TakeMeUp.cv – CV & Resume Builder

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

Goes well beyond the annotations: it discloses the default status, the free-account cap of 5 applications versus unlimited PRO, and — crucially — that supplying only jobUrl causes the server to fetch the page and that the fetched text must be treated as untrusted. That is prompt-injection-relevant context an agent could not infer from readOnlyHint/openWorldHint alone.

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-loads the action and the "at least a company or role" constraint before the conditional linkage rules and quota caveat. Every sentence carries information, though the dependency conditions (cvId/jobDescription, jobUrl-alone) read as slightly compressed clauses that could be cleaner as a short list.

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 6-parameter mutation with no output schema, the description covers creation defaults, input minimums, conditional fetch behavior, untrusted-content handling, and plan quotas. The one gap is the return value: it never hints what the caller gets back (e.g., a new application ID or the populated record), which matters for a create tool with no output 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 coverage is 100%, so the baseline is 3, but the description adds genuine cross-parameter semantics the schema only states piecemeal: cvId pairs with jobDescription, and jobUrl alone triggers page fetching and field population. The remaining parameters (locale, company, roleTitle) are left entirely to the schema, which is fine at full coverage.

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

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

Opens with a specific verb and resource ("Add a job to the user's job application tracker") and pins down the resulting state (status "saved"). The verb "add" together with an explicit creation default distinguishes it from the sibling update_takemeup_application without needing to open either 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?

Explicitly names the trigger condition ("Use when the user saves or applies for a job") and states the minimum input requirement ("Give at least a company or role"). It does not explicitly route to update_takemeup_application for jobs already tracked, so the create-vs-update boundary is implied by the verb 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 readOnly, idempotent, non-destructive, and closed-world, so safety is covered. The description adds genuinely new context: 'No AI, no cost' and the concrete nature of the findings returned, which an agent could not infer from structured fields alone.

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 action and the returned artifact, followed by a tight usage condition. Dense but every clause carries information; slightly long single sentence but not wasteful.

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, no-output-schema tool, the description supplies enough: what it does, what comes back, that it is free and non-AI, and the triggering intent. Only the sibling-routing and locale nuance are 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 cvId and locale are already documented in the schema; a baseline of 3 applies. The description only implies single-CV scoping ('one of the user's CVs') and adds nothing about the locale parameter or its defaulting behavior.

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 (run ATS check), resource (a CV), and even the output shape (0–100 score plus findings like weak verbs, missing dates, unquantified bullets). However, with many overlapping siblings (find_takemeup_weak_achievements, detect_takemeup_buzzwords, roast_takemeup_cv), it does not name what distinguishes it from those.

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 closing clause 'Use when the user asks whether their CV or resume is ATS-friendly or will pass automated screening' gives a real trigger condition. But there is no guidance on when NOT to use it or which sibling (weak-achievements, buzzwords) to prefer for overlapping diagnoses.

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 flag destructiveHint=true and openWorldHint=true; the description adds the crucial concrete consequence — approving shares the user's contact details with the recruiter — which is exactly the kind of context annotations cannot convey. It stops short of saying the decision is final or what happens on decline (no data shared, request left pending?).

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 efficient sentences that lead with the action and then the consequence; nothing is wasted. It is appropriately short for the operation, and the key side effect is front-loaded rather than buried.

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-required-param mutation with an output schema absent, the definition covers the action, the data-sharing consequence, and (via schema) where ids come from. Minor gap: it never states whether the decision can be changed afterward or what a decline does, which matters for an idempotentHint=false, destructive 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 100% and the field descriptions already explain cvId source (list_my_takemeup_cvs), interestId source (get_takemeup_get_found), the decision enum, and locale fallback. The description adds nothing about parameters, so 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 pair (approve or decline) and a specific resource (a recruiter's contact request tied to one of the user's CVs). It is clearly distinguishable from read-side siblings like get_takemeup_get_found, though it does not name them.

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 context (a pending recruiter request exists) and the schema's interestId field points to get_takemeup_get_found, but the description itself gives no explicit when-to-use or when-not-to-use guidance or names an 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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety bar is met. The description adds real behavioral context beyond them: 'No AI, no cost' (deterministic, free) and that the rule set is country-dependent ('for the CV's country'). It does not describe the shape of the returned flags or links.

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 action, with cost and timing pushed to the end where they belong. The colon-embedded flag list makes the middle clause dense, but every sentence carries information an agent can act on.

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 read-only, two-parameter tool with full schema coverage and no output schema, the description covers purpose, cost, scope and timing adequately. The one gap is return shape — an agent cannot tell whether it gets a boolean, a flag list, or links, though 'flags details such as...' hints at a findings list.

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

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

Schema coverage is 100%, so cvId and the locale enum are already fully documented in the schema. The description contributes only the implicit notion of a country-sensitive check and never mentions the locale parameter or how it affects output language, which the schema already covers. 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 ('Run TakeMeUp's free GDPR and personal-data check on one of the user's CVs') and names the exact artifacts it flags (date of birth, photo, marital status, nationality). The GDPR/discrimination framing makes it unambiguous against sibling analysis tools like analyze_takemeup_cv_ats or check_takemeup_six_second.

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 a clear trigger condition: 'Use before sending a CV to an employer in the EU,' which also implies a geographic exclusion. It never names alternatives (e.g., the ATS or six-second check) or states what to do for non-EU CVs, so routing is informative but not fully disambiguating.

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 mark this as non-readOnly and non-idempotent, which is ambiguous, but the description clarifies the output is findings 'with fixes' and discloses the PRO entitlement plus AI-consent prerequisite — context annotations do not carry. What remains unstated is whether any CV content is modified; 'with fixes' implies suggestions rather than writes, but this is not made explicit.

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 plus two short clauses; the core purpose is front-loaded and every fragment carries information. The 'PRO and AI consent.' sentence is telegraphic but efficient rather than padded.

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 two-parameter analysis tool with full schema coverage, the description covers purpose, audience, prereqs, and that fixes are returned. With no output schema present, it could say more about the shape of the findings, which is the only meaningful 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?

Schema description coverage is 100%, and the cvId field even points to list_my_takemeup_cvs. The description adds nothing about cvId or the locale enum, so baseline 3 applies since the schema does all the work.

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 ('Find what makes... look dated') and resource (the user's CV), and enumerates concrete signals: old technologies, outdated phrasing, graduation years revealing age. It is clearly distinct from siblings like roast_takemeup_cv or run_takemeup_authenticity, though it does not explicitly name or contrast with any of them.

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 experienced candidates worried about age bias' gives a clear usage condition and audience. It also flags prerequisites ('PRO and AI consent'), but offers no explicit exclusions or alternative tools for adjacent analyses such as buzzword detection.

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 and non-destructive, so safety is covered. The description adds genuinely useful context beyond them: "Free, no AI" tells the agent there is no billing or AI-consent implication, which matters because several siblings do consume AI runs/credits.

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

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

Two tight sentences with no filler. The output content and the placement expectation are front-loaded, and the usage/routing hint follows.

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 two-parameter read-only tool with full schema coverage and declared annotations, the description supplies purpose, trigger and cost model. It could say a bit more about the shape of the answer (e.g. a list of terms vs. a scored verdict), but nothing essential 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 description coverage is 100%: cvId already tells the agent to source the id from list_my_takemeup_cvs, and locale is fully enumerated with a documented default. The description adds nothing about parameters, 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: extracts the key terms a recruiter should spot in a six-second skim of a CV, plus the concrete expectation that they belong in the top third of page 1. That framing distinguishes it from get_takemeup_cv_preview, which merely shows what is currently rendered.

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 an explicit trigger condition ("Use when the user asks whether their CV makes a strong first impression") and points to the complementary sibling get_takemeup_cv_preview. It does not state when NOT to use it or how it relates to the other CV review tools (roast, ats, buzzwords), so it stops short of full routing guidance.

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=false, destructive=false, openWorld=false, so the mutation profile is covered. The description adds value beyond that by disclosing the return values (id + editor link), the optional template parameter's behavioral effect, and the free-cost trait. It does not restate or contradict any annotation.

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 tightly-packed sentences with no filler: the core action and return value come first, followed by the usage context and next-step tool. Every clause carries information the agent 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 simple zero-required-parameter creation tool with no output schema, the description covers the action, return values, cost, and the follow-up save step, which is sufficient to invoke it correctly. The only gap is the lack of explicit routing against the sibling creation tools.

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 both locale and templateId are already documented with examples, establishing the baseline of 3. The description only hints at the template option ('optionally with a template') without adding syntax or default behavior beyond what the schema says.

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 ('Create a new, empty CV') and adds the return payload (id and editor link), so the agent knows exactly what it gets back. It does not explicitly distinguish itself from close siblings like create_takemeup_cv_from_role or create_takemeup_one_click_cv, leaving a small ambiguity for an agent choosing between creation variants.

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 a clear situational trigger ('start a CV from scratch or from a file the user attached') and names the natural follow-up tool save_takemeup_cv, plus a 'Free' cost signal. It stops short of excluding the sibling creation tools, so the agent must infer when to prefer create_takemeup_cv_from_role instead.

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 this is a non-read-only, non-destructive, non-idempotent, closed-world write. The description adds genuinely non-structured facts: the result is ~70% complete, ready to personalise, free of charge, and involves no AI generation. It stops short of saying the CV is persisted immediately or that repeated calls create separate CVs, which matters given idempotentHint=false.

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: the first defines the artifact and its scope, the second gives the trigger. The em-dash role list is compact and the key qualifiers (70% complete, free, no AI) are front-loaded. Nothing is redundant with the title.

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, three simple params, and full schema coverage, the description covers what an agent needs to decide and to call it via roleId. Minor gaps remain around what identifier is returned and whether locale should be set explicitly, but the essential decision information is present.

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 roleId/locale are enums with documented defaults, so the schema does the heavy lifting. The description usefully spells out the six role presets in human-readable form, but says nothing about locale selection semantics or templateId, so it adds only modest value beyond the schema.

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

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

States a specific verb and resource ("Create a new CV pre-filled") plus the concrete presets covered (driver, warehouse, healthcare assistant, cleaner, retail, food service), so the agent knows exactly what comes back. It does not name the nearest sibling (create_takemeup_cv or run_takemeup_one_click_cv), so the distinction is inferred 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?

"Use when the user needs a CV for one of these roles and has little to start from" gives a clear triggering condition tied to low user input. There is no explicit when-not or named alternative, so an agent facing a user who wants a CV for a non-listed role has to reason it out.

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 disclose that the operation is destructive, non-idempotent, and open-world. The description adds meaningful context beyond that: creating a link makes the CV publicly viewable by anyone with the link, and revoking takes it offline again. It does not explain regenerate behavior in detail, such as whether an old link is invalidated.

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 front-loaded and compact: it states the action, the PRO constraint, and the public-viewability consequence without wasted sentences. Every sentence contributes to understanding the operation.

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 rich annotations and full schema coverage, the description covers the key behavioral facts an agent needs: public exposure, PRO status, and revoke semantics. It is slightly incomplete because it does not clarify return values or precisely what regenerate does to an existing share link.

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 explains cvId, action, purpose, and locale. The description reinforces that the link points to one of the user's CVs and can be a link or QR code, but adds little parameter-level detail beyond the schema.

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

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

The description states a specific verb set (create, renew, revoke) and resource (public link or QR code to a CV), making the tool's purpose clear. It does not explicitly differentiate itself from nearby siblings such as get_takemeup_qr_code or get_takemeup_download_link, so it falls short of a 5.

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 gives a useful usage constraint: use only on the user's explicit request, and it clarifies that revoking takes the CV offline. However, it does not name alternatives or explain when to use this instead of adjacent tools like get_takemeup_qr_code or get_takemeup_download_link.

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?

Beyond the annotations, it explains that this creates a public page with no name or contact details, that recruiters can request a reveal, that it requires explicit user action, and what identifiers are returned. This gives strong behavioral context for a non-read-only, open-world operation.

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 compact and front-loaded: purpose, privacy scope, PRO requirement, safety constraint, and return values are all covered without filler. The terse 'PRO.' sentence is acceptable given the surrounding context.

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, but the description explains the returned fields ({stealthId, pseudoId, url}) and the need to retain stealthId for later removal. Combined with full schema parameter descriptions and annotations, an agent has enough information to call this 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 description coverage is 100%, so both cvId and locale are already well documented in the schema. The description adds only indirect context ('one of the user's CVs') and does not explain locale semantics beyond what the schema provides.

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: publish an anonymised version of a CV on a public page for recruiters. It also captures the stealth/anonymised scope and recruiter reveal flow, which distinguishes it from general CV sharing or revision siblings.

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 clearly constrains usage to the user's explicit request and notes PRO gating, which is important context. It does not explicitly name an alternative tool or compare against siblings like create_takemeup_share_link, but the publish/stealth condition is sufficiently clear.

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 readOnlyHint=false already in the annotations, the description usefully adds the prerequisites 'PRO and AI consent' — exactly the kind of gating context an agent needs (there is a sibling get_takemeup_ai_consent). It does not resolve the tension between readOnlyHint=false and 'suggest' (i.e., whether the CV is modified, whether addon credits are consumed, or whether results are persisted), which would have earned 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?

Two sentences plus a short gating fragment, with purpose front-loaded and no filler. The 'PRO and AI consent' fragment is efficient but slightly cryptic — it doesn't say what the agent should do when consent is absent.

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 2-parameter analysis tool with full schema coverage this is close to adequate, and the gating note is valuable. However, with no output schema, the description never characterizes the result shape (a list of flagged phrases with per-item replacements?) or whether findings are saved, which leaves the agent guessing at return behavior.

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 both parameters (cvId with a pointer to list_my_takemeup_cvs, and locale with a full enum) are documented in the schema itself. The description only implies that a single user CV is targeted; it adds no format or selection guidance beyond the schema, 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 target ('Find clichés and empty buzzwords in one of the user's CVs') plus the follow-up action ('suggest concrete replacements'), with concrete examples in parentheses. This is clearly distinguishable from neighboring analysis tools such as find_takemeup_weak_achievements or rewrite_takemeup_cv.

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 an explicit trigger condition: 'Use when the user wants their CV to sound less generic', which is a usable routing signal. It does not name alternatives (e.g., rewrite_takemeup_cv for full rewrites) or state when not to use it, so it falls short of a 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?

Adds useful prerequisite context (PRO subscription and AI consent requirements) that annotations don't carry. However, with readOnlyHint=false it is unclear whether the drafted explanations are persisted, whether the CV is modified, or whether this consumes an add-on run; the description says nothing about those side effects.

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

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

Two tight sentences: the capability first, then the trigger condition and prerequisites. No filler or repetition of the name.

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 the description does sufficiently characterize the return (draft explanations for CV and interviews). Minor gaps remain around what happens if no gaps are found and whether results are saved, but the essentials are covered.

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 both parameters are fully documented in the schema, including the enum locale and where to get cvId. The description adds no parameter-level meaning beyond that, 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+resource pair: find gaps in a CV's work history and draft explanations for CV and interviews. This is clearly distinguishable from siblings like check_takemeup_resume_aging or find_takemeup_weak_achievements, which target different CV issues.

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 a concrete trigger condition ('when the user has a break in employment they're worried about') plus prerequisites ('PRO and AI consent'), which is strong context. It stops short of naming alternatives or stating when not to use it, so not a 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, so the bar is lower, and the description still adds genuinely non-obvious behavior: the file opens in the user's browser while signed in and is NOT returned to the assistant. That last point is exactly the kind of fact an agent would otherwise get wrong.

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 core action front-loaded and the delivery-channel caveat as a short follow-up. Nothing could be removed without losing 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 correctly explains what the user gets (a JSON file in the browser) and why the agent sees no payload, plus the signed-in precondition. Adequate for a one-optional-parameter read tool; only minor detail like the export being complete/scoped-all-data is left implicit.

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 optional 'locale' parameter is fully documented in the schema, including the default-to-account-language behavior. The description adds no parameter meaning beyond that, 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 (downloads) and resource (all TakeMeUp data) and enumerates the included data domains (account, CVs, AI results, tracker, orders), which separates it from single-artifact exports like export_takemeup_cv_yaml or export_takemeup_europass. It stops short of naming a sibling explicitly, so differentiation is by scope rather than by routing.

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 phrasing 'Where the user downloads a copy of all their data' implies the triggering condition (a user-requested GDPR export), but there is no explicit when-to-use/when-not guidance and no named alternative for partial exports. The agent must infer that per-CV exports belong to different tools.

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. The description still adds value beyond them: it discloses the 'Free' cost tier and the exact return shape {yaml, filename} even though no output schema exists. Only gaps are auth/permission requirements and any size or rate limits.

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: purpose is front-loaded, then audience, then cost and return shape. Every sentence earns its place.

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

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

For a simple, read-only, two-parameter export with rich annotations and a stated return shape, an agent has enough to call it correctly. The only shortfall is the missing routing guidance against the other export/get-CV siblings.

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 both parameters are fully documented, including the enum and the hint to obtain cvId from list_my_takemeup_cvs. The description adds no parameter semantics beyond the schema, 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 ('Export ... CVs as YAML text') and even describes the output artifact ('a clean, structured copy for other tools or AI assistants'). Distinct from export siblings by format, but never explicitly contrasts itself with export_takemeup_europass or get_takemeup_cv.

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 implied by 'for other tools or AI assistants' and 'Free', but there is no explicit when-to-use statement, no conditions for choosing this over export_takemeup_europass or get_takemeup_cv, and no prerequisites such as needing the CV id from list_my_takemeup_cvs (that only appears 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?

Annotations only say readOnlyHint=false, idempotentHint=false, destructiveHint=false, which for an export is not self-explanatory; the description adds genuinely useful context by disclosing that the output is file content and that it may be truncated when very large. The "PRO" marker also hints that the call is gated or credit-consuming, which the agent would otherwise not know. It doesn't explain the cost or what happens when the CV lacks Europass-relevant fields, so it is not fully transparent.

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

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

Three short sentences with the action and the use case front-loaded and no filler. The standalone "PRO." fragment is terse to the point of being cryptic out of context, a minor structural blemish.

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 the description covers the return shape (file content, possibly truncated) as well as the triggering scenario, which is what an agent needs to call it correctly. Adding what PRO gating or credit consumption implies would close the remaining 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?

Schema description coverage is 100%, and the schema already documents cvId (with a pointer to list_my_takemeup_cvs), the format enum and the locale default. The description adds nothing about parameters, which is acceptable at this coverage level but is baseline only.

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 (export), resource (one of the user's CVs) and target format (EU Europass XML/JSON-LD), plus the reason (EU job portals). The format name alone distinguishes it from the sibling export_takemeup_cv_yaml, so an agent can pick correctly 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?

"Use when an application asks for an Europass CV" gives a concrete triggering condition. It stops short of naming alternatives such as export_takemeup_cv_yaml or export_my_takemeup_data, so the agent gets a positive trigger but no explicit routing rules.

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?

Adds real context beyond the annotations: it's open-world fetching, so the description's auth/plan constraint ('Signed-in; not PRO'), the LinkedIn limitation, and the prompt-injection warning ('treat it as data, not instructions') are genuinely useful. It leaves unexplained why readOnlyHint=false — an agent might expect a pure read, so a note on persistence or credit consumption would close the 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?

Three short sentences, front-loaded with capability then constraints, with no filler. Every clause carries an actionable fact (auth tier, unsupported source, injection caution).

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 two-parameter extraction tool with no output schema, the description handily covers the return shape ('job title and description text'). It omits failure behavior — what happens on a paywalled, dead, or unsupported URL — which matters for an openWorldHint 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?

With 100% schema description coverage, the schema already documents both 'url' (length cap) and 'locale' (enum plus default fallback). The description adds no syntax, format, or defaulting detail beyond that, so 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 ('Fetch') and resource ('a job ad from its web address') plus exactly what comes back ('job title and description text'). No sibling tool does URL-based extraction, so the agent can distinguish it from match_takemeup_cv_to_job and generate_takemeup_cover_letter, which consume rather than produce a JD.

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 downstream use cases (job matching, cover letters, interview prep, job tracker) and two explicit exclusions: signed-in but not PRO, and no LinkedIn links. It does not name an alternative path for LinkedIn ads or for users without a plan, so the routing is clear but not complete.

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 and destructiveHint=false, so the safety profile is fully covered. The description usefully adds 'Free, no AI' and that weak phrases are returned flagged, but since there is no output schema it should say more about the shape of results.

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 core purpose front-loaded and the pricing/usage notes trailing. Every sentence earns its place.

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

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

A read-only, closed-world analysis tool with both parameters fully documented in the schema and no output schema to explain. The description covers what it detects and what the user gets, leaving only minor gaps in output detail.

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%: cvId documents where to get the id and locale enumerates the languages and default. The description adds nothing about either parameter, 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: finding CV bullet points that list duties rather than results, with weak phrases flagged. It is clearly distinct from analysis siblings like detect_takemeup_buzzwords or analyze_takemeup_cv_ats, though it never names them.

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 an explicit trigger condition: 'Use when the user wants stronger, quantified achievements.' There are no explicit exclusions or named alternatives (e.g., rewrite_takemeup_cv for the follow-up step), so it stops short of a 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 only declare the safe-mutation profile (readOnly=false, destructive=false, idempotent=false); the description adds the plan/PRO gate and the AI-consent requirement, which are the real blockers an agent will hit. It also discloses the return shape ({result, cacheKey} or a plan gate), which the missing output schema would otherwise leave unknown. It does not say whether the letter is persisted or how long generation takes.

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?

Four short sentences, with purpose, inputs, and usage condition front-loaded before the gating and return details. Minor redundancy between the parenthetical "(motivation letter)" and the rest, but nothing 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?

With no output schema, the description compensates by naming the return shape and the plan-gate failure mode, and it covers both prerequisites for a PRO generation tool. Missing only secondary details such as length/format of the produced letter or whether it is saved anywhere.

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 cvId, locale, and jobDescription are already fully documented, and the description only echoes their existence ("from one of the user's CVs and a job description"). The one thing it could clarify, that "conventions of the CV's country" is not the same as the locale parameter, is left ambiguous. Baseline 3 is appropriate.

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

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

States a specific verb and resource ("Write a cover letter (motivation letter)") plus the inputs that produce it (a CV and a job description) and the country-convention constraint. That combination is distinguishable from siblings like generate_takemeup_cv_summary or generate_takemeup_linkedin without opening any 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?

"Use when the user needs a cover letter for a specific job" gives a clear triggering condition, and "PRO and AI consent" flags the prerequisites an agent must check before calling. It stops short of naming alternatives or stating when not to use it (e.g. no CV on file, no consent yet), so it is clear but not exhaustive.

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 only cover the mutation profile (readOnly=false, destructive=false, non-idempotent). The description adds prerequisites the agent cannot infer: PRO plan plus AI consent, a daily limit surfaced as a 429 quota_exceeded error, an exact return shape {summary, usage}, and the persistence target (personal.summary via save_takemeup_cv). This is substantive context that directly affects how to call and handle failures.

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 dense but well-ordered paragraph: purpose first, then prerequisites, then the usage trigger, then return shape and next step. No filler sentences, though the prerequisite and limit clauses are compressed tightly enough to require careful reading.

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 no-output-schema, single-required-param generation tool, the description supplies the return fields, the gating conditions, and the follow-up write step. The main gap is not clarifying how it differs from rewrite_takemeup_cv when a summary already exists.

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 all three parameters (cvId, focus, locale) are already documented, including that cvId comes from list_my_takemeup_cvs and that locale defaults to the account language. The description only echoes the experience/skills focus, adding essentially nothing beyond the schema – 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 and resource: 'Write a professional summary (profile) for the top of one of the user's CVs,' with the experience/skills focus scoping it further. This distinguishes it from broad mutators like rewrite_takemeup_cv, though it doesn't 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?

'Use when the CV has no summary or a weak one' gives a clear trigger condition, and the description routes the agent to save_takemeup_cv as the required follow-up. What's missing is an explicit 'don't use this when …' versus rewrite_takemeup_cv, which is a natural alternative for existing summaries.

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?

The 'PRO and AI consent' note adds genuine prerequisite information not captured in annotations, which is valuable for a mutation tool. Annotations already convey the safety profile (non-destructive, non-idempotent, local). Missing: whether it publishes to LinkedIn or just generates content, and how the cache/regenerate behaves beyond the schema hint.

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 core action and its outputs, followed by the usage trigger and prerequisites. No wasted wording.

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 generation tool with no output schema, the description enumerates the produced sections and names the PRO/consent prerequisites, which is enough to call it correctly. It stops short of clarifying whether the result is just generated content versus published to LinkedIn, a minor gap given openWorldHint=false.

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 all four parameters (cvId, locale, language, regenerate) are fully documented in the schema. The description adds 'in a language LinkedIn supports' as light context but does not meaningfully extend parameter meaning beyond the schema.

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

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

States a specific verb and outcome ('Turn one of the user's CVs into a matching LinkedIn profile') and enumerates exactly what is produced (headline, About, experience, skills). This clearly distinguishes it from siblings like generate_takemeup_cover_letter, generate_takemeup_cv_summary, and translate_takemeup_cv.

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 when the user wants to create or update their LinkedIn profile from their CV' gives a clear triggering condition. No explicit exclusions or named alternatives, but the context is unambiguous for a generation 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 readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered without the description. The description adds that it reports entitlement state (PRO active, download quota consumed), which is useful context, but says nothing about auth requirements or behavior when no account/subscription exists.

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 redundancy, and the substantive payload (what is returned) is front-loaded before the usage triggers. Every clause carries information an agent 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 single-param, read-only tool with rich annotations and no output schema, the description is nearly sufficient because it enumerates the returned state (PRO flag, download usage counters). Minor gap: no mention of what the response looks like when the user has no plan or is unauthenticated.

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% for the single `locale` parameter, including its enum and default-fallback behavior, so the schema does the explanatory work. The description adds no interpretation of `locale` (e.g., that it only affects the language of returned plan text and links), so this is 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 and resource ('Get the user's TakeMeUp.cv plan') and names the two fields returned (PRO active status, monthly PRO downloads used). That distinguishes it from catalog-level siblings like get_takemeup_pricing and from download-link tools such as get_takemeup_download_link.

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 two concrete triggering conditions: a refused feature/download, or the user asking about their plan. Clear context for invocation, but it does not explicitly exclude or route to the closest alternative (get_takemeup_pricing / get_takemeup_download_access) when the question is about price tiers rather than entitlement.

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 and destructiveHint=false, so the safety profile is carried. The description usefully adds that results include scores and dates, but says nothing about behavior for a missing cvId, empty history, or whether locale affects stored names — modest value beyond structured fields.

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 with zero redundancy: the first defines the scope and return payload, the second defines the usage moment. Front-loaded and appropriately sized for a simple read 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?

Simple read tool with full annotation coverage and full schema coverage; the description covers purpose and return contents even though there is no output schema. It could be slightly more complete about what counts as "next steps" or what an empty run list means.

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 parameters are already documented, including the locale default. The description adds no hint about how cvId is obtained or how locale affects the returned scores and dates, so 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 ("List which TakeMeUp checks and AI features have already run on one of the user's CVs") plus the return payload (scores and dates). It implicitly separates itself from list_takemeup_addons by emphasizing "already run," but never names that 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?

"Use to see what has been done on a CV before suggesting next steps" gives a clear triggering context. There is no exclusion clause or named alternative (e.g. use list_takemeup_addons for the catalog of available add-ons), so it stops short of full routing guidance.

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 the read-only, idempotent, non-destructive profile, and the description adds real context beyond them by clarifying the consent grant happens out-of-band on the website. It stops short of describing what the response contains (status flag, link), which would have closed the last 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?

Three short sentences, front-loaded with what is checked, then the out-of-band consent constraint, then the invocation trigger. Nothing redundant.

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 single-param read tool with annotations covering safety and no output schema, the description supplies enough to call it correctly and interpret the trigger. It only omits what a successful result looks like (status field, consent link), 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?

Schema coverage is 100% and the single optional `locale` parameter is fully documented with its enum and default in the schema. The description adds nothing about locale or localization of results, 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: checking whether the user consented to TakeMeUp's AI processing their CV, plus the reason it matters (AI features need it). No sibling tool covers consent status, and the description makes that boundary unambiguous.

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

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

Gives an explicit trigger ('Use when an AI feature returns consent_required') and an explicit exclusion: consent is granted on the website, not through the assistant, so the agent knows this is a check-only tool and must not attempt to grant consent here.

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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral context by naming the data returned (notes, job description, status dates, follow-up), though it does not discuss authentication, rate limits, or error behavior.

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

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

The description is two concise sentences with no wasted language. It front-loads what the tool returns and follows with a clear usage condition.

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 single-resource getter with full annotation coverage and complete schema descriptions, the description is sufficient. It explains both the content returned and when to invoke the tool, and no output schema exists that would require additional return-value explanation.

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 parameters are already well documented: applicationId is linked to list_my_takemeup_applications, and locale explains language defaults. The description adds no parameter-specific meaning beyond what the schema provides, making the baseline 3 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 uses a specific verb ('Read') and resource ('one application') and clarifies the scope by listing the data returned: notes, job description, status dates, and follow-up. It clearly distinguishes reading a single specific application from listing applications.

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 explicitly states when to use the tool: 'Use when the user asks about a specific application.' However, it does not explicitly name the alternative sibling list_my_takemeup_applications or state when not to use 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?

The annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world behavior. The description adds useful context by saying it returns full CV content and that changes must go through save_takemeup_cv, though it omits any notes on authorization or response formatting beyond the section 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?

The description is two compact sentences and front-loads the read purpose before listing returned sections. Every part earns its place by clarifying scope, usage timing, and the save pathway.

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 read tool with full schema coverage and rich annotations, the description gives complete-enough context: what it reads, which CV sections are returned, when to use it, and how to persist changes. No output schema exists, but the section enumeration adequately communicates what comes back.

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

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

Schema coverage is 100%, with cvId documented as coming from list_my_takemeup_cvs and locale documented with its default. The description does not add parameter-specific meaning beyond what the schema already provides, so the baseline score 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?

The description uses the specific verb "Read" with the specific resource "one of the user's CVs" and enumerates exactly what content is returned. It is clearly distinguishable from sibling tools like list_my_takemeup_cvs or get_takemeup_cv_preview.

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 gives explicit usage context: use before editing, tailoring, or reviewing a CV, and directs saving changes to save_takemeup_cv. It does not explicitly exclude preview or listing tools, so it stops short of fully routing between all alternatives.

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, and the description adds genuinely new context beyond them: it consumes one of the daily render slots and returns only page 1. Quota cost is exactly the kind of constraint an agent needs, though the response format (image type, dimensions) isn't described.

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 behavior, then the trigger, then the resource cost. Nothing is redundant.

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, and the description does confirm an image is returned and that only page 1 is rendered, plus the quota constraint. Minor gaps remain: image format and what happens when the daily render slots are exhausted.

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 cvId already documents its source (list_my_takemeup_cvs), so the schema does the heavy lifting. The description only implies the singular CV selection and template context, adding no syntax or format detail beyond the schema.

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

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

States a precise verb, resource, and output form: show page 1 of a CV as an image in its current template. It also pins the scope with 'the same view as the editor's free preview,' which distinguishes it from data-returning siblings like get_takemeup_cv or export_takemeup_europass.

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 an explicit trigger: after creating or editing a CV, or changing its template, so the user sees the result. It doesn't name an alternative for cases where the agent wants structured data instead (e.g., get_takemeup_cv) or a downloadable file (get_takemeup_download_link), so it stops short of full routing guidance.

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 and destructiveHint=false, so the safety profile is covered. The description adds meaningful context beyond that: it performs no download and no charge, and it evaluates entitlement logic (one-time purchase vs. PRO remaining monthly downloads). It does not describe the response shape, which limits it to a 4.

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 tightly written sentences with zero filler. The core purpose is front-loaded, and the usage instruction follows naturally.

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 gives a strong conceptual picture of what is checked (purchase status, PRO quota) without explaining the exact return format (e.g., boolean plus reason or remaining count). For the agent's decision about whether to offer a download, this is largely sufficient, though a hint at the return shape would complete 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?

Schema coverage is 100% – both cvId and locale are fully documented in the schema, including the enum and default behavior. The description adds no parameter-specific detail beyond what the schema provides, 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 ('Check whether the user can download a CV') and scopes it precisely to entitlement checks (one-time purchase or PRO remaining downloads). The closing line distinguishes it from an actual download action, setting it apart from siblings like get_takemeup_download_link.

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?

Explicitly says when to call it: 'Use before offering a PDF or Word download.' This gives clear context for invocation. It does not name the alternative download tool by name or state exclusions, so it falls just short of the 5 level.

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?

Adds substantial behavior beyond annotations: link validity is 10 minutes, missing entitlement returns a reason and links instead of a file, opening the link consumes one PRO download once per link, and purchased CVs download without limit. These details are consistent with readOnlyHint=false and are important for correct invocation.

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-loads the core action and then adds only distinct operational constraints: TTL, entitlement fallback, and download accounting. It is dense but tightly structured with no wasted sentences.

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?

Covers the important edge cases an agent needs: entitlement requirements, fallback behavior, 10-minute link validity, and download counting. It does not specify the exact response object shape, but the key behavioral context is present and actionable.

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 schema already documents cvId, the pdf/docx enum, locale enum, defaults, and cvId source. The description restates the available formats but adds no meaningful parameter semantics beyond the schema.

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

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

States a specific verb and resource: get a download link for one of the user's CVs as PDF or Word. It is clearly a download-link tool, but it does not explicitly distinguish itself from nearby siblings like get_takemeup_download_access or get_takemeup_cv_preview.

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 a clear use condition with 'Use when the user wants the file' and states entitlement prerequisites: one-time purchase for that CV or PRO. It does not name alternatives or explicitly say when not to use it instead of another sibling.

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, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new context: the call is free (no credit consumption) and it enumerates exactly what the response contains, including recruiter contact status. It does not state whether the data is real-time or cached, which is the main remaining behavioral 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?

One dense sentence front-loads the three payload items, followed by the free-cost note and the usage cue — no filler. The triple-clause structure is slightly list-like but remains readable and every clause maps to an actual returned field.

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 of describing return values and does so explicitly: findability by verified recruiters, contact requests with status, and recent view counts. Only the shape of 'status' and the recency window for views are undefined, which are minor gaps for a two-parameter read tool.

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

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

Schema description coverage is 100% — cvId points the caller to list_my_takemeup_cvs and locale documents the fallback to account language. The description only implies the single-CV scope ('For one of the user's CVs') and adds nothing about locale or output language, so the schema does the heavy lifting. 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 description names a specific verb (report) plus the concrete resource: whether verified recruiters can find a given CV, which recruiters requested contact and their status, and recent view counts. It is scoped to 'one of the user's CVs', which pairs with the required cvId. It stops short of explicitly distinguishing itself from the sibling set_takemeup_get_found, leaving the read-vs-configure split to be inferred from the name.

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 when the user asks about recruiter interest' gives a clear triggering condition, and 'Free' signals there is no cost barrier to calling it. There are no explicit exclusions or named alternatives (e.g. answer_takemeup_recruiter for responding, or set_takemeup_get_found for changing visibility), so routing relies partly on the agent's own 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 declare readOnly/idempotent/non-destructive/closed-world, so the safety profile is covered. The description adds genuinely new behavior: the full set of returned content components and the translation fallback ('in the user's language when a translation exists'), which tells the agent results may be localized or fall back. It does not discuss size/truncation of long guides, which is the only notable 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 dense sentences with zero filler: the first establishes what is returned and how it is addressed, the second establishes when to call it. Front-loaded and 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?

For a two-parameter read-only tool with full schema coverage and no output schema, the description covers what is retrieved, how it is addressed, locale behavior, and its place in the workflow. No output schema means the enumerated content list is doing necessary work, and nothing an agent needs to invoke 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 the baseline is 3, but the description adds meaning beyond it: 'by its slug' frames the identifier as coming from list/search results, and 'in the user's language when a translation exists' explains the actual resolution behavior of the locale parameter rather than just its default.

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 ('Read one ... guide in full') and resource, identifies the addressing key (slug), and enumerates what the response contains (short answer, sections, steps, FAQs, sources). It is clearly distinguishable from list_takemeup_guides and search_takemeup_guides because it positions itself as the follow-up step to those.

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 after listing or searching guides, to quote or summarise one' gives a clear when-to-use condition and a purpose for retrieving the full body rather than list metadata. It stops short of naming the sibling tools explicitly or stating when not to use it, so it is strong but not an exhaustive routing rule.

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 adds genuine content value by listing what the response contains (photo/DOB/nationality/marital-status norms, date format, CV language). It omits edge behavior such as what happens for an unsupported country, which keeps it just 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?

Two sentences, front-loaded with the core purpose followed by the usage cue. The first sentence is long but every listed convention earns its place by telling the agent what to expect back. Little waste overall.

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, so the description carries the burden of describing return content, and it does so by enumerating the convention categories. With only two fully documented parameters and a simple read-only lookup, an agent has enough to call it correctly; only fallback/error behavior for unknown countries is unaddressed.

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 parameters are already documented, including the locale enum and its account-default behavior. The examples in the description ('de', 'fr', 'nl', 'se') merely repeat what the country parameter already shows and add no new format or validation semantics. 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 a country's CV/resume conventions') and then enumerates exactly which conventions are returned: photo, date of birth, nationality, marital status, date format and CV language. That level of specificity cleanly separates it from siblings like create_takemeup_cv or rewrite_takemeup_cv, which operate on actual CV content rather than locale rules.

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 a clear triggering condition: 'Use before writing or reviewing a CV for a job in that country.' No sibling alternatives are named (e.g. vs. translate_takemeup_cv or export_takemeup_europass), but the usage window is unambiguous enough that an agent knows when to reach for it.

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, and openWorldHint=false, so the safety profile is covered. The description adds valuable context beyond that by spelling out exactly which plan information is surfaced (free vs one-time download vs PRO tier details), which matters since no output schema exists.

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

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

Two tight sentences: the first front-loads what the tool returns, the second front-loads when to call it. 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?

With no output schema, the description does the work of summarizing the returned plan structure, and it covers the locale default indirectly via the schema. A minor gap is whether prices are localized or in a fixed currency, but nothing critical to calling the tool 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?

The single locale parameter has 100% schema description coverage including the enum values and the default behavior, so the schema fully carries parameter meaning. The description contributes nothing about locale or localized pricing beyond that, which is the baseline case.

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 names a specific verb and resource and enumerates the concrete contents (free tier features, one-time per-CV download price, PRO monthly/yearly with AI features and download allowance), so an agent knows exactly what it returns. It stops short of naming the nearest sibling, get_my_takemeup_billing, which could be confused with plan pricing.

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 when the user asks what something costs or what PRO includes' gives a clear triggering condition with concrete example intents. It does not state exclusions or point to get_my_takemeup_billing for the user's own charges, so the routing is helpful but not exhaustive.

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=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds genuine value with the PRO entitlement gate and the required share-link prerequisite, but it leaves the return shape ambiguous — image bytes vs. a URL — which matters for a tool with no output schema.

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 what the tool returns and where it points, followed by the prerequisite. No redundant restatement of the name or title.

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 two-parameter read-only tool, the description covers purpose, entitlement (PRO), and the ordering dependency on create_takemeup_share_link. The only gap is not clarifying whether the response is an image payload or a link, which an agent would want to know before calling.

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%, with cvId documented (source: list_my_takemeup_cvs) and locale carrying an enum plus default behavior. The description adds nothing about either parameter, so it sits at the baseline where the schema does the work.

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 the specific resource ('QR code image for one of the user's CVs') and its target ('pointing to its public QR link'), plus concrete use cases (business cards, events, printed CVs). This clearly separates it from get_takemeup_download_link or get_takemeup_cv_preview, though it doesn't name 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?

Gives a clear usage context (printed CVs, events, business cards) and an explicit prerequisite: an active QR link must exist, created via create_takemeup_share_link with purpose 'qr'. It names the enabling tool but does not state when to prefer other retrieval tools instead.

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 and non-destructive, so safety is covered; the description adds useful context the annotations don't carry — the concrete field set and status vocabulary returned, plus 'Free' (no cost/credit impact). It doesn't state ordering, result count, or pagination behavior for a list endpoint with no output schema.

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 what is returned, then the usage trigger. The inline status enumeration is dense but load-bearing; 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?

For a simple read-only, zero-required-parameter list tool this is nearly complete: it tells the agent what records and fields come back. Minor gap is the absence of result-count/ordering/pagination expectations, which an agent would want before summarizing a long list.

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

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

Schema coverage is 100% and the single locale parameter (with its enum and default-to-account-language behavior) is fully documented in the schema. The description adds nothing about locale, so 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 ('List the job applications in the user's TakeMeUp job tracker') and enumerates the returned fields (company, role, status, CV used, follow-up dates), making the plural listing role unambiguous against the singular sibling get_takemeup_application.

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 when the user asks about their applications, follow-ups or job-search progress' gives clear positive triggers. It doesn't name the alternative for single-record lookups (get_takemeup_application) or update_takemeup_application, so the boundaries are implied rather than explicit.

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, non-destructive and closed-world, so safety is covered. The description adds genuine value on top: it discloses that anonymous in-progress sessions are included and that a new account yields an empty list, which pre-empts a false 'no CVs exist' conclusion. Pagination/ordering is unstated, keeping 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?

Two sentences, no filler: contents and scope first, then the routing directive, then the empty-state caveat. Every clause carries information an agent would otherwise have to guess.

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 list tool with no output schema, the description fully compensates by enumerating returned fields and documenting the empty-list edge case. Nothing needed to invoke 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?

The single optional parameter (locale) has 100% schema description coverage including the default behavior, so the schema does the work. The description never mentions locale or how language affects titles and editor links, so it adds nothing beyond the schema — 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 ('List the user's CVs (resumes) on TakeMeUp.cv') and enumerates the fields returned (id, title, template, country format, status, editor link), so the agent knows exactly what comes back. The parenthetical about an in-progress anonymous session further scopes which CVs appear, distinguishing it from get_takemeup_cv.

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 first in almost every task, to find the CV to work on' gives clear entry-point guidance that routes the agent before it reaches for get_takemeup_cv or update tools. It stops short of naming the sibling to call after obtaining an id, so it is not fully explicit about alternatives.

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, openWorldHint=false and destructiveHint=false, so safety is covered. The description adds that each entry carries a free/PRO marker, which is useful output context, but it does not say whether the list is static catalog data or account-filtered, nor whether unavailable/locked add-ons are omitted or flagged.

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 stage-grouped parenthetical is dense but each group names concrete tools, which earns its length for a catalog tool. The purpose sentence comes first and the usage hint closes it; only minor trimming (e.g. the 'Use to show...' tail) would be possible.

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 must convey the return shape and does so partially: stage grouping plus free/PRO flags. It omits whether identifiers (add-on names/slugs) returned can be used to invoke the run_* siblings, which is the key thing an agent needs from a catalog listing. This gap keeps it at adequate-but-incomplete for a schema-less 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?

Only one optional parameter and schema coverage is 100%: the enum, allowed values and the 'defaults to the account language' behavior are already documented in the schema. The description adds nothing about locale, so 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 — listing TakeMeUp.cv's CV tools grouped by job-search stage, with free/PRO marking. The catalog/discovery nature is clear against siblings like run_takemeup_interview_prep or get_takemeup_pricing, though it never explicitly frames itself as the discovery entry point for those 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?

'Use to show what TakeMeUp can do' gives an implied use case but no explicit when-not or named alternatives (e.g. get_takemeup_pricing for plan/cost questions, get_takemeup_addon_runs for what the user already ran). An agent must infer the routing itself.

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 safety is covered. The description adds real behavioral value by disclosing the shape of the returned data (ATS rating, photo flag, suitability, length) — information an agent needs and that annotations cannot carry. Pagination/ordering behavior is not mentioned, keeping it below 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?

Two sentences, no filler: the first front-loads what is listed and what comes back, the second gives the usage trigger. Every clause carries distinct 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 the fields returned, which is the primary completeness need for a list tool. It is still silent on result volume, ordering, and whether template identifiers are included (needed to subsequently select a template), leaving a small 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?

Schema coverage is 100%, so both parameters are already fully documented; baseline is 3. The description's "optionally for one country's CV format (e.g. 'de-DE')" mildly reinforces that cvFormatLocale is a country-format filter rather than a language, but it says nothing about the locale parameter's effect on results and links.

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 (CV templates), then enumerates the attributes returned: ATS-safety rating, photo presence, who they suit, and length. That detail makes it unambiguously the read-only discovery counterpart to set_takemeup_cv_template in the sibling set.

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 when choosing or changing a CV's design" gives a clear triggering context for the tool. It stops short of naming the follow-up sibling (set_takemeup_cv_template) or stating when this tool is *not* appropriate, so it is clear context without explicit alternatives.

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=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description still adds value by disclosing the return shape ('Each result has a title, summary and link') and that the guides are free, which matters because no output schema exists.

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: what it lists, when to use it, and what comes back. Front-loaded with the resource, zero filler, and the return-shape note is short and 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?

With annotations covering safety and no output schema, the description supplies what's missing: scope, use case and result shape. It omits any mention of result volume, ordering or pagination, which an agent might want to know, but nothing needed 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 both parameters have enums plus descriptions, so the schema already carries the semantics. The description only adds 'optionally by category' and a topical mapping that largely restates the category enum values, 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 ('List TakeMeUp.cv's free CV and job-search guides') and enumerates the topical coverage (CV writing, ATS, interviews, job search, country rules), which an agent can map directly onto the category enum. It does not name or distinguish itself from the close sibling search_takemeup_guides or get_takemeup_guide, so it stops short of a 5.

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 a clear triggering condition: 'Use when the user wants advice on CVs/resumes, applications or working in another European country.' That covers the common intent well, but there is no exclusion guidance and no pointer to the obvious alternative search_takemeup_guides when the user has a specific query rather than a browse intent.

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?

Adds real context beyond annotations: 'PRO and AI consent' prerequisites and 'Returns {result:{score,…}} or a plan gate', which tells the agent to expect a gated failure mode. The annotations already flag non-readOnly and non-idempotent behavior, and the description does not contradict them; it simply does not explain why repeated calls differ.

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: purpose first, then the invocation trigger, then prerequisites and return shape. Nothing is redundant with the title and every sentence carries information an agent 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?

Covers the core needs for a gated scoring tool: what it computes, when to call it, the PRO/AI-consent prerequisite, and the two possible response shapes. There is no output schema, and the shape sketch plus the honest mention of a plan gate is adequate, though a bit more on what the score fields mean would make it complete.

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

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

Schema description coverage is 100%, so cvId, locale, and jobDescription are already well documented, including the pointer to list_my_takemeup_cvs. The description adds no parameter-level detail beyond implying a job-ad text and a chosen CV, 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: 'Score how well one of the user's CVs matches a job description' and names the outputs (matching/missing skills and keywords). It does not explicitly distinguish itself from the similarly-scoped sibling analyze_takemeup_cv_ats, so it stops short of a 5.

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 a concrete trigger: 'Use when the user shares a job ad and asks whether they're a good fit or what to add.' That is clear context for invocation, but it names no alternative tool and states no exclusion (e.g., versus ATS analysis), so it is not a 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?

Annotations already declare destructiveHint=true and idempotentHint=false, and the description adds real context: the public page becomes not-found, and the operation requires a signed-in, non-PRO account. It does not say whether a revoked CV can be re-published or whether existing links are permanently invalidated, which would complete the destructive-action picture.

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

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

Two tight sentences; the effect (page returns not found) is front-loaded alongside the verb, and the id-sourcing note follows. No wasted prose.

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 two-parameter destructive tool with full annotation and schema coverage and no output schema, this covers effect, prerequisites, and id origin. The only omission is the reversibility question (whether the user can restore the CV afterwards), which matters for a destructive action.

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 both parameters are already documented, including the stealthId's dual origin. The description repeats that sourcing hint rather than adding new semantics, and never mentions the locale parameter. Baseline 3 is appropriate.

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

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

Specific verb plus resource ('Take one of the user's published stealth (anonymised) CVs offline') with the concrete observable consequence that the public page returns not found. It is clearly distinct from the publishing sibling create_takemeup_stealth_cv, which is named as the source of the id.

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

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

States the operating condition clearly ('Signed-in; not PRO') and points at create_takemeup_stealth_cv as where the id comes from. It does not discuss when not to revoke (e.g. vs. leaving a CV up or unpublishing in the UI), so it falls short of the 5 bar for explicit alternatives.

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?

Beyond the annotations, the description discloses two things an agent cannot infer: the tool returns non-persisted suggestions that must be saved separately, and it requires a PRO plan plus AI consent. That consent/plan prerequisite is exactly the kind of context annotations never carry. It does not mention credit consumption or latency for an AI operation, which keeps 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?

Three tight sentences, front-loaded with the action and scope, followed by the return contract and the prerequisite. No redundant restatement of the name or title.

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 does the essential work of explaining that output is suggestions rather than a saved CV, and it flags the consent and plan gates. A brief note on what the suggestions look like (or whether credits are spent) would close the remaining 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?

Schema description coverage is 100%, so all four parameters are already documented, including the 'roleTailor only applies with a targetRole' constraint on mode. The description's mention of optional tailoring to a target role restates what the schema already explains rather than adding syntax or format detail, 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 ('Rewrite the summary and bullet points of one of the user's CVs') plus the qualitative goal ('stronger and more results-focused'). It also distinguishes itself from save_takemeup_cv by declaring that it only returns suggestions, so an agent can separate it from write and summary-generation siblings.

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 when the user asks to improve or rewrite their CV or resume' gives a clear trigger condition, and the follow-up handoff to save_takemeup_cv is explicit. It does not contrast itself with the closely related generate_takemeup_cv_summary or analyze_takemeup_cv_ats siblings, so the routing guidance is good but not exhaustive.

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 declare a non-read-only but non-destructive operation, and the description adds real value on top: explicit daily quotas (free 1, PRO 3), the 429 quota_exceeded failure mode, and the return shape {session, usage}. It does not say whether a failed/roasted run consumes quota or what the session object enables, which keeps it below 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 purpose, then limits, then routing cue and return shape. No filler or restatement of the title.

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 responsibly discloses the return envelope and the quota error. The remaining gap is that '{session, usage}' is unexplained — an agent cannot tell from this whether the roast text is in the response or must be fetched separately.

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 cvId even points to list_my_takemeup_cvs, so the schema carries the full burden. The description mentions neither cvId nor the locale fallback behavior, adding nothing beyond structured fields — 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?

Names a specific verb and resource ('roast of one of the user's CVs') and characterizes the output ('blunt feedback on what a recruiter would laugh at'). This clearly separates it from analytical siblings like analyze_takemeup_cv_ats or detect_takemeup_buzzwords, which read as objective tools rather than entertainment-with-substance.

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 when the user asks for brutally honest feedback or a roast' gives an explicit triggering condition. It does not name a competing alternative tool or an exclusion (e.g., 'for ATS scoring use analyze_takemeup_cv_ats'), so it stops short of full routing guidance.

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?

The description adds meaningful behavioral context beyond the annotations by stating the PRO/AI-consent prerequisite and that the call may return either a result or a plan gate. The annotations already disclose read-only=false and idempotent=false, though the description does not explain the implications of those flags, such as quota consumption or repeated scoring behavior.

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

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

The description is compact and front-loaded: it states what is scored, when to use it, prerequisites, and return shape without filler. Every clause carries useful information for selecting or invoking the 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 two-parameter scoring tool with no output schema, the description gives enough context to call it correctly, including plan/consent gating and a return-shape hint. It could be slightly more complete by explaining what the score means or why the tool is marked non-read-only and non-idempotent, but the essential invocation context is present.

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 100% description coverage for both parameters, so the baseline is 3. The description mentions that the tool operates on one of the user's CVs, but it does not add format or selection guidance beyond the schema's cvId and locale descriptions.

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

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

The description states a specific verb and resource: score how authentic and human one of the user's CVs reads, and contrasts it with generic or AI-sounding text. It also names the concrete outcome, passages to rewrite, so the tool is clearly distinct from CV editing or generic CV analysis siblings.

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 gives a clear triggering condition: use when the user worries their CV sounds AI-written or generic. It does not explicitly name when not to use it or point to a specific alternative such as rewrite_takemeup_cv, but the intended context is easy to infer.

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?

The description discloses two gating facts the annotations do not: the tool is PRO-gated and requires AI consent. That is valuable precondition context for a non-readOnly call. It stops short of saying whether a run is consumed against an addon quota or whether results are persisted, so it is not fully transparent.

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

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

Two sentences, front-loaded with the outcome and ending on the trigger. The fragment 'PRO and AI consent.' is telegraphic but dense with useful information, so it earns its place despite the clipped phrasing.

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 sketches the return shape (next roles plus skills per role) and flags the PRO/consent prerequisite. For a 2-parameter, closed-world generation tool that is nearly sufficient; only quota/persistence behavior 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?

Schema coverage is 100%, so both cvId and locale are already documented in the schema, including where to get the CV id. The description only restates the CV input ('based on one of their CVs') and says nothing about the locale parameter or output language, so it adds little beyond the schema. 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 output: 'suggest realistic next roles' plus 'skills to build for each', scoped to one CV. It is distinguishable in substance from match_takemeup_cv_to_job and explain_takemeup_career_gaps, but no sibling is named, so the agent must infer the boundary itself.

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 when the user asks what job to aim for next or how to change career' gives a concrete trigger condition. It lacks any when-not clause or explicit routing away from the near-neighbour match/explain tools, so the routing is clear but not exhaustive.

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?

The description adds useful context beyond annotations by noting that the tool is PRO-gated and requires AI consent. It does not contradict the annotations, and it discloses key prerequisites, though it does not explain side effects or persistence behavior.

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

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

The description is two tight sentences, front-loads the core purpose, and includes the key prerequisite without unnecessary detail.

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?

It explains what the tool produces, what inputs drive it, and the main gating condition. Given the rich schema coverage and absence of an output schema, this is largely complete, though it could say more about output format or what happens when consent 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 description coverage is 100%, so the schema already documents cvId, jobDescription, and locale. The description reinforces that the job description is the source text and the CV is the basis for answers, but it does not add meaning beyond the schema for locale or parameter formatting.

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 action and resource: preparing interview questions and answers from a job description and CV. It is clearly distinguishable from most siblings by the interview-prep focus, though it does not explicitly name alternatives or contrast itself with related 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?

It gives a clear usage trigger: 'Use when the user has an interview coming up.' It does not include when-not-to-use guidance or explicitly route to sibling tools, so it falls short of the top score.

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 flag openWorld and non-read-only; the description adds genuinely new context the annotations don't carry — it is a PRO feature with a daily limit and pulls from external labour-market data (explaining both openWorldHint and why readOnlyHint is false). It doesn't state what happens when the daily limit is hit or whether a run is consumed on failure.

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 dense sentences, front-loaded with capability then gating then trigger condition. No filler; 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 helpfully states what comes back (salary ranges, demand, in-demand skills) and warns of PRO quota. Still silent on result shape/size and on limit-exceeded behaviour for a gated external-data call.

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 both params (cvId, locale) are documented in-schema, so baseline 3 applies. The description only implies that the record is keyed to 'one of the user's CVs' and says nothing the schema doesn't already cover.

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+resource (live EU job-market read of a CV profile) and enumerates the outputs: salary ranges, demand, in-demand skills. The scope ('external labour-market data', EU) separates it from profile-analysis siblings like analyze_takemeup_cv_ats or match_takemeup_cv_to_job.

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 an explicit trigger: 'Use when the user asks what they could earn or how in-demand their profile is.' No when-not or named alternatives (e.g. vs match_takemeup_cv_to_job for a specific posting), so it stops short of full routing guidance.

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?

Beyond the annotations, it discloses that the original CV is preserved and a NEW CV is created (consistent with destructiveHint=false), the 1–3 minute runtime, the AI-consent prerequisite, and the daily quota (free 2, PRO 5). It also describes the return payload (new CV id and before/after scores). This is exactly the operational 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?

The purpose and side effect ('saved as a NEW CV (the original stays)') are front-loaded, followed by runtime, consent, quota, usage trigger, and return value in a tight sequence. Every clause carries operational information; nothing is repeated from the 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?

With no output schema, the description covers the return value (new CV id and before/after scores), the async-feeling runtime, the consent gate, quota limits, and the non-destructive side effect. An agent has everything needed to decide to call it and to set expectations with the user.

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%, with cvId pointing to list_my_takemeup_cvs and locale documented in-schema, so the schema already carries full parameter meaning. The description adds only the general framing ('one of the user's CVs') and no extra syntax, defaults, or edge cases beyond the schema, which is the expected baseline.

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 ('Run TakeMeUp's one-click makeover on one of the user's CVs') and enumerates exactly what the operation does (rewrite, ATS check, aging, buzzwords, authenticity, template pick). It clearly distinguishes itself from narrower siblings like rewrite_takemeup_cv and analyze_takemeup_cv_ats by being the all-in-one operation.

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 states the triggering condition explicitly ('Use when the user wants the whole CV improved in one go'), which implicitly contrasts with the single-purpose sibling tools. It stops short of naming the alternatives (e.g. 'for only ATS fixes use analyze_takemeup_cv_ats'), so routing is clear but not exhaustive.

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?

The description adds meaningful context beyond annotations by disclosing that the entire CV is replaced and that a draft with blank required fields is still saved. Combined with the destructiveHint annotation, this gives the agent a good sense of the overwrite behavior, though it doesn't mention permissions or what the response contains.

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 compact and front-loaded with the replacement behavior and data shape. Every sentence mostly earns its place, though the final partial-draft sentence is slightly confusing because the parameter it references is absent from the 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?

For a destructive full-replacement tool with no output schema, the description covers the input shape and overwrite semantics. But it omits any return-value guidance and incorrectly references a 'partial' parameter not present in the schema, leaving gaps an agent may need to resolve at call time.

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 baseline is 3. The description adds some useful meaning (whole CV in get_takemeup_cv shape, only requested changes applied), but it also references 'partial=true' even though no such parameter exists in the input schema, which is a notable mismatch.

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 (save complete CV content) and the replacement semantics. It references get_takemeup_cv for the expected shape, but does not explicitly distinguish this tool from sibling mutation tools like create_takemeup_cv or rewrite_takemeup_cv, which could cause hesitation.

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 gives clear contexts for use: filling in a new CV, applying edits, rewrites, and fixes, and saving drafts with blank required fields. However, it does not name when to use this tool instead of a sibling such as rewrite_takemeup_cv or create_takemeup_cv, so the agent is left to infer the boundary.

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 this as read-only, idempotent, non-destructive, and not open-world. The description adds useful behavioral context by saying it returns best matches with a summary and link, which helps the agent understand the result shape beyond the safety annotations.

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 waste, and the core purpose is front-loaded before usage examples. It is appropriately sized for a straightforward search tool.

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

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

Given full parameter documentation and rich annotations, the description is nearly complete for agent selection. The main minor gap is that it does not specify result cardinality, ranking behavior, or what happens with no matches.

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 query, locale, and category parameters are fully documented in the schema. The description reinforces topical search and uses examples, but does not add meaning beyond the schema for locale or category.

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 (“Search TakeMeUp.cv's CV, resume and job-search guides by topic”) and explains what is returned (“best matches with a summary and link”). It implies differentiation from list/get siblings by emphasizing topic-based search, but does not explicitly name or contrast those alternatives.

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 usage context: answer CV, resume, cover-letter, interview, or job-application questions with a readable guide. The examples make the intended use concrete, but there are no explicit exclusions or routing guidance for when to use list_takemeup_guides or get_takemeup_guide instead.

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 destructiveHint=true and idempotentHint=false, so the description's job is to say what is lost or overwritten. It says nothing about whether existing customizations or layout edits are discarded, whether the change is reversible, or whether a paid plan is required — for a mutation flagged destructive, that is a real 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 compact sentences, with the action and the prerequisite lookup front-loaded and the use cases trailing. Nothing is redundant with the title.

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?

Parameters and the template-discovery path are complete, and there is no output schema to explain. But for a destructive, non-idempotent mutation it omits confirmation expectations and what happens to the CV's existing content, leaving an agent to guess at side effects.

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 cvId, templateId, and locale are already documented in the schema. The description only indirectly adds meaning by pointing at list_takemeup_cv_templates for templateId, which matches the baseline 3 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 and resource ('Switch one of the user's CVs to another template (design)') and ties the operation to the sibling that supplies valid options. An agent can distinguish this from create_takemeup_cv or rewrite_takemeup_cv without opening either 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 trigger conditions ('different look, an ATS-safer layout, or a design with or without a photo') and routes to list_takemeup_cv_templates for valid template ids. No explicit exclusions or when-not guidance, which keeps it short of a 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 flag destructiveHint=true and openWorldHint=true; the description adds genuinely new behavior — recruiters see the CV but not contact details until the user approves each one, and the feature is free. That privacy/consent model is not derivable from the annotations. It does not say what happens to in-flight recruiter approvals when enabled is set to false, which is the remaining 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?

Four short sentences, front-loaded with the action and its reverse, followed by the privacy consequence, the cost, and the consent precondition. No restatement of the name or title and 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?

For a toggling mutation with full annotation coverage and no output schema, the description covers action, visibility effect, pricing, and consent, which is enough to call it correctly. Only the absence of any mention of checking current state or handling pending recruiter approvals keeps it from the top.

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%: cvId already points at list_my_takemeup_cvs, enabled already spells out true/false meaning, and locale explains the default. The description adds only the possessive framing ('one of the user's CVs'), so the schema is doing the heavy lifting and 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 with both directions of the toggle: 'Make one of the user's CVs findable by verified recruiters ... or hide it again.' This cleanly separates it from the read-side sibling get_takemeup_get_found and from create_takemeup_share_link, which implies a different sharing mechanism.

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 context for when to invoke: on the user's request, at no cost, and 'Only with the user's explicit agreement' as a hard precondition. It stops short of naming a sibling (e.g. check get_takemeup_get_found for current state, or answer_takemeup_recruiter for pending recruiter requests), so there are no explicit 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?

Annotations already declare it is a non-read-only, open-world, non-idempotent, non-destructive operation. The description adds real value beyond that: PRO gating with a 403 pro_required outcome and the exact response shape {ok, subscribed, cvId}. It does not say what happens on repeat subscriptions or how to undo them, which matters given idempotentHint=false.

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 action and the PRO constraint, then the trigger condition, then the return contract. No filler and nothing buried.

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 an optional-only parameter set and no output schema, the description supplies what the schema cannot: the PRO entitlement, the 403 failure mode, and the response keys. The remaining gap is lifecycle behavior — duplicate subscriptions and unsubscribing — which an agent would want before promising a side effect.

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 both parameters are already documented, so the 3 baseline applies. The mention of matching 'one of their CVs' loosely corroborates the cvId default but adds no syntax or selection guidance beyond the schema.

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

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

States a specific verb (subscribe) and resource (weekly email of matched job openings), plus the matching basis against the user's CV. An agent can tell this is a notification-subscription operation, distinct from CV/application/job-match siblings.

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 when the user wants job alerts by email" gives an explicit triggering condition. It stops short of stating when not to use it or naming an alternative (e.g. match_takemeup_cv_to_job for one-off matching), so it falls short of a full routing rule.

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?

The description adds meaningful behavior beyond annotations: it creates a NEW separate CV, leaves the original untouched, requires PRO and AI consent, adapts format to locale, and returns translatedCvId and an editor link. These details are valuable, though it does not cover caching/regenerate behavior or failure modes.

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 four compact sentences, front-loaded with the core action and followed by the important constraint that the original stays. Every sentence adds useful information without 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?

For a mutation tool with no output schema, the description explains the non-destructive nature, PRO/AI consent requirement, locale adaptation, and returned values. It is largely complete, though it could clarify how AI consent is obtained and how regenerate/caching interacts with repeated calls.

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 cvId, locale, regenerate, and targetLocale thoroughly. The description only implicitly maps to the inputs by saying 'one of the user's CVs' and 'another language,' adding little parameter-specific detail beyond the schema.

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

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

The description states a specific verb and resource: translate a user's CV into another language as a new, separate CV. It also clarifies the key differentiator that the original CV remains intact and that the output is adapted to the target language's CV format, which distinguishes it from rewrite or create 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?

It gives a clear usage trigger: when the user applies in another country or language. However, it does not explicitly name alternatives such as rewrite_takemeup_cv or explain when not to use this tool, leaving some selection ambiguity.

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 flag destructiveHint=true and idempotentHint=false, so the safety profile is covered; the description adds genuinely new context: partial-update semantics ('Only the fields given change') and a non-obvious side effect ('Moving to "applied" saves a snapshot of the CV as sent'). It does not explain irreversibility or how the notes replacement interacts with destructiveHint, which keeps it below 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?

Front-loaded with the core action, then the field inventory, then the two behavioral notes, then the trigger sentence — a sensible ordering with no filler. The middle field enumeration is somewhat list-like and partly redundant with the schema, costing a point.

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 12-parameter mutation with no output schema and solid annotations, the description covers the field set, partial-update behavior, a key side effect, and usage triggers. What remains missing is what the response returns and whether status changes are reversible, but the coverage is nearly sufficient 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?

Schema description coverage is 100%, so all 12 parameters are already documented, establishing a baseline of 3. The description mostly restates field names and enum values already present in the schema; the only semantic additions (status value list, yyyy-mm-dd format) duplicate structured data rather than extend it.

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 (Change) and resource (an application in the user's job tracker), then enumerates the mutable fields with their accepted values. An agent can distinguish it from add_takemeup_application and get_takemeup_application without reading either 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 closing sentence gives clear triggering situations (user applies, hears back, wants a follow-up reminder), which routes the agent well against sibling creation/get tools. It stops short of explicit exclusions or naming alternatives like add_takemeup_application, so it is strong context rather than full when/when-not guidance.

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