Himalayas Remote Jobs MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states that the operation requires employer authentication, which is useful, but it does not disclose what happens on success or failure, whether the operation is idempotent, what the response looks like, or any potential side effects. For a mutation tool, this is minimal disclosure.

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

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

The description is a single sentence that is concise and front-loaded with the action. There is no redundant wording; it states the purpose and a key requirement in a compact, efficient manner.

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 mutation tool with three parameters and no output schema, the description is reasonably complete. It covers the action, the resource, the platform, and an authentication requirement. However, it does not mention what the successful result is or whether there are additional constraints (e.g., whether the company must exist), leaving some gaps for an agent to infer.

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

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

The schema description coverage is 100%, so the baseline is 3. The description does not add any additional meaning beyond what the schema already provides for the title, category, and description fields. Since the schema documents each parameter clearly, the description does not need to compensate, but it also contributes nothing new.

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 clear action ('Add'), a specific resource ('perk/benefit'), and scope ('to your company on Himalayas'). It clearly distinguishes itself from sibling tools like remove_company_perk and get_company_perks by indicating it is the creation operation. The platform context also orients the agent.

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—if you want to add a perk, use this tool—and mentions a prerequisite (employer authentication). However, it does not explicitly state when to use this versus the related get or remove tools, nor does it note any exclusions or alternative scenarios. The guidance is implicit 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?

No annotations are provided, so the description carries full responsibility. It only discloses that authentication is required; it doesn't mention side effects (e.g., profile modification), reversibility, or constraints on duplicate entries. For a mutation, this is minimal coverage.

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 concise with no wasted words, front-loading the action and specifying context. It is appropriately brief, though it could add a phrase about the profile update without becoming verbose.

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

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

The tool has 9 parameters and no output schema. The description covers the essential action and authentication, but lacks any mention of prerequisites beyond auth, and doesn't specify that it modifies the current user's profile (though 'your' implies this). It is adequate but not thorough for a mutation tool with no annotations.

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 fully documents each parameter. The description adds no additional parameter semantics beyond the schema, meeting the baseline for this dimension.

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 verb 'Add' and the resource 'education entry' in the context of 'your Himalayas profile'. It implicitly differentiates from sibling tools like add_experience by resource type, though it doesn't name any directly.

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

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

No guidance is provided on when to use this tool vs alternatives such as add_experience or update_profile. It only mentions authentication as a prerequisite, not the intended scenario or exclusions.

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

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

No annotations are provided, so the description must carry all behavioral disclosure. 'Requires authentication' is a useful safety signal, but the description omits any information about side effects (e.g., whether it overwrites existing experiences), response behavior, or potential errors. For a mutation tool, this is insufficient.

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 extremely concise – one sentence with no redundancy. However, it is under-specified for an agent to use effectively. While it earns credit for brevity, it loses points for lacking essential context that could have been included without bloat.

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 tool with 9 parameters, 4 required, no output schema, and no annotations, the description is too minimal. It does not mention the required fields, the format of dates, the existence of the experience_skill_list parameter, or any success/error behavior. An agent would need to deeply inspect the schema to understand all constraints, but even then, behavioral expectations are unclear.

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% – every parameter has a description in the schema. The tool description adds no additional parameter meaning beyond the schema. Per the guidelines, baseline 3 is appropriate when the schema fully documents parameters, and the description neither helps nor hurts.

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: 'Add a work experience to your Himalayas profile.' It identifies the specific resource (work experience) and destination (profile). It is distinct from sibling add_education, though it doesn't explicitly contrast them. The verb 'Add' is specific and 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?

The description provides no guidance on when to use this tool versus alternatives like add_education or update_profile. It only mentions authentication, which is a requirement, not a usage condition. The intended use case is implied by the name, but not explicitly 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?

No annotations are present, so the description must carry the full behavioral disclosure. It only mentions 'No authentication required' and the operation itself (check), which implies read-only but isn't explicit. It doesn't state side effects, return format, or whether it's idempotent. This is a significant gap for a tool with no 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?

Three short sentences, front-loaded with the purpose, followed by input provenance and auth requirement. No fluff.

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-parameter check with no output schema, this is fairly complete. It tells the agent what to do, what to pass, and the auth context. It could mention what the response contains, but that's implied by the operation name.

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 already describes session_id as 'Stripe checkout session ID' (100% coverage). The description adds value by specifying the source of the session_id (returned from post_job_public or create_company_job with extras), which helps the agent obtain the correct value. That goes 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?

Clearly states the verb and resource: 'Check the payment status of a job posting.' It also tells the agent where to get the session_id (from post_job_public or create_company_job with extras), which further clarifies the operation. No sibling tool does this, so it's distinct.

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 explicit context on when to use: you need a session_id from post_job_public or create_company_job with extras, and no authentication is required. While it doesn't name alternatives, there are no payment-status sibling tools, so the guidance is sufficient.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that jobs are free, require admin approval before going live, and require employer authentication—key behavioral traits. However, it omits other behaviors like the option to save as a draft (though visible in schema) and any error/validation details. It provides essential context but not exhaustive transparency, hence a 3.

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

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

The description is a single, focused sentence that front-loads the primary action and immediately relays crucial behavioral notes (free, admin approval, auth). There is zero redundancy or filler, making it concise and effective. It delivers necessary information without wasting words.

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

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

The description covers the core context needed for an agent to call the tool correctly: the purpose, the cost implication, the approval process, and authentication. Since the schema provides comprehensive parameter details, the description complements rather than duplicates that info. It could mention return behavior or draft handling, but these are covered in schema or are not essential. Overall, it is adequately complete for a creation tool with rich schema metadata.

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

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

The input schema has 100% parameter description coverage, so all 14 parameters are already documented in detail. The tool description adds no additional meaning beyond what the schema provides. Given the high coverage, a baseline of 3 is appropriate, as the description does not enhance parameter understanding.

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 ('Post a new job on Himalayas') and provides context about free posting and admin approval. However, it does not explicitly differentiate itself from sibling tools like post_job_public or update_company_job, which would elevate it to a 5. Overall, the purpose is unambiguous and specific enough for an agent to select it.

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

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

The description gives context about when to use the tool (to post a new job) and includes prerequisites like employer authentication. However, it does not explicitly state when not to use it or mention alternatives (e.g., post_job_public). This implies usage rather than providing explicit routing guidance, so a 3 is appropriate.

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

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

No annotations are provided, so the description carries the full burden. It does disclose two behavioral traits: irreversibility and authentication requirement. However, it does not mention side effects, whether it is a hard or soft delete, or what happens to related data. For a destruction tool, this is useful but not comprehensive.

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 sentences with no fluff. It front-loads the action and immediately states the irreversibility, then the auth requirement. Every sentence provides essential 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 single-parameter delete tool with no output schema and no annotations, the description covers the critical aspects: what is deleted, irreversibility, authentication, and ownership scope. It lacks details about post-deletion effects or confirmation responses, but these are less critical for correct invocation. The mention of 'your company' clarifies ownership constraints.

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 description covers the only parameter 'job_slug' with a clear description ('Job slug to delete'). The tool description adds no extra meaning beyond the schema, so a baseline of 3 is appropriate.

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

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

The description clearly states the action ('Delete'), the resource ('a job posting from your company on Himalayas'), and adds an important qualifier ('This action cannot be undone'). It is unambiguous and distinguishes well from siblings like update_company_job or list_company_jobs.

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

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

The description gives clear context about the action (delete job, irreversible) and a prerequisite (employer authentication), but it does not explicitly mention alternatives or when not to use this tool. For a delete operation, this is adequate but not fully explicit about exclusions or alternative tools like soft-deactivate via update_job_status.

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

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

With no annotations provided, the description carries the full behavioral burden. It explicitly states 'Delete' (a destructive action) and mentions authentication, which is helpful. However, it does not disclose that deletion is likely permanent, does not mention side effects on messages or related data, and does not state whether the operation can be undone. Basic transparency is present, but more is expected for a destructive tool.

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

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

The description is a single, concise sentence that leads with the action and includes key parameters and authentication. There is no unnecessary filler, and it is appropriately front-loaded. It could slightly expand on usage guidance without becoming verbose, so it is not a perfect 5.

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 delete operation, the description covers the core action, accepted parameters, and auth requirement. However, it lacks important context such as the irreversibility of deletion, the need to provide exactly one of the two identifiers, and any prerequisite steps (e.g., that room_name comes from list_conversations, although the schema implies this). The absence of annotation makes this more critical, so a 3 reflects the gap.

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

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

The schema description coverage is 100%, and the schema already explains both parameters: room_name is 'Room name from list_conversations' and talent_slug is 'Talent slug (from search_talent results) — resolves to room name automatically.' The tool description merely repeats 'Accepts room_name or talent_slug' without adding new semantic meaning. Baseline is 3 due to high schema coverage, and the description does not elevate 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?

The description states the action clearly: 'Delete a conversation.' It names the resource and the verb, and it mentions the two accepted identifiers. While it does not explicitly differentiate from siblings, the tool name itself and the action make it distinct from nearby tools like get_conversation or start_conversation, so it is sufficiently clear.

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

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

The description provides one usage constraint ('Requires employer authentication') but gives no guidance on when to use this tool versus alternatives, such as when to delete versus archive, or how to obtain the required identifiers. It does not mention that a conversation must first be listed (e.g., via list_conversations) or that only one of the two parameters should be provided. This leaves the agent to infer usage conditions.

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

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

With no annotations, the description must carry the full burden of disclosing behavior. It mentions the core functionality but omits key behaviors like pagination (page parameter), the return structure, and the worldwide-overrides-country interaction (though that is in the schema, the description adds nothing beyond it). For a list tool, this is insufficient behavioral disclosure.

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

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

The description is a single, concise sentence with no fluff. It front-loads the main purpose ('Browse remote-friendly companies') and mentions filters efficiently. There is 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 tool with no output schema and a sibling 'search_companies', the description should hint at what the response looks like and when to use this over other search/list tools. It provides none of that, leaving the agent to guess about pagination, return format, and differentiation. The description is too sparse to be fully 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%, so the baseline is 3. The description only restates the existence of filters ('country or worldwide availability') without adding any new semantics or usage nuances beyond what the schema already documents. It does not compensate for any gaps, but none exist in 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 clearly states the tool browses remote-friendly companies with optional filters, which is a specific verb and resource. It does not explicitly differentiate from the sibling 'search_companies', so it lacks that distinguishing clarity that would earn 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?

There is no guidance on when to use this tool versus 'search_companies' or other listing tools. The description only states what it does, not when it is the preferred choice, and no exclusions or alternatives are mentioned.

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

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It only states that the tool retrieves details, implying a read-only operation, but does not disclose additional behaviors such as authentication requirements, response format, pagination, or potential latency. This is a significant gap for a tool that might return a large payload.

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

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

The description is a single, efficient sentence that front-loads the core action ('Get full details') and then lists the key content areas. There is no redundant text or unnecessary embellishment, making it highly concise and well-structured.

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

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

The description adequately conveys the scope of the data returned, but it omits any indication of the output schema or error conditions. Given that it's a simple tool with one parameter, this might be sufficient, yet the lack of detail on response structure (e.g., whether open positions are a list of IDs or full objects) leaves some ambiguity. It is complete enough for basic use but not fully comprehensive.

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 only parameter, company_slug, is already described in the schema with a concrete example. The tool description adds no further semantic detail beyond what the schema provides. Since schema description coverage is 100%, the baseline score of 3 applies; the description does not enhance parameter understanding.

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

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

The description clearly states the tool retrieves full company details and enumerates specific content areas (about, tech stack, benefits, open positions, social links). However, it does not explicitly differentiate itself from sibling tools like get_company_profile or get_company_perks, which could lead to ambiguity for an agent deciding between 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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of when to prefer get_company_details over get_company_profile or other related tools, nor any exclusions for cases where a lighter-weight call would suffice. The agent is left to infer usage from the 'full details' phrasing.

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

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

With no annotations, the description must disclose behavioral traits. It mentions the authentication requirement, but it does not state that the operation is read-only (though implied by 'Get'), nor does it describe the response structure, potential errors, or any side effects. This leaves the agent with limited information about what to expect.

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

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

The description is a single, concise sentence that front-loads the core action and resource. It contains no unnecessary words and is appropriately sized for a simple no-parameter getter.

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 absence of an output schema, the description should describe the return value. It only says 'Get your company's perks/benefits', which implies a list but does not explain the format, fields, or any pagination. For a simple tool, some detail about the response would be expected. The authentication note is useful, but the lack of return information makes it incomplete.

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

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

The tool has zero parameters, so the baseline is 4. There is nothing to explain about parameters, and the schema correctly shows an empty properties object. The description does not need to elaborate on parameter semantics.

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

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

The description clearly states the verb 'Get' and the resource 'company's perks/benefits' on Himalayas, distinguishing it from sibling tools like add_company_perk and remove_company_perk. It is specific and unambiguous about the tool's purpose.

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 the tool is for retrieving perks, but it does not explicitly mention when to use it versus alternatives like add_company_perk or remove_company_perk. It provides the context that employer authentication is required, which hints at eligibility, but lacks explicit guidance on selection criteria.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose the authentication requirement, which is a key behavioral trait, but it omits other important details such as what the tool returns, how it handles unauthenticated calls, or any potential side effects. For a tool that could be confused with get_company_details, this is insufficient.

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 concise—two short sentences with no filler. It front-loads the primary purpose ('Get your company's profile') and follows with the authentication requirement. This is well-structured and efficient, though it is arguably too minimal for the complexity of the tool ecosystem.

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

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

Given the tool's simplicity (0 params, no output schema), the description covers the basics but leaves ambiguity about its scope relative to sibling tools. It does not explicitly clarify that this is specifically for the authenticated employer's own company, nor does it explain the return format. The description is not misleading but is incomplete for confident tool selection.

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

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

The tool has zero parameters, so the schema trivially covers everything. The description adds no parameter information because none is needed. Baseline for 0 parameters is 4, and the description meets that standard without adding unnecessary details.

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 it retrieves 'your company's profile on Himalayas,' indicating a specific resource (company profile) and context (the caller's own company). It distinguishes implicitly from get_companies (a listing) and get_company_details (likely any company) via the 'your company' phrasing, but it does not name these 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?

It mentions the precondition of employer authentication but provides no guidance on when to choose this tool over get_company_details or similar tools. No alternatives are named, and no conditions for when to use it versus others are given. The agent is left to infer the appropriate use case from the tool name alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does state the authentication requirement, which is useful, but it does not confirm read-only status, error behavior, rate limits, or what happens if both parameters are supplied or none are. These gaps leave the agent uncertain about side effects and edge cases, though the 'get' verb implies a safe read 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 exactly two sentences: the first states the core purpose, and the second adds the parameter alternatives and authentication requirement. There is no filler, repetition, or tangential detail. The most important information is front-loaded, making it easy for an agent to quickly parse.

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 operation with two optional parameters and no output schema, the description covers the essentials: what it does, the identifying inputs, and the auth requirement. It could be more complete by clarifying whether at least one parameter is required (schema says required: 0) and what the return structure is, but these are minor given the straightforward nature of the tool and that it returns 'full message history'.

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% for both parameters, so the schema already documents their purpose and origin (e.g., 'Room name from list_conversations'). The description adds that the tool 'Accepts room_name or talent_slug', reinforcing that they are interchangeable alternatives, a nuance not explicitly in the schema. This is a modest addition, so a baseline of 3 is appropriate rather than a higher score for significant extra meaning.

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

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

The description clearly states the operation ('Get full message history') with a specific resource ('a conversation') and scope, which immediately distinguishes it from siblings like list_conversations (listing conversations) and send_message (sending messages). The acceptance of room_name or talent_slug further clarifies the tool's targeted use.

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 indicates when to use it (retrieving full history of a single conversation) and imposes an authentication constraint ('Requires employer authentication'). However, it does not explicitly reference alternatives or when not to use it (e.g., when listing all conversations instead). The context is clear enough for most agents, but it lacks direct exclusion or sibling routing.

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

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

No annotations are provided, so the description has full responsibility. It mentions the output format ('correct format') but does not disclose what happens with invalid input, normalization behavior, or exact return value. The description is too thin to fully inform an agent about edge cases.

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 sentences with no wasted words. It front-loads the core purpose and then adds a practical usage hint. Each 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 one-parameter tool with no output schema, the description covers the core function and usage context. However, it lacks explicit detail on the return format (beyond 'correct format') and failure behavior, which would be useful for an agent to fully predict the tool's 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% for the single parameter, so the schema already documents its meaning. The description adds context about fuzzy matching but does not provide additional semantics beyond what the schema states. 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?

The description clearly states a specific verb and resource: 'Resolve a country name to the correct format accepted by Himalayas filters.' It distinguishes itself from other tools by referencing the specific 'Himalayas filters' context. However, it does not explicitly name or contrast with any sibling tool, so it doesn't fully earn 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?

The description includes a usage hint: 'Useful for fuzzy matching user input.' This gives some context on when to use it, but it doesn't explicitly say when not to use it or mention alternatives. For a helper tool with no obvious siblings, this is adequate but not exceptional.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It does reveal that the tool returns details including description, requirements, salary, and application link, which is useful. However, it does not mention error behavior, authentication needs, or whether the operation is strictly read-only beyond the verb 'Get'.

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 deliver the core purpose and the parameter sourcing guidance without any redundant filler. The main action is front-loaded.

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 two-parameter getter with no output schema, the description covers the main behavior, the output fields, and how to obtain the required slugs. It is missing only minor details like not-found handling, which are not critical 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 coverage is 100%, so the baseline is 3. The description adds value by telling the agent where to source the slugs ('from job listings'), which is contextual guidance beyond the schema's basic field 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 clearly states the tool retrieves full details for a specific job and lists key content fields (description, requirements, salary, application link). However, it does not explicitly distinguish itself from similar siblings like show_company_job, 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?

The instruction to use company_slug and job_slug from job listings implies when the tool is appropriate, but there is no explicit guidance on when to prefer this tool over alternatives like get_jobs or list_company_jobs. The usage context is present but not fully developed.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a read operation but does not explicitly state that it is read-only, nor does it describe pagination behavior, response format, or any side effects. The description adds minimal value beyond what the schema already states about parameters.

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

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

The description is a single, concise sentence that front-loads the primary purpose and mentions filtering options. There is no redundancy or waste. It is optimal in length and structure.

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

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

For a simple tool with three optional parameters and no output schema, the description is adequate to convey the core action. However, it lacks any mention of the response format (though not required without an output schema) and does not provide routing guidance to alternatives, which could be considered a gap for a tool in a large ecosystem. Overall, it covers the basics but leaves room for improvement.

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 provides 100% coverage for parameter descriptions, so the baseline is 3. The description mentions 'filtering by country or worldwide availability' which loosely maps to the 'country' and 'worldwide' parameters, but it doesn't add any new semantic detail. The schema already explains that worldwide overrides country, so the description adds no additional clarity.

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 clear action ('browse') and resource ('remote job listings'), with optional filtering mentioned. It is specific enough to convey the tool's core purpose, but it doesn't explicitly differentiate from sibling tools like search_jobs, which could serve a similar browsing need. The verb 'browse' suggests a listing vs. a search, but it's not explicit.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention that search_jobs might be better for complex queries or that get_job_details is for individual jobs. The agent is left to infer usage from the schema parameters alone, which is inadequate for distinguishing among siblings.

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

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

With no annotations, the description must carry the behavioral burden. It discloses that authentication is required but does not describe the return format, read-only nature (though implied by 'get'), or error behavior. This is minimal but not misleading.

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 concise sentences with zero wasted words. The essential information (what it does and the auth requirement) is front-loaded and efficiently stated.

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

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

For a zero-parameter tool with no output schema, the description is adequate. It tells the agent exactly what the tool does. While it doesn't detail the response schema, the tool's simplicity and clear name make it complete enough for correct invocation.

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

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

The tool has zero parameters, and schema coverage is 100%. The baseline for zero parameters is 4, and the description correctly adds nothing about parameters since none exist. No further explanation is needed.

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

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

The description states a specific verb ('Get') and resource ('your Himalayas profile'), clearly distinguishing it from sibling tools like get_company_profile and get_talent_profile. The phrase 'your' unambiguously marks this as the authenticated user's own profile.

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 mentions the authentication requirement but provides no explicit guidance on when to use this tool versus alternatives like get_talent_profile or get_company_profile. The name and wording imply it's for the current user, but no direct comparison or exclusion is 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?

With no annotations provided, the description carries the full burden. It discloses the basis of similarity (skills, location, category) but does not explain the return format, ordering, pagination, or any limitations. For a read-oriented tool this is adequate but shallow; an agent might not know whether results include full job objects or summaries.

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 waste. The primary purpose is stated first, followed by a usage hint. Every word contributes, and the description is immediately scannable.

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?

This is a simple tool with two required parameters and no output schema. The description includes the recommendation context and the matching criteria, but does not mention what the response looks like (e.g., a list of job objects) or any limits. Given the sibling tools like get_jobs, an agent could infer the return shape, but the description alone is slightly incomplete for a 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?

The schema has 100% coverage; both parameters have descriptions that state they are slugs of the original job. The tool description does not add extra meaning beyond this, but the schema descriptions are already sufficient. Baseline 3 is appropriate since the description adds no redundant detail yet the parameters are self-explanatory.

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

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

The description clearly states the tool finds jobs similar to a specific job, and specifies the criteria (skills, location, category). It distinguishes from generic listing or search tools by focusing on similarity. The added context 'Use after viewing a job to suggest alternatives' further clarifies its purpose, making it unmistakable.

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 explicitly says 'Use after viewing a job to suggest alternatives,' giving a clear trigger condition for when this tool is appropriate. While it doesn't name specific alternative tools, the shown usage is sufficiently targeted to guide an agent. It could be stronger with an explicit 'instead of search_jobs' but the intent is 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not state that this is a read-only operation, nor does it describe response format, pagination, or defaults (though defaults are present in the schema). The description only conveys the tool's purpose, not any behavioral traits beyond the obvious.

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

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

The description consists of two short sentences. The first states the action and scope directly, and the second adds a brief value statement. No unnecessary words, and the core purpose is front-loaded.

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

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

The tool has no output schema and only three optional parameters, but the description hints at the output being counts per breakdown type. It does not mention structure, sorting, or edge cases, but for a simple statistics endpoint this is likely sufficient for an agent to invoke it correctly. Missing details like pagination could be 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%, so the description need not repeat parameter details. It does clarify that 'type' refers to skill/category/country/industry breakdowns and 'record' to jobs vs. companies, adding semantic context that aligns with the schema descriptions. However, this is largely a rephrasing of the schema, providing minimal additional value.

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

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

The description clearly states the tool retrieves remote work statistics with specific breakdown types (skills, categories, industries, countries) aggregated by job or company count. This differentiates it from sibling tools like search_jobs or get_jobs, which deal with individual records rather than aggregate statistics.

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 offers a high-level use case ('Great for understanding the remote work landscape') but does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or specific conditions. Since no sibling tool directly overlaps, the lack of explicit differentiation is less critical, but guidance remains implicit 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?

No annotations are provided, so the description carries the burden. It states that the tool returns data (min/max/median) and does not contradict any safety assumptions, but it does not explicitly disclose that it is read-only or mention any rate limits, auth requirements, or side effects. It adds only the return-value context, which is minimal but not misleading.

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

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

A single, well-structured sentence that front-loads the core purpose, then specifies filters and return fields. No filler or redundancy; every clause 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 benchmark tool with fully documented parameters and no output schema, the description covers the essential details: input filters, return values, and currency. It does not mention error cases or pagination, but neither is critical for a tool this straightforward, so it is complete enough.

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 parameter meanings are already documented. The description adds value by specifying that the salary is in USD and scoped to remote jobs, which is not in the schema. It also clarifies the optional nature of filters without repeating the schema text, enhancing beyond 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?

States a specific verb ('Get'), resource ('salary benchmarks'), and scoping ('remote jobs by job title'), with clear output fields (min, max, median in USD). It is distinct from siblings like get_jobs or get_related_jobs, which focus on job listings rather than benchmarks.

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

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

The description implies when to use it (to retrieve salary benchmarks) but offers no explicit guidance on when to prefer this tool over alternatives, nor exclusions. With 39 sibling tools, some routing context would help, but the purpose is clear enough that an agent can infer typical use cases.

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?

Without annotations, the description adds 'Requires authentication' – a useful behavioral note. However, it does not disclose other traits like return format or error behavior, which are not covered by 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?

Single sentence, no redundancy, directly states purpose.

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

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

For a zero-parameter tool, the description covers what it returns (all jobs in tracker) and auth requirement. However, it could benefit from mentioning that results are limited to the authenticated user's tracker, but that's implied. The description is adequate for the simplicity.

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?

No parameters exist, so the schema already captures everything; description doesn't need to add parameter details. Baseline 4 for zero-param tools.

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 all jobs in your application tracker' – clearly distinguishes from siblings like get_jobs (general) and save_job/remove_saved_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?

No guidance on when to use this tool versus alternatives; does not mention any exclusions or comparisons with siblings.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It communicates that this is a read operation that returns a set of fields (bio, experiences, education, tech stack, social links), which adds useful context about the response content. However, it does not mention failure behavior when the slug doesn't resolve, authentication requirements, or the shape of the returned object, leaving some gaps for a no-annotation tool.

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 waste. The purpose is front-loaded first, and the usage guidance second. Every clause adds value — no filler, no repetition of the schema, and no redundant information. This is an ideal length for a single-record fetch tool.

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

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

For a one-parameter fetch tool with no output schema and no annotations, this description is nearly complete. It covers what the tool returns, and the schema documents the parameter format. The only missing element is behavior on invalid or non-existent slugs, which is a minor edge case for a simple GET-style tool. This is adequate for an agent to call it correctly.

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

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

Schema description coverage is 100%, so the schema already documents the talent_slug parameter including accepted formats ('john-doe', '@john-doe', or a himalayas.app URL). The description adds the provenance tip that the slug comes from search_talent results, which is a minor but useful addition. Since the schema does the heavy lifting, a 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?

The description states a clear verb, 'Get', and resource, 'full details for a candidate', and enumerates specific content: bio, experiences, education, tech stack, social links. The 'and more' is slightly vague, but the structure clearly distinguishes this from the sibling search_talent, which finds candidates rather than fetching a single profile. The purpose is unambiguous for an agent.

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

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

The description gives explicit context on how to invoke the tool: 'Use the talent_slug from search_talent results.' This tells the agent where to obtain the required parameter, which is the primary usage question for a get-by-slug tool. It doesn't state exclusions or alternatives, but for a single-record fetch there are no natural sibling alternatives, so this level of guidance is appropriate.

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

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

No annotations exist, so the description must carry the full burden. It discloses the output fields and the authentication need, which is helpful. However, it does not mention pagination defaults, sorting order, whether the list includes all job statuses or only active ones, or any side effects (though it's clearly a read operation). The lack of any caveat about list size or filtering leaves some ambiguity.

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 that front-load the main purpose and then list the output and a key prerequisite. No filler or redundancy; 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 listing tool with one optional parameter and no output schema, the description covers the essential behavior: what it lists, what fields are returned, and the auth requirement. Pagination is implied via the schema parameter, and the output format isn't needed since no output schema exists. It's complete enough for an agent to confidently invoke 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?

The schema covers the single parameter (page) with 100% description coverage, so the schema already explains it. The tool description adds no additional parameter details or usage hints, matching the baseline for high schema 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?

The description clearly states the verb+resource: 'List your company's job postings on Himalayas' and specifies the output fields ('status, views, clicks, expiry'). It differentiates from siblings like get_jobs or search_jobs by scoping to the employer's own company postings, though it doesn't explicitly name 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?

The description gives a contextual clue ('your company's job postings') and an authentication requirement ('Requires employer authentication'), but it doesn't explicitly say when to use this vs. other job-listing tools (e.g., get_jobs, search_jobs). No when-not-to-use guidance is provided.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does state a prerequisite ('Requires employer authentication') and what the output includes (preview and status), which is useful. However, it does not explicitly confirm this is a read-only operation, nor does it mention potential side effects, pagination, or return structure. It provides some transparency but leaves notable gaps.

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, consisting of two sentences that lead with the primary action ('List all your messaging conversations') and follow with relevant details (preview, status, authentication). Every sentence contributes to agent selection and execution. There is no redundancy or extraneous information, striking an ideal balance between completeness and brevity.

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

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

For a parameterless list operation, the description covers the essential aspects: what is listed, what is shown for each conversation, and the authentication requirement. It lacks explicit details about return format, sorting, or pagination, but since there is no output schema and the tool is simple, this is a minor shortcoming. The description is adequate for an agent to understand and invoke the tool correctly, though a note about potential volume or ordering would have made it fully 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?

The tool has zero parameters, so the schema is trivial and provides no information to clarify. The baseline for 0 parameters is 4, and the description appropriately focuses on the tool's behavior rather than parameter details. It adds contextual value by explaining what the list contains, which indirectly informs parameter usage. No further parameter documentation is needed.

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

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

The description clearly states the verb 'List' and the resource 'conversations', and adds specifics: 'with last message preview' and 'Shows conversation status'. This distinguishes it from sibling tools like get_conversation, which retrieves a single conversation. The purpose is unambiguous and immediately actionable.

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 explains what the tool does but does not provide explicit guidance on when to choose it over alternatives. It does not mention, for example, that get_conversation should be used for a single conversation or that start_conversation is for creating new ones. An agent must infer usage from the action alone, which is insufficient for tools with many messaging-related siblings.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states an authentication requirement, but does not mention side effects, idempotency, reversibility, or return behavior. For a mutation action, this is insufficient.

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

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

The description is a single, efficient sentence that immediately states the core action and the key constraint. All words earn their place, with no redundancy. Front-loaded purpose makes it easy to scan.

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 tool with three parameters and no output schema, the description is minimal. It does not explain how to obtain the message ID or when to use room_name versus talent_slug, though the schema references the source tools. The description is adequate but leaves the param interplay to the schema, which is acceptable but not rich.

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 all three parameters, so the baseline is 3. The description's mention of 'by its ID' aligns with message_id but adds no extra meaning beyond the schema. It does not clarify the relationship or usage trade-offs between room_name and talent_slug.

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 verb 'mark' and the resource 'a message as read,' with the required identifier. It does not explicitly differentiate from sibling tools like send_message or get_conversation, but the purpose is unambiguous. Lacks explicit sibling differentiation, so not 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?

The description provides no guidance on when to use this tool versus alternatives such as send_message or start_conversation. It mentions a prerequisite (employer authentication) but no context for selection. No usage scenarios or exclusions are given.

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

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

With no annotations, the description carries the full burden. It discloses key behaviors: no authentication needed, payment required, and a Stripe checkout URL will be returned. However, it omits what happens to the job if payment is not completed (e.g., whether it remains unpublished) and does not describe the overall mutation effect beyond the payment flow. This partial transparency is useful but incomplete.

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 sentences with zero fluff. It front-loads the critical aspects (no account, payment, no auth) and avoids restating schema details. Every sentence earns its place, making it highly efficient.

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?

Despite 14 parameters, 8 of which are required, and no output schema, the description provides minimal context. It mentions a single return value (Stripe URL) but does not explain the payment flow, job visibility after payment, or any other side effects. For such a complex tool, this is insufficient for an agent to fully understand consequences.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description adds generic guidance ('Provide your email and company details') but no parameter-specific clarification beyond what the schema provides. This meets the baseline but does not exceed 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?

The description states a specific verb ('Post'), a clear resource ('a job'), and a distinguishing context ('on Himalayas without an account'). It differentiates itself from sibling tools like create_company_job by explicitly noting no account is needed, making the purpose 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?

The description clearly indicates when to use this tool: when posting a job without an account, and it notes that payment is required and authentication is not. It gives clear usage context but does not explicitly mention alternatives or when not to use it. Since the purpose is unique among siblings, this is sufficient.

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

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

With no annotations, the description carries the full burden. It states the action (purchase), the return (checkout URL), and a prerequisite (employer auth). It does not disclose whether the job posting is immediately modified or if payment is processed separately, but the 'checkout URL' implies a deferred flow. This is a reasonable level of transparency for a purchase tool.

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

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

A single, concise sentence packs the purpose, specific options, prices, return value, and auth requirement. No wasted words; all essential information is front-loaded and efficiently structured.

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

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

The description covers the core aspects: what it does, the return type, auth requirement, and specifics. However, it omits details like idempotency, error handling, or whether the checkout URL is temporary. For a purchase operation, this is slightly incomplete but still adequate for an agent to call it correctly.

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

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

Schema coverage is 100% with both parameters already described, including prices for extras. The description repeats the prices and purpose but adds no new semantic detail beyond the schema. This meets the baseline of 3 for high schema coverage; it does not substantially enhance parameter understanding.

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?

Description clearly states the verb 'Purchase', the resource 'paid extras for an existing job posting', and the specific extras (sticky $299, newsletter $99). It also mentions the return value (Stripe checkout URL), distinguishing it from job creation or status tools. This is unambiguous and specific.

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

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

The description implies when to use it — when you need to add paid extras to an existing job. It also notes the authentication requirement. However, it does not explicitly mention alternatives or when not to use it (e.g., if the job is not yet posted). This leaves some inference, but the context is clear enough.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It does state 'Requires employer authentication,' which is useful, but it does not disclose whether the removal is permanent, reversible, or the impact on existing data. The verb 'Remove' implies deletion, but further details on side effects or response behavior are absent.

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

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

The description is a single, compact sentence that conveys the essential purpose and a key prerequisite immediately. No filler words or redundant information, making it efficient and well-structured.

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 delete operation with one parameter and no output schema, the description provides enough context: what is removed, where, and the authentication requirement. It does not specify return values, but that is often implicit for such operations, and the absence of an output schema reduces the need for detailed 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 description coverage is 100% for the single parameter 'id' (Perk ID to remove), including a pointer to get_company_perks. The description adds no additional parameter meaning beyond what the schema already 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?

The description clearly states the action (Remove) and the resource (perk/benefit from your company on Himalayas). It is specific and distinct from siblings like add_company_perk and get_company_perks, making the tool's purpose 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?

The description does not explicitly indicate when to use this tool versus alternatives. It mentions the prerequisite of employer authentication, which is a precondition, but gives no guidance on when removal is appropriate or when to prefer other tools. The schema's reference to get_company_perks helps with finding IDs but not with usage context.

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

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

No annotations are provided, so the description carries the full burden. It discloses that authentication is required, which is useful, and implies mutation by 'remove'. However, it does not mention potential irreversibility, side effects, or whether the action only affects the user's saved list. The disclosure is minimal but not contradictory.

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

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

The description is a single, focused sentence that front-loads the action and includes the key requirement (authentication). There is no extraneous information; every word earns its place.

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

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

For a simple one-parameter mutating tool with no output schema, the description adequately explains what it does and the authentication requirement. It does not explain error conditions or return values, but these are not mandated in the absence of an output schema. The context is sufficient for an agent to invoke the tool 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?

The input schema already documents the sole parameter 'id' with a clear description ('Kanban item ID to remove'), and coverage is 100%. The tool description does not add any additional meaning about parameters, so it relies on the schema per 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 clearly states the action (remove), the resource (a job), and the context (application tracker). This distinguishes it from sibling tools like save_job and get_saved_jobs, making its purpose 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?

The description implies when to use it (when removing a saved job) but does not explicitly mention alternatives or exclusions, such as the distinction from delete_company_job. It provides no proactive guidance on choosing this tool over others.

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

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

No annotations are present, so the description carries the full burden. It only mentions 'Requires authentication' but does not disclose what happens on save (e.g., whether duplicates are allowed, if it validates required fields, or what the response contains). Since the tool mutates state, this lack of behavioral detail is a significant 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?

The description is two short sentences with no wasted words. It front-loads the core purpose and includes the auth requirement efficiently. However, it is so minimal that it sacrifices useful context, though that is more a completeness issue than a conciseness one.

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 10 parameters, no output schema, and no annotations, the description is far too thin. It does not explain how parameters like status, excitement, or salary fields should be used, nor what the tool returns. An agent needs more guidance to call this correctly, especially given the variety of optional fields.

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 10 parameters already have descriptions in the schema. The tool description adds no additional parameter context, so it does not exceed the baseline. It neither helps nor hurts 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 clearly states the verb 'Save' and the resource 'a job' plus the destination 'your application tracker', making the purpose unambiguous. It distinguishes from siblings like create_company_job (which creates a company-level job) and get_saved_jobs (which retrieves saved jobs).

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

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

No guidance is provided on when to use this tool versus alternatives. It does not mention that it is for personal tracking, nor does it contrast with update_job_status or remove_saved_job. The agent must infer usage from the name alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that the tool 'searches' for companies, implying a read-only operation, but doesn't disclose effects like pagination, result ordering, or that the 'worldwide' flag can override the country filter (which is only noted in the schema). The description offers no additional behavioral context beyond the obvious.

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

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

The description is a single, clear sentence with no filler or repetition. It immediately conveys the core action and key scoping criteria, and every word earns its place. It is optimally concise.

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 complexity of 7 parameters, no output schema, and no annotations, this description is under-specified. It lacks guidance on pagination, defaults (e.g., sort default), the interaction between filters, and how it differs from 'get_companies'. An agent would need to open the schema and infer behavior, which is a significant gap for a search 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%, so all parameters already have descriptions in the schema. The description adds only a high-level mention of 'keywords' and 'optional country filtering', which doesn't enhance understanding of the other five parameters (benefits, tech_stack, page, sort, worldwide). Since the schema already documents each parameter, a baseline of 3 is warranted.

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 ('Search') and resource ('remote-friendly companies'), which clearly indicates the tool's purpose. However, it doesn't explicitly differentiate from the sibling 'get_companies', nor does it mention that it's a filtered search with multiple criteria. The mention of 'keywords' and 'optional country filtering' adds specificity, but the lack of contrast with other search tools keeps it from 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?

No guidance is provided on when to use this tool versus alternatives like 'get_companies', 'search_jobs', or 'search_talent'. There is no mention of preferred contexts, exclusions, or how this search complements others. The description leaves the decision of tool selection entirely to inference.

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

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. The only behavioral hint is the word 'search', implying a read operation, but there is no explicit statement about side effects, rate limits, result format, or safety. For a tool with 15 parameters, the description offers almost no transparency beyond the basic action.

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

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

The description is a single concise sentence, with no redundant words or filler. It front-loads the core action ('search for remote jobs') and then mentions the key optional filters. It is appropriately sized, though one might argue it under-delivers given the tool's complexity, but the structure itself is efficient.

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

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

Given the tool's complexity (15 parameters, no output schema, no annotations), the description is substantially incomplete. It only references keyword, country, and worldwide filters, ignoring pagination, sorting, employment type, markets, benefits, currency, companies, experience, salary filtering, and exclude_worldwide. An agent relying on this description alone would miss the majority of available functionality, making the tool appear far more limited than it is.

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 parameters have descriptions in the schema. The tool description adds no semantics beyond mentioning keyword, country, and worldwide, which are already described in the schema. It does not highlight that all parameters are optional, nor does it clarify interactions like 'worldwide overrides country' (though the schema does). Thus, the description provides minimal added value over the schema, warranting the baseline score of 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?

The description clearly states a specific verb+resource: search for remote jobs, using keywords with optional country/worldwide filters. It is understandable but does not differentiate from sibling tools like get_jobs or get_related_jobs, which may also return job listings. The mention of 'remote' and 'worldwide' gives some context, but without distinguishing it from other job-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?

No guidance is provided on when to use this tool versus alternatives such as get_jobs, get_related_jobs, or search_talent. There are no explicit conditions, exclusions, or comparisons to siblings. The description only states what it does, not when to choose it over other job-searching tools, leaving the agent to infer usage from the schema alone.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states 'Search' and 'Browse' but does not disclose what the response looks like, whether pagination is supported (though page param exists), or any limits. There is no mention of sorting or any side effects (though it is read-only by nature). For a search tool with no output schema, this is a significant 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?

The description is only two sentences but includes the inaccurate mention of 'role' as a filter. The core info is concise and front-loaded, but the inaccuracy reduces its effectiveness. It could be more precise without the misleading 'role' term.

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 there is no output schema and no annotations, the description must explain the return format and usage context adequately. It does not describe what the search results contain (e.g., candidate profiles, pagination behavior, how sorting works). An agent would not know what to expect from the response or how to interpret the results. This is incomplete for a search 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% for all four parameters (page, sort, country, keyword), so the baseline is 3. However, the description introduces 'role' as a filter criterion, which is not a parameter in the schema. This is misleading and does not add value beyond the schema. The description adds little beyond what the schema already provides, and the 'role' mention creates confusion.

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

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

The description clearly states the tool searches for remote talent/candidates on Himalayas, using the verb 'search' and a specific resource. It mentions browsing by keyword, country, or role, differentiating from sibling job/company search tools. However, it mentions 'role' as a search criterion, but the schema only supports keyword, country, sort, and page, which could confuse an agent.

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

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

There is no explicit guidance on when to use this tool versus alternatives like search_jobs, search_companies, or get_talent_profile. The description implies it is for finding candidates, but it does not state exclusions or mention that get_talent_profile should be used for details on a specific candidate. An agent would have to infer usage from the name and context rather than from the description.

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

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

With no annotations provided, the description is the primary source of behavioral disclosure. It states that authentication is required, which is useful context for a mutation tool. However, it does not mention side effects beyond 'send', potential errors (e.g., invalid room_name), or whether the operation is reversible. For a mutation, this partially covers the burden but lacks depth.

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 sentences with no fluff. The first sentence states the core purpose clearly, and the second provides usage guidance and authentication. It is front-loaded with the primary action and efficient in conveying all necessary 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?

The description covers the main usage and prerequisites, but leaves ambiguity about whether at least one of room_name or talent_slug is required (the schema marks both optional, yet sending a message without a conversation seems impossible). It does not address what happens if both are provided or if neither is provided, nor does it describe error handling or return value. Given the schema has no output schema and the tool is relatively simple, this is a moderate gap.

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

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

The schema already describes all parameters (100% coverage), but the description adds meaning by explaining the provenance: room_name comes from list_conversations and talent_slug from search_talent, and that talent_slug 'resolves to room name automatically'. This clarifies the functional relationship between parameters and provides context the schema does not.

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 verb 'send' and the resource 'a message in an existing conversation', which is precise and distinct from starting a conversation. It also distinguishes itself from start_conversation by explicitly stating it targets an existing conversation.

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

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

The description provides explicit guidance on when to use this tool: it requires an existing conversation, and directs to start_conversation if none exists. It also tells where to obtain room_name (from list_conversations) and talent_slug (from search_talent), which are clear prerequisites. The authentication requirement is also 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?

No annotations are provided, so the description carries the full burden. It discloses a key behavioral requirement ('Requires employer authentication') and implicitly indicates a read-only operation via 'Get'. It does not mention return format, error behavior, or rate limits, but for a simple getter this is a reasonable minimum. It does not contradict any 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?

A single, clearly worded sentence that states the action and the key requirement up front. No filler or redundant phrasing; every word contributes meaning.

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

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

For a one-parameter getter with no output schema, the description is nearly complete. It mentions the auth requirement and the scope ('your company'). The only minor gap is that it doesn't specify what 'full details' includes or behavior on invalid slugs, but given the simplicity, this is adequate for an agent to use it correctly.

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

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

Schema description coverage is 100%, and the parameter 'job_slug' already has a helpful description ('Job slug (from list_company_jobs)') in the schema. The tool description adds no additional parameter information beyond what the schema states, 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 ('Get'), resource ('full details of one of your company's job postings on Himalayas'), and a clear scope ('your company's') that distinguishes it from general job lookup tools like get_job_details or get_jobs. The purpose is unambiguous and not a tautology.

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: it's for your own company's job postings, which is a clear context. However, it does not explicitly contrast with alternatives like get_job_details or mention when not to use it (e.g., for public job details). No exclusions or alternative references are given beyond the scope wording.

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

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

With no annotations, the description discloses key behavior: it returns an existing conversation if present, and implies message sending via the optional parameter. It does not detail errors, rate limits, or side effects, but covers the most critical traits.

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

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

Two tight sentences with no fluff, front-loaded purpose, and clear prerequisites. Every word earns its place.

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

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

For a 2-parameter tool with no output schema, the description covers the main use case, prerequisites, and the existing-conversation behavior. It doesn't explain the return object shape, but that is a minor gap for this simple 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%, so the schema already documents both parameters. The description adds minimal value beyond the schema, only restating the talent slug and message purpose.

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?

Clear verb+resource: 'Start a conversation with a candidate by talent slug.' It explicitly mentions the optional initial message and is distinct from siblings like send_message and get_conversation.

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 instructs to use search_talent first and then use the slug. It also states the authentication requirement. This provides clear when-to-use context.

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

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

With no annotations, the description must carry behavioral disclosure. It transparently states the partial-update behavior (only provided fields change), the destructive replacement behavior for screening_questions, and the authentication requirement. However, it omits other behaviors like whether the operation is idempotent, how missing fields are treated (implicitly unchanged), or error/response semantics. It covers the most critical nuances but not exhaustively.

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 concise (two sentences) and front-loaded with the core purpose. It packs a lot of essential information efficiently: the operation, partial-update semantics, the screening_questions replacement behavior, and authentication. Every sentence earns its place without redundancy, though the phrasing is dense.

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 14 parameters, a nested screening_questions object, no output schema, and no annotations, the description covers the main usage context and the one special destructive behavior. It does not mention error scenarios, success indicators, or other edge cases, but it addresses the key facts an agent needs to call the tool correctly. It is adequate but not comprehensive for the tool's complexity.

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 description coverage is 100%, so each parameter is already documented. The description adds global parameter semantics—'Only provide fields you want to change'—which clarifies that omitted fields remain unchanged, a valuable instruction beyond the per-field schema descriptions. This exceeds the baseline of 3 for high 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?

The description states a specific verb ('Update'), a resource ('existing job posting'), and the platform ('Himalayas'), making the tool's purpose clear. It doesn't explicitly contrast with siblings like create_company_job or delete_company_job, but 'existing' implies a modification operation, which is enough for basic differentiation.

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

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

The description provides usage guidance about how to use the tool ('Only provide fields you want to change' and the screening_questions replacement rule), but it does not specify when to choose this tool over alternatives (e.g., create_company_job for new jobs, delete_company_job for removal). No explicit when/when-not or alternative routing is given, leaving the agent 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?

No annotations are provided, so the description carries the full burden. It discloses the authentication requirement, which is useful, but does not state whether the update is partial (only provided fields) or a full overwrite, nor does it mention side effects, validation rules, or response behavior. For a mutation tool affecting multiple company fields, this is a significant 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?

The description is a single, tightly written sentence that conveys the essential purpose and a key constraint (authentication). There is zero waste. The most critical information is front-loaded, making it easy for an agent to parse quickly.

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?

This is a mutation tool with 10 parameters, all optional, no output schema, and no annotations. The description is too minimal to guide an agent on calling it correctly: it doesn't explain whether unspecified fields remain unchanged, how partial updates work, or what happens on validation failure. An agent would need to make assumptions about update semantics, which is risky.

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 every parameter already has a description. The tool description adds no additional parameter-level information beyond what the schema provides. Per the calibration, the baseline is 3 when schema covers all parameters, and the description doesn't enhance it further.

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 ('Update'), the resource ('your company's profile'), and the platform ('Himalayas'). It effectively distinguishes from sibling tools like update_company_job and update_company_tech_stack, which target different aspects of the company. The phrase 'Requires employer authentication' adds crucial context about who can perform this action.

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

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

The description gives no explicit when-to-use or when-not-to-use guidance, nor does it name alternative tools. However, the resource and context are sufficiently clear that an agent can infer this is for updating company profile fields (as opposed to job postings or tech stack). The lack of explicit routing reduces score from 4 to 3.

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

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It mentions authentication and matching to the database, but does not disclose whether the update replaces the entire stack or appends to it, whether it is idempotent, or how errors are handled. For a write operation, these are significant missing details, so it falls short of comprehensive transparency.

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

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

The description is two sentences, with the primary action front-loaded and the additional behavior (matching to database) placed second. There is no redundant information, and the wording is efficient. It earns a top score for conciseness and structure.

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

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

For a simple tool with a single parameter and no output schema, the description covers essential aspects: the action, the input format, the matching behavior, and the authentication prerequisite. It is sufficient for an agent to call it correctly, though it does not specify the return value or error scenarios. Since this is straightforward, a 4 reflects near-completeness.

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

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

The input schema already documents the 'stacks' parameter with 100% coverage. The description adds value by explaining that technology names will be matched to the Himalayas stack database, which is not mention in the schema. This gives the agent insight into how the parameter is processed, making it more than a mere restatement.

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 verb ('Update'), the resource ('your company's tech stack'), and the platform ('on Himalayas'). It also differentiates from siblings like 'update_tech_stack' (likely personal) and 'update_company_profile' (general) by being specific to the company's tech stack. The mention of matching to the Himalayas stack database further clarifies its function.

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

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

The description provides clear context for when to use it: when updating a company's tech stack on Himalayas. It instructs users to pass technology names, implying the input format. However, it does not explicitly state when not to use it or name alternative tools (e.g., 'update_tech_stack' for personal profiles). The context is clear but excludes no alternatives, so a 4 is appropriate.

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

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

With no annotations, the description must disclose behavioral traits, but it only reveals that authentication is needed. It does not state whether the operation is reversible, what happens to unspecified fields, or what the response contains. For a mutation tool, this is a significant 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 short sentences with no waste. The purpose is front-loaded and the authentication requirement is noted. It is concise without sacrificing clarity.

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

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

The description is functional but minimal. Given the absence of an output schema and annotations, an agent lacks information about return values, error handling, or whether partial updates are supported. For a tool with four parameters, this is adequate but not thorough.

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 each parameter's meaning. The description adds no extra context about parameters, so a baseline score of 3 is appropriate given the high 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?

The description clearly states the verb 'Update' and the resource 'a saved job', and mentions 'status or details' which gives a sense of scope. It is distinct from siblings like save_job and remove_saved_job, though it does not explicitly name 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?

The only usage guidance is the authentication requirement. There is no information about when to use this tool versus other update/save tools, or any conditional logic for selecting it. The description does not help an agent decide between this and update_company_job or save_job.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the authentication requirement, which is useful, but it does not explain whether the update is partial or full, how omitted fields are handled, any side effects, or what the response contains. This is insufficient for a mutation tool.

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 extremely concise—two sentences with no fluff. Purpose is front-loaded, and the authentication note is a single clause. Every word earns its place.

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

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

Given eight optional parameters, no output schema, and no annotations, the description is not contextually complete. It omits crucial operational context: whether updates are partial, validation requirements (especially for location), and how this relates to sibling tools like get_my_profile or get_correct_country_name. An agent would need to infer too much.

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 each parameter. The description adds no parameter-specific information beyond what the schema provides, and notably omits guidance on the location constraint (e.g., pointing to get_correct_country_name). Baseline of 3 is appropriate.

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

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

The description clearly states the verb 'Update' and the resource 'your Himalayas profile', which distinguishes it from company profile updates. It is concise and unambiguous, though it does not explicitly list the fields, relying on the schema for that.

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 only guidance is 'Requires authentication', which is a prerequisite rather than a usage condition. There is no mention of when to use this tool versus alternatives (e.g., get_my_profile for reading, update_company_profile for companies), nor any routing to helper tools like get_correct_country_name for the location field.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the authentication requirement and that it updates the tech stack, but does not disclose whether the operation replaces the entire stack, appends to it, or is destructive. There is no information about idempotency, failure behavior, or side effects. For a mutation tool, this is a significant 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?

The description is a single, well-structured sentence that conveys the purpose and authentication requirement without any filler. Every word contributes value, and the key information is front-loaded. It is appropriately concise for a simple one-parameter 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 the tool's low complexity (one parameter, no output schema, no nested objects), the description covers the core purpose and a prerequisite. However, it omits important behavioral context such as whether the update replaces the entire stack. For a complete picture, the user would need to know the effect on existing data, which 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 input schema covers the only parameter ('stacks') with a clear description and example. Since schema description coverage is 100%, the baseline is 3. The tool description adds no additional parameter-level detail beyond what the schema provides, so it does not exceed the 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 states a specific verb ('Update') and resource ('your tech stack on your Himalayas profile'), which clearly identifies the tool's purpose. It does not explicitly differentiate from sibling tools like update_company_tech_stack or update_profile, but the phrase 'your Himalayas profile' implies personal scope. This is clear enough for basic selection, but could be more explicit about the distinction.

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 mentions the prerequisite of authentication, which is helpful. However, it does not provide guidance on when to use this tool versus alternatives such as update_company_tech_stack or update_profile. No exclusions or alternative recommendations are given, so the user is left to infer the intended use case from the resource name.

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