a meaningful gap given zero required parameters.

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

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

The description states a specific verb and resource ('look up a learner candidate') and discloses the returned data (readiness, scores, test trajectory, weaknesses). The alias framing ('Alias for get_candidate_dossier') clearly ties it to a sibling, though it doesn't explain why two names exist.

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?

Calling it an alias implies this can substitute for get_candidate_dossier, which is useful routing context. However, it never says when to prefer this alias over the canonical tool, nor which of the three lookup parameters to use.

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. It discloses the returned content categories (pass rate vs baseline, failure deficits, center rankings), which adds useful behavioral context, but it does not mention data freshness, source limitations, authentication, or any other operational traits.

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

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

A single, front-loaded sentence that packs the specific resource and key return dimensions into a parenthetical without waste. Every phrase 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?

The description is reasonably complete for a no-argument reporting tool but leaves some gaps: no output schema exists, and the description does not specify aggregation period, geographic scope, or data format. It conveys enough for an agent to understand the tool's purpose but not enough to anticipate the shape of the response.

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 has zero parameters, so the baseline score is 4. No additional parameter semantics are needed or present.

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 (Retrieve) and resource (official traffic department driving license exam outcomes), with concrete details about what the outcomes include: pass rate vs national baseline, failure hurdle deficits, and center rankings. It is clear what the tool does, though it does not explicitly differentiate itself from siblings like get_platform_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?

Provides no guidance on when to use this tool versus alternatives such as get_platform_stats or get_top_mistakes. The description implies usage by naming the specific data domain, but there are no explicit when-to-use or when-not-to-use instructions.

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 disclosure burden. 'Retrieve' weakly implies a read, but it says nothing about aggregation scope, data freshness, cost, or whether this is an expensive executive-level rollup — all relevant for a no-parameter platform-wide stats call. Undefined acronym 'DLTC' adds opacity rather than clarity.

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

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

One sentence is appropriately sized, but it is a stacked list of buzzwords ('comprehensive', 'velocity', 'efficacy') rather than front-loaded, concrete information about the return payload. Words are spent on tone rather than meaning.

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?

There is no output schema and no annotations, so the description is the only place an agent could learn what comes back. It names abstract categories but never enumerates fields, granularity, or time window, and leaves 'DLTC' undefined — insufficient for a reader who cannot inspect a response shape.

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 there is nothing for the description to disambiguate. Baseline 4 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 verb 'Retrieve' plus a list of data domains signals this is a read-only stats aggregator, but the domains are described in marketing jargon ('executive telemetry', 'test volume velocity', 'DLTC pass rate efficacy') that never resolves into what an agent would actually get back. It also never distinguishes this from the sibling get_outcomes_summary, which appears to cover overlapping ground.

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

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

There is no statement of when to reach for this tool versus get_outcomes_summary or get_candidate_summary, both of which sound adjacent. No prerequisites or exclusions are given; the agent must infer everything from the title.

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

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

With no annotations, the description must carry the full behavioral load. It usefully discloses the aggregation scope ('across all learners') and the returned measures (error frequencies, candidate counts, mastery recovery rates), but says nothing about read-only safety, whether results are ranked, or how the limit behaves.

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

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

A single front-loaded sentence with the resource and scope stated first and the return measures appended. It is dense but every clause carries information; 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?

With no output schema and no annotations, the description partially compensates by naming the returned metrics and the segmentation. Remaining gaps are the read-only/ranking behavior and limit semantics, but an agent has enough to select and call it correctly.

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

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

Schema coverage is 100%, so the enum and limit defaults are already documented. The description only reframes the sections as statutory sections, adding minor domain meaning beyond the schema, which fits the baseline 3.

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

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

States a specific verb ('Retrieve') and a specific aggregate resource ('South Africa's most failed K53 test questions across all learners'), with the segmentation dimension named. It does not, however, explicitly distinguish itself from siblings such as get_outcomes_summary or get_platform_stats, so the agent must infer the boundary.

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: 'across all learners' hints that this is the aggregate-analytics option versus candidate-specific tools like get_candidate_dossier. There is no explicit when-to-use, when-not-to-use, or named alternative.

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 are provided, so the description carries the full behavioral burden. It discloses that matches are 'live' and spans multiple entity types, which helps, but it does not describe result ranking, limits, pagination, permissions, or output shape. For a search tool this is moderate but incomplete transparency.

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 efficient sentence with the search scope front-loaded. 'Intelligently' is mild filler but does not obscure the content, and no sentences are 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?

For a one-parameter search tool the description covers the searchable domains and live matching, but with no output schema it leaves the return shape, result limits, and ranking behavior unexplained. It also lacks sibling routing guidance, so it is adequate but not complete.

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

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

Schema description coverage is 100%, and the query parameter already documents acceptable inputs with examples. The description only restates the searchable domains, adding little parameter-level meaning beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description clearly states a specific verb, 'search', and enumerates the resource domains: candidates, common mistakes, DLTC testing centers, and database schemas. It does not explicitly differentiate this broad search from specialized siblings like get_top_mistakes, get_candidate_dossier, or execute_sql, and 'Intelligently' is vague, but the core purpose is clear.

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

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

There is no explicit guidance on when to use this search versus alternatives such as get_candidate_dossier or get_top_mistakes. The cross-domain scope implies broad lookup usage, but the agent must infer that from the description, and no exclusions or preferred conditions are given.

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; 'Return' signals a read-only, side-effect-free operation and it names what is disclosed (identity, service metadata, connection status). It does not say whether authentication is mandatory or how failures/partial connectivity are reported, so the behavioral picture is only partly filled in.

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

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

A single front-loaded sentence listing exactly the three things returned, with no filler or 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?

Since there is no output schema, the description must enumerate return values and it does so in three clear categories. For a zero-parameter diagnostic tool this is close to sufficient; only the failure/partial-status reporting is 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?

The tool takes no parameters, so there is nothing for the description to disambiguate; baseline 4 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 ('Return') with three named resources: authenticated platform identity, service metadata, and database connection status. This clearly sets it apart from the data-oriented siblings (execute_sql, get_platform_stats, etc.) as a diagnostic/identity call, though it does not name a sibling 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?

Usage is only implied: the mention of 'authenticated' identity and 'database connection status' suggests a verify-connectivity/verify-credentials check, but the description never states when to call it or what to use instead when only data is needed.

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