Pre-vetted senior LATAM engineers for US startups: hiring leads, open roles, applications.
Request a custom software project from Ruzora's Software Factory. Fixed-price development by senior LATAM engineers.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Contact person name | |
| Yes | Business email | ||
| budget | No | Budget range (USD) | |
| company | Yes | Company name | |
| timeline | No | Desired timeline | |
| description | Yes | What you want built |
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 correctly indicates this is a request/submission operation ('Request a custom software project'), implying data submission but not querying or mutation. However, it doesn't disclose whether the submission triggers an email, creates a CRM record, or requires confirmation, leaving behavior somewhat opaque.
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, efficiently stating the purpose and unique value proposition. It is front-loaded with the core verb ('Request'). No extraneous information is present. It could be slightly more concise by merging the sentences, but overall it's 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?
Given the 6-parameter input schema with no output schema, the description adequately covers the tool's purpose. The parameter names and schema descriptions are self-explanatory (name, email, description). However, it omits any mention of response expectations (e.g., what happens after submission) and doesn't address timing or follow-up process, which would be helpful for 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 has 100% description coverage, so all 6 parameters are already documented. The tool description adds no parameter-specific details beyond the schema, but this is acceptable since the schema is already comprehensive. The baseline for high coverage is 3, and the description's overall purpose context earns a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates an inquiry for a custom software project, specifying it's for Ruzora's Software Factory with fixed-price development by senior LATAM engineers. This distinguishes it from siblings like 'request_demo' or 'submit_candidate_application', which have different purposes.
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 this tool should be used when a user wants to request custom software development, which is distinct from demo requests or hiring inquiries. However, it doesn't explicitly state when not to use it or provide alternatives, such as 'request_demo' for standard product inquiries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit a hiring inquiry to Ruzora. Use when a company wants to hire senior LATAM engineers for their team.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Full name of the hiring manager | |
| Yes | Business email address | ||
| phone | No | Phone number | |
| company_name | Yes | Company name | |
| company_domain | No | Company website domain |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It identifies the tool as a form submission for hiring inquiries but does not disclose any behavioral traits such as data handling, confirmation process, rate limits, or whether it triggers follow-up actions. This is insufficient for a tool that collects personal information.
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 brief and front-loaded with the main action. Every sentence has a purpose, and there is no extraneous information. It could be slightly improved by adding specific guidance, but it is appropriately concise for a simple 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 has 5 parameters, 3 required, no output schema, and no annotations, the description is too minimal. It does not explain what happens after submission, how data is used, or any constraints (e.g., format expectations for 'phone'). Completion is especially lacking for behavioral and post-submission context.
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 baseline is 3. The description does not add meaning beyond the schema; it simply restates the purpose. No examples or context for parameters like 'phone' or 'company_domain' are provided, but the schema already explains these clearly.
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 ('Submit a hiring inquiry') and the target ('Ruzora') and specifies the use case ('hire senior LATAM engineers'). It distinguishes from sibling tools by focusing on hiring as an employer, not applying as a candidate or requesting a demo.
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 a clear when-to-use condition ('when a company wants to hire senior LATAM engineers'), but does not explicitly state when not to use it or mention alternative tools for related tasks such as 'submit_candidate_application' or 'request_demo', which are among the siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get comprehensive information about Ruzora's services, process, value proposition, and differentiators.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavior. It indicates the tool returns comprehensive information (not destructive), but doesn't mention any limitations like rate limits, auth requirements, or if results are cached. No contradiction with annotations since none exist.
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 front-loads the action ('Get comprehensive information') and specifies the scope. Every word adds value with no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and no output schema, the description sufficiently explains its purpose. It covers what information is retrieved, which is adequate for a simple info tool. Could mention that it returns static data or if it covers all updates, but for zero-param tools this is 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 schema coverage is trivially 100%. The description adds value by clarifying what data the tool returns (services, process, value proposition, differentiators), which is not available elsewhere. No parameter explanations 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 that this tool retrieves comprehensive information about Ruzora's services, process, value proposition, and differentiators. It uses specific nouns like 'services' and 'process' which distinguish it from sibling tools like 'list_open_roles' or 'get_job_details'.
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 name 'get_company_info' implies it's the right tool when an agent needs general company details, but there's no explicit guidance on when not to use it or how it compares to siblings. For instance, it doesn't say 'use this for high-level overview, not for specific job roles'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a specific job role including responsibilities, requirements, and tech stack.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Job slug (e.g., "devops-engineer", "ai-engineer") |
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 describes the content returned (responsibilities, requirements, tech stack) but does not disclose behavioral traits such as being read-only, error handling for invalid slugs, or authentication requirements. The name implies a safe read operation, but the description is minimal on behavioral context.
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 of 14 words, front-loading the purpose and listing the content categories. Every word contributes meaning without redundancy. It is optimally concise for a simple read tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, no output schema, and no nested objects, the description adequately explains what the tool returns (responsibilities, requirements, tech stack). It does not describe the return format, but the verb 'Get' and the list of content categories provide sufficient completeness for an agent to understand the tool's output. Sibling tools like 'list_open_roles' and 'get_company_info' confirm the context is appropriate.
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 a clear description for the 'slug' parameter. The tool description adds value by listing the types of information returned (responsibilities, requirements, tech stack), which clarifies the purpose of the parameter. However, this is marginal beyond the schema, so a baseline score 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 'Get' and the resource 'detailed information about a specific job role', and lists the content categories (responsibilities, requirements, tech stack). It effectively distinguishes from sibling tools like 'list_open_roles' (which provides a list) and 'create_factory_inquiry' (which creates a new record), making the tool's unique 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 usage when an agent needs detailed information about a single job role, providing clear context. However, it does not explicitly state when not to use it (e.g., for listing all roles) or mention alternatives like 'list_open_roles'. The guidance is clear but lacks direct comparisons or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all currently open engineering roles at Ruzora. Returns titles, tech stacks, experience levels, and application URLs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It indicates a read operation ('get') and lists return fields, but it does not state that it is read-only, whether authentication is required, or what happens if no roles exist. The description is adequate but thin for a tool with zero annotation support.
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 packs purpose and return information without any fluff. Every word earns its place, and it is front-loaded with the action and target.
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, parameterless tool with no output schema, the description sufficiently explains what is returned (titles, tech stacks, experience levels, application URLs). It could be slightly more explicit about the list format ('returns an array of roles'), but it is nearly complete for an agent to invoke 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?
With 0 parameters and 100% schema coverage, the baseline is 3. The description does not need to add parameter meaning, but it adds context about the tool's output (what fields are returned), which is helpful but not required for 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 tool retrieves 'all currently open engineering roles at Ruzora' and lists the specific returned fields (titles, tech stacks, experience levels, application URLs). This distinguishes it from sibling tools like get_job_details (which likely gets a single role) and get_company_info, so an agent can immediately understand its scope.
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 listing open engineering roles, but it does not explicitly state when to use it versus alternatives (e.g., 'use this to browse available positions before creating an inquiry') or any prerequisites. Minimal guidance beyond the verb and resource.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Request a demo call with Ruzora. A team member will reach out within 24 hours to discuss your hiring needs.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Full name | |
| Yes | Business email | ||
| phone | No | Phone number | |
| company_name | Yes | Company name | |
| company_domain | No | Company website domain |
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 indicates a human follow-up but does not clarify whether the tool creates a record, is destructive, or has side effects. The agent cannot assess safety or idempotency from this description.
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, 21 words) and front-loaded with the core purpose. Every sentence adds value, and there is no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters, no output schema, and no annotations, the description is somewhat minimal. It explains the purpose and follow-up but does not specify the tool's response (e.g., confirmation message) or address idempotency. It is adequate for a simple request tool but could be more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The tool description does not add any additional meaning beyond the schema's parameter descriptions. It mentions required fields but adds no format or usage guidance beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Request a demo call') and the resource ('Ruzora'), with a specific outcome ('team member will reach out within 24 hours'). This distinguishes it well from sibling tools like create_factory_inquiry or submit_candidate_application, which have different purposes.
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 provide any guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The agent must infer usage solely from the tool name and description, which is insufficient for a clear decision.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Apply to Ruzora's talent network. For software engineers in LATAM interested in remote positions with US startups paying USD salaries.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address | ||
| phone | No | Phone number | |
| message | No | Brief intro or relevant experience | |
| last_name | Yes | Last name | |
| first_name | Yes | First name | |
| linkedin_url | No | LinkedIn profile URL | |
| role_category | Yes | Primary role category |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully reveal behavioral traits. It states the tool's purpose (application submission) but does not disclose any side effects, such as whether a confirmation is sent, if data is persisted, or if there are rate limits or authorization requirements. The description is clear about what it does but lacks depth on consequences.
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 composed of two concise sentences. The first sentence states the core action, and the second provides crucial context about the target audience and job type, all without unnecessary words. Every sentence is meaningful.
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 (7 parameters, 4 required, 1 enum) and lack of output schema, the description adequately covers the who, what, and why. It could be enhanced by mentioning what happens after submission (e.g., confirmation email, expected response time), but overall it provides sufficient context for an agent to understand the tool's purpose and target audience.
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 does not add any additional meaning beyond what the schema's parameter descriptions already provide. It does not explain the enum values, provide examples, or clarify how parameters relate to the application process.
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 'Apply' and the resource 'Ruzora's talent network', and further specifies the target audience ('software engineers in LATAM') and context ('remote positions with US startups paying USD salaries'), which distinguishes it from sibling tools like 'create_factory_inquiry' or 'create_hiring_lead'.
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 tells when to use this tool: for applying to the talent network, and crucially, it specifies the target audience (software engineers in LATAM) and ideal job context (remote, US startups, USD salaries). This implicitly excludes candidates outside this scope, guiding the agent not to use this tool for other purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
create_factory_inquirycreate_hiring_leadget_company_infoget_job_detailslist_open_rolesrequest_demosubmit_candidate_applicationClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Recruiter-scoped search across Latin America's largest tech talent pool. OAuth-authenticated.
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Hiring developers? Find and source software engineers by what they build on GitHub.
Privacy-safe synthetic financial data for LatAm fintech, AI agents, testing and ML.
Add one secure layer between your agents and this server.
Each tool addresses a distinct purpose: factory inquiries, hiring leads, company information, job details, open roles, demos, and candidate applications. There is no ambiguity in their target actions or stakeholders (client vs. candidate).
Tool names consistently follow a verb_noun pattern using snake_case, such as create_factory_inquiry or list_open_roles. The minor inconsistency is that get_job_details and get_company_info use 'get' while others use 'create', 'list', or 'request', but the pattern is still predictable and clear.
With 7 tools, the count is well-scoped for a recruitment and software services server. Each tool covers a necessary interaction (inquiries, applications, demos, info, role listing) without unnecessary bloat or deficiency.
The tool set covers the main lifecycle: company info, role discovery, applying/inquiring, and demo requests. A minor gap is the lack of update or delete operations for inquiries or applications, but for this domain those are typically handled out-of-band, so the surface is reasonably complete.
