ScoreMindset — capital mental

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 for a mutation. It does not say whether the cancellation is reversible, what happens if the request is already confirmed, whether permissions are required, or what the result looks like.

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 short, front-loaded sentence with no filler, so it reads efficiently. It is concise to a fault, though, leaving no room for the behavioral and parameter context the tool needs.

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 mutation tool with no annotations, no output schema, and an undocumented identifier parameter, the description is too thin. An agent cannot determine preconditions, side effects, or error behavior before invoking it.

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 one required parameter, action_id, with 0% schema description coverage, so the schema explains nothing beyond the name and integer type. The description never clarifies what an action_id refers to, how to obtain it, or how it maps to "une demande preparee".

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 ("j'annule") and a specific resource ("une demande preparee non confirmee"), so the agent knows this cancels an unconfirmed/prepared request rather than an arbitrary action. It implicitly contrasts with the sibling confirmer_action but never names it.

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 qualifier "non confirmee" implies the tool only applies to requests that have not yet been confirmed, which gives a usable usage boundary. However there is no explicit when-to-use/when-not-to-use guidance and no mention of confirmer_action as the alternative for confirmed flows.

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 behavioral burden. It does disclose the side effects (saving the lead, notifying the operator), which is valuable. But it is silent on idempotency, what happens if the action_id is stale or the confirmation was never given, and whether the operation is reversible.

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 sentence that front-loads the precondition (explicit confirmation) before the effects. No padding, though the phrasing is slightly terse for the amount of behavior implied.

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 one-parameter mutation with no annotations and no output schema, the description covers the trigger and the side effects, which is the core need. It falls short on failure modes and the provenance of action_id, leaving gaps an agent would have to guess at.

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% and there is one required parameter, action_id. The description only obliquely hints at its meaning via 'une demande preparee' and never states the expected format, source, or validity constraints of the identifier the agent must supply.

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?

Names a specific verb (execute) and resource (a prepared request), and adds the concrete effects: 'enregistre le lead et previent l'operatrice'. This distinguishes it reasonably from siblings like statut_actions and annuler_action, though it never names them 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?

The gating condition 'APRES confirmation explicite du visiteur' tells the agent when this tool becomes callable, which is real usage guidance. However, it does not name the alternative tools (e.g. annuler_action for rejection, proposer_demande_rappel for deferral) or state what to do if confirmation is absent.

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 of behavioral disclosure. It reveals only that the result is a daily question drawn from the book's ten questions and includes a work track, but it does not state whether the operation is read-only, how the daily question is selected, or what the response looks like.

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 compact phrase with no filler, and it front-loads the core idea 'La question du jour' before adding context. It loses a point only because the opening phrase largely repeats the tool name and the description reads more like a label than an instructive 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?

With no output schema and no annotations, the description should clarify what the agent receives and how to interpret it. It mentions the question and the work track, but leaves unspecified the output format, the meaning of 'piste de travail', and how 'daily' selection works, which is a meaningful gap even for a no-parameter 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?

The tool declares zero parameters, so there is nothing for the description to clarify. The baseline of 4 for a no-parameter tool applies; the schema coverage of 100% is vacuously satisfied.

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 identifies the resource as 'La question du jour' and adds provenance ('tirée des 10 questions du livre') plus a component ('avec sa piste de travail'), so it is more than a bare tautology. However, it is a noun phrase with no verb or explicit operation, and it does not distinguish the tool from siblings like get_test or search_book.

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 gives no guidance on when to use this tool versus alternatives, no exclusions, and no context about selection criteria such as date or randomness. An agent must infer usage from the 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?

With no annotations, the description carries the full burden. It states what the tool does (details a test) and what it returns (dimensions, profiles, method), but it does not explicitly say whether it is read-only, what side effects (if any) occur, or whether authentication is required. For a 'get' tool, read-only behavior is implied, but not disclosed. It adds minimal behavioral context beyond the action itself; it does not contradict anything, but does not go beyond what is obvious.

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 efficiently states the purpose, the content of the response, and the parameter source. It is front-loaded with the primary action and includes the essential pointer for the parameter. No filler or redundant information; every word 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?

For a simple tool with one required parameter and no output schema, the description is complete. It tells the agent what the tool does, what it returns (dimensions, profiles, method), and how to obtain the required slug. There are no obvious gaps that would prevent correct invocation. The tool's simplicity means the description does not need to elaborate on return formats or error handling.

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 schema has no description for the 'slug' parameter (coverage 0%), so the description must compensate. It does so by stating 'Slug via list_tests', which tells the agent that the slug is a test identifier obtained from list_tests. This adds meaningful context about the parameter's source and format, which is valuable since the schema provides no hints. It does not specify the exact format, but pointing to list_tests is sufficient for 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 clearly states the action ('Détaille un test' – details a test), the specific resource (a test), and what is detailed (dimensions mesurées, profils possibles, méthode). It distinguishes from siblings by indicating that it retrieves a single test's details, while list_tests presumably lists available tests. The phrase 'Slug via list_tests' further clarifies that this tool is for a specific test identified by a slug obtained from list_tests.

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

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

The description provides implicit guidance by saying 'Slug via list_tests', which tells the agent to call list_tests first to obtain the slug. This implies the tool is used when you need details of a specific test and have already identified it via list_tests. It does not explicitly list alternatives or exclusions, but the purpose is clear enough that an agent would not confuse it with list_tests or search_book. A small gap is the lack of explicit 'use this when...' statement.

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 of behavioral disclosure. It does add useful scope: only free tests are included and the returned fields are listed. However, it does not disclose potential pagination, ordering, authentication needs, or response shape, though a simple read-only list operation reduces the risk.

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 compact French sentence delivers the action, scope, and expected fields without filler. It is front-loaded and every word 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?

For a parameterless list operation, the description is nearly complete: it names the resource, limits scope to free tests, and enumerates return fields since no output schema exists. It could add explicit guidance on using get_test for individual test details, but that is a minor gap.

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 parameter semantics are not a concern. The description correctly focuses on the output fields instead. Per calibration, 0 parameters earns a baseline 4.

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

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

The description uses a specific verb ('Liste') and a precise resource ('les tests psychologiques gratuits de ScoreMindset'), and enumerates the returned fields (slug, titre, durée, thème). This clearly distinguishes list_tests from sibling get_test: one enumerates, the other retrieves a single item.

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

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

The context is implicit: this tool is for listing free psychological tests, while get_test likely retrieves a specific test. However, there is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named, so the agent must infer the routing from the name and sibling list.

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 provided, the description carries the full burden of behavioral disclosure. It does not explicitly state that the tool is read-only or has no side effects, nor does it describe the output format or any potential errors. The verb 'Présente' implies a non-mutating action, but it is not explicit, and nothing is said about response structure or server interaction.

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, front-loaded sentence that immediately names the tool's purpose ('Présente ScoreMindset') and then lists the covered aspects. It is concise with no filler or redundancy, earning a high score for structure.

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 simplicity (no parameters, no output schema), the description covers its core purpose adequately. It explains what content is presented (mission, tools, pages, limits) and leaves little ambiguity. It could optionally mention that it serves as an introduction or entry point, but that is not essential for correct usage.

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

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

The input schema has zero parameters, so the baseline for this dimension is 4. The description adds no parameter information because none exists. It correctly omits any discussion of arguments, and the schema fully covers the (empty) parameter set.

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 presents ScoreMindset, specifying the content areas: mission, public tools, key pages, and server limits. It uses a specific verb ('Présente') and names the resource, distinguishing it from sibling tools that focus on individual actions like daily_question or list_tests.

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. The description does not mention that it should be called first for an overview, nor does it mention any conditions or exclusions. It simply describes what it presents, leaving the usage context entirely implicit.

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 so well: it discloses that nothing is written without confirmation, that an action_id is returned, and that it expires in 10 minutes. It omits auth/permission requirements and expiry-failure behavior, keeping it from 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?

Three short sentences, front-loaded with the write/consent constraint, with no filler. Efficient and readable.

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 4-param mutation with no output schema, it covers the essential flow (preconditions, returned token, confirmation gate). The undocumented p一篇renom/message fields and lack of auth context are the remaining gaps.

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 0% so the description must compensate; it usefully clarifies that consent must be true (beyond the schema's default false) and that email is required. However prenom and message are left entirely undocumented, so only half the parameters gain meaning.

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 (prepare a callback request) and clarifies the write is consented, making the two-step nature explicit. It is distinguishable from generic siblings, though it does not name confirmer_action/annuler_action which are clearly the follow-on tools.

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 clear preconditions (email AND consent=true required) and states the output must be user-confirmed, which is effectively the when-to-use contract. It stops short of naming confirmer_action as the required next step.

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 present, so the description carries the full burden of behavioral disclosure. It says nothing about whether this is a read-only content lookup, what invoking it returns, or whether any side effects exist—it only names the content.

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 short phrase with no filler, and the core subject—the six dimensions—is front-loaded. For a parameterless content resource, this length is appropriate.

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 zero-parameter tool with no output schema, the description provides only the identity of the content. It does not explain how an agent should use this information, when it is relevant, or what the tool actually returns, leaving selection among siblings largely to inference.

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 parameter semantics are mostly moot and the baseline is 4. The description's mention of dimensions and levers is content description rather than parameter guidance, but no parameter ambiguity exists.

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 French noun phrase identifies a specific resource: the six dimensions of the Score Mindset /1000 signature assessment, together with their levers. It is not a tautology and gives a clear subject, though it lacks an explicit verb and does not explicitly differentiate itself from sibling tools like list_tests.

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 gives no guidance on when to use this tool versus siblings such as daily_question, get_test, list_tests, manifest, or search_book. No contexts, exclusions, or alternatives are mentioned.

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 burden. It states it returns the most relevant passages, which implies a read-only operation, but does not explicitly declare non-destructive behavior, permissions, or other constraints. It adds scope context but lacks explicit behavioral disclosures.

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, front-loaded sentence with no filler. It efficiently conveys the purpose and scope without excess length.

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 search tool with no output schema and no parameter descriptions, the description is incomplete. It does not explain the 'limit' parameter, the query format, or the structure of the returned passages. Significant gaps remain for a correct invocation.

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 0%, so the description must compensate. It indirectly covers the 'query' by describing the search, but completely ignores the 'limit' parameter. The description adds minimal value for understanding the 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 clearly states the action (search) and the specific resource (the full text of a specific book), and even enumerates the topics covered. This makes it unambiguous and distinct from the sibling tools, which are about tests, questions, and scoring.

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

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

The description implies when to use it (when you need to search the book's content) and the sibling names make the distinction obvious. However, it does not explicitly state exclusions or alternative conditions, so it stops short of full 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?

With no annotations, the description carries the full burden, and it does disclose one behavioral trait: the output contains no personal data ('aucune donnee personnelle'). It does not state whether the operation is read-only, whether it requires authorization, or how the counters are scoped, so the disclosure is 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?

A single short sentence with no filler, front-loading the return value and appending the privacy note. Efficient, though the parenthetical slightly interrupts the core statement.

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 convey the return shape, and it does indicate the response is counters of prepared entries. However, it leaves the domain of those counters and the tool's relationship to the action-related siblings ambiguous, which matters for correct invocation.

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 the schema has nothing to document and the description's lack of parameter detail is expected. Baseline 4 applies for a no-parameter tool.

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 verb and resource ('Je renvoie les compteurs des ecritures preparees'), so it is not a pure tautology. However, the resource named ('ecritures preparees') does not obviously map to the tool name 'statut_actions', and there is no differentiation from siblings like confirmer_action or annuler_action. An agent could reasonably wonder whether this is accounting counters or action-status counters.

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 guidance on when to use this tool versus a sibling, no prerequisites, and no exclusions. The description merely states what is returned, leaving the agent to infer the context of use.

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